diff --git a/.gitattributes b/.gitattributes index 51fe98e19c0f64a38d180a36c5b306971f958bed..9964dc30bc1a49a5660ea8f57f05de67a757123c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -94,3 +94,4 @@ venv/lib/python3.10/site-packages/nvidia/cublas/lib/libcublas.so.12 filter=lfs d venv/lib/python3.10/site-packages/nvidia/curand/lib/libcurand.so.10 filter=lfs diff=lfs merge=lfs -text venv/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_ops_infer.so.8 filter=lfs diff=lfs merge=lfs -text venv/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_adv_train.so.8 filter=lfs diff=lfs merge=lfs -text +venv/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc-builtins.so.12.1 filter=lfs diff=lfs merge=lfs -text diff --git a/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/config.yaml b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..79628c942c812d0048bdae0c8a214a683339eb01 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/config.yaml @@ -0,0 +1,43 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + python_version: 3.10.12 + cli_version: 0.17.0 + framework: huggingface + huggingface_version: 4.41.0 + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1716404423 + t: + 1: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 2: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 3: + - 23 + 4: 3.10.12 + 5: 0.17.0 + 6: 4.41.0 + 8: + - 5 + 13: linux-x86_64 diff --git a/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/output.log b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..8e129768047c381a5ddab2956dc7c8873baf4e8a --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/output.log @@ -0,0 +1,34 @@ + +2024-05-22:19:00:24,724 INFO [__main__.py:251] Verbosity set to INFO +2024-05-22:19:00:33,199 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande'] +2024-05-22:19:00:33,200 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 +2024-05-22:19:00:33,200 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step6000'} +2024-05-22:19:00:35,577 INFO [huggingface.py:164] Using device 'cuda' +Traceback (most recent call last): + File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/usr/lib/python3.10/runpy.py", line 86, in _run_code + exec(code, run_globals) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in + cli_evaluate() + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate + results = evaluator.simple_evaluate( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper + return fn(*args, **kwargs) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate + lm = lm_eval.api.registry.get_model(model).create_from_arg_string( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string + return cls(**args, **args2) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__ + self._get_config( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config + self._config = transformers.AutoConfig.from_pretrained( + File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained + config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict + config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict + resolved_config_file = cached_file( + File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file + raise EnvironmentError( +OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step6000 does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step6000/tree/main' for available files. \ No newline at end of file diff --git a/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/requirements.txt b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3573b3bbfd5b190fed4ccaed4ac2846002aec22 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/requirements.txt @@ -0,0 +1,155 @@ +DataProperty==1.0.1 +GitPython==3.1.43 +Jinja2==3.1.4 +Markdown==3.6 +MarkupSafe==2.1.5 +Pillow-SIMD==7.0.0.post3 +PyYAML==6.0 +Werkzeug==3.0.3 +absl-py==2.1.0 +accelerate==0.30.1 +aiohttp==3.9.5 +aiosignal==1.3.1 +async-timeout==4.0.3 +attrs==23.2.0 +av==9.2.0 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.15.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +cmake==3.29.2 +colorama==0.4.6 +datasets==2.19.1 +deepspeed==0.12.4+hpu.synapse.v1.15.1 +dill==0.3.8 +distlib==0.3.8 +docker-pycreds==0.4.0 +einops==0.8.0 +evaluate==0.4.2 +exceptiongroup==1.2.1 +expecttest==0.2.1 +filelock==3.14.0 +frozenlist==1.4.1 +fsspec==2024.3.1 +gitdb==4.0.11 +google-auth-oauthlib==0.4.6 +google-auth==2.29.0 +grpcio==1.63.0 +habana-media-loader==1.15.1.15 +habana-pyhlml==1.15.1.15 +habana-torch-dataloader==1.15.1.15 +habana-torch-plugin==1.15.1.15 +habana_gpu_migration==1.15.1.15 +habana_quantization_toolkit==1.15.1.15 +hjson==3.1.0 +huggingface-hub==0.23.1 +identify==2.5.36 +idna==3.7 +iniconfig==2.0.0 +joblib==1.4.2 +jsonlines==4.0.0 +lightning-habana==1.4.0 +lightning-utilities==0.11.2 +lightning==2.2.0.post0 +lm_eval==0.4.2 +lm_eval==0.4.2 +lm_eval==0.4.2 +lxml==5.2.2 +mbstrdecoder==1.1.3 +more-itertools==10.2.0 +mpi4py==3.1.4 +mpmath==1.3.0 +multidict==6.0.5 +multiprocess==0.70.16 +networkx==3.3 +ninja==1.11.1.1 +nltk==3.8.1 +nodeenv==1.8.0 +numexpr==2.10.0 +numpy==1.23.5 +oauthlib==3.2.2 +packaging==24.0 +pandas==2.0.1 +pathspec==0.12.1 +pathvalidate==3.2.0 +peft==0.11.1 +perfetto==0.7.0 +pillow==10.3.0 +pip==22.0.2 +pip==23.3.1 +platformdirs==4.2.1 +pluggy==1.5.0 +portalocker==2.8.2 +pre-commit==3.3.3 +pretty-errors==1.2.25 +protobuf==3.20.3 +psutil==5.9.8 +py-cpuinfo==9.0.0 +pyarrow-hotfix==0.6 +pyarrow==16.1.0 +pyasn1==0.6.0 +pyasn1_modules==0.4.0 +pybind11==2.10.4 +pycparser==2.22 +pydantic==1.10.13 +pynvml==8.0.4 +pytablewriter==1.2.0 +pytest==8.2.0 +python-dateutil==2.9.0.post0 +pytorch-lightning==2.2.4 +pytz==2024.1 +regex==2023.5.5 +requests-oauthlib==2.0.0 +requests==2.31.0 +rouge_score==0.1.2 +rsa==4.9 +sacrebleu==2.4.2 +safetensors==0.4.3 +scikit-learn==1.5.0 +scipy==1.13.0 +sentencepiece==0.2.0 +sentry-sdk==2.2.1 +setproctitle==1.3.3 +setuptools==59.6.0 +setuptools==69.5.1 +six==1.16.0 +smmap==5.0.1 +sqlitedict==2.1.0 +symengine==0.11.0 +sympy==1.12 +tabledata==1.3.3 +tabulate==0.9.0 +tcolorpy==0.1.6 +tdqm==0.0.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 +tensorboard==2.11.2 +threadpoolctl==3.5.0 +tokenizers==0.19.1 +tomli==2.0.1 +torch==2.2.0a0+git8964477 +torch_tb_profiler==0.4.0 +torchaudio==2.2.0+08901ad +torchdata==0.7.1+5e6f7b7 +torchmetrics==1.4.0 +torchtext==0.17.0+400da5c +torchvision==0.17.0+b2383d4 +tqdm-multiprocess==0.0.11 +tqdm==4.66.4 +transformers==4.41.0 +typepy==1.3.2 +typing_extensions==4.11.0 +tzdata==2024.1 +urllib3==1.26.18 +virtualenv==20.26.1 +wandb==0.17.0 +wheel==0.37.1 +wheel==0.43.0 +word2number==1.1 +xxhash==3.4.1 +yamllint==1.35.1 +yarl==1.9.4 +zstandard==0.22.0 \ No newline at end of file diff --git a/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/wandb-metadata.json b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/wandb-metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0b9ab4818f6e61e753494ac3924d554acd1bd4bf --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240522_190023-cvyp4jfb/files/wandb-metadata.json @@ -0,0 +1,850 @@ +{ + "os": "Linux-5.15.0-92-generic-x86_64-with-glibc2.35", + "python": "3.10.12", + "heartbeatAt": "2024-05-22T19:00:24.497248", + "startedAt": "2024-05-22T19:00:23.945579", + "docker": null, + "cuda": null, + "args": [ + "--model", + "hf", + "--model_args", + "pretrained=/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step6000", + "--tasks", + "hellaswag,arc_easy,openbookqa,winogrande,sst2,mrpc", + "--batch_size", + "auto", + "--wandb_args", + "project=bharatgpt,group=trial_expt_2" + ], + "state": "running", + "program": "-m lm_eval.__main__", + "codePathLocal": null, + "git": { + "remote": "https://github.com/EleutherAI/lm-evaluation-harness", + "commit": null + }, + "email": null, + "root": "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness", + "host": "peacock-evaluation-worker-0", + "username": "root", + "executable": "/usr/bin/python3", + 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a/lm-evaluation-harness/wandb/run-20240523_050626-h62ge6b5/files/output.log b/lm-evaluation-harness/wandb/run-20240523_050626-h62ge6b5/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..d208ac9ea12130cff528a2c044cdf54cd18dc798 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240523_050626-h62ge6b5/files/output.log @@ -0,0 +1,34 @@ + +2024-05-23:05:06:26,787 INFO [__main__.py:251] Verbosity set to INFO +2024-05-23:05:06:31,370 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande'] +2024-05-23:05:06:31,371 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 +2024-05-23:05:06:31,371 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100'} +2024-05-23:05:06:32,597 INFO [huggingface.py:164] Using device 'cuda' +Traceback (most recent call last): + File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/usr/lib/python3.10/runpy.py", line 86, in _run_code + exec(code, run_globals) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in + cli_evaluate() + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate + results = evaluator.simple_evaluate( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper + return fn(*args, **kwargs) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate + lm = lm_eval.api.registry.get_model(model).create_from_arg_string( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string + return cls(**args, **args2) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__ + self._get_config( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config + self._config = transformers.AutoConfig.from_pretrained( + File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained + config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict + config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict + resolved_config_file = cached_file( + File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file + raise EnvironmentError( +OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100 does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100/tree/main' for available files. \ No newline at end of file diff --git a/lm-evaluation-harness/wandb/run-20240523_050626-h62ge6b5/files/requirements.txt b/lm-evaluation-harness/wandb/run-20240523_050626-h62ge6b5/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8150356038c46ec25f623f6e945d6dcb66a2e717 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240523_050626-h62ge6b5/files/requirements.txt @@ -0,0 +1,155 @@ +DataProperty==1.0.1 +GitPython==3.1.43 +Jinja2==3.1.4 +Markdown==3.6 +MarkupSafe==2.1.5 +Pillow-SIMD==7.0.0.post3 +PyYAML==6.0 +Werkzeug==3.0.3 +absl-py==2.1.0 +accelerate==0.30.1 +aiohttp==3.9.5 +aiosignal==1.3.1 +async-timeout==4.0.3 +attrs==23.2.0 +av==9.2.0 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.15.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +cmake==3.29.2 +colorama==0.4.6 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0000000000000000000000000000000000000000..5f1552ca81fc7c24b671469b1cc029443beb77e4 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240523_122942-38kvtgkv/files/config.yaml @@ -0,0 +1,43 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + python_version: 3.10.12 + cli_version: 0.17.0 + framework: huggingface + huggingface_version: 4.41.1 + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1716467382 + t: + 1: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 2: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 3: + - 23 + 4: 3.10.12 + 5: 0.17.0 + 6: 4.41.1 + 8: + - 5 + 13: linux-x86_64 diff --git a/lm-evaluation-harness/wandb/run-20240523_122942-38kvtgkv/files/output.log b/lm-evaluation-harness/wandb/run-20240523_122942-38kvtgkv/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..b743ceedcaa50ed2a88d74376d4d2cfc3247839d --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240523_122942-38kvtgkv/files/output.log @@ -0,0 +1,34 @@ + +2024-05-23:12:29:43,477 INFO [__main__.py:251] Verbosity set to INFO +2024-05-23:12:29:52,019 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande'] +2024-05-23:12:29:52,019 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 +2024-05-23:12:29:52,020 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000'} +2024-05-23:12:29:54,370 INFO [huggingface.py:164] Using device 'cuda' +Traceback (most recent call last): + File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/usr/lib/python3.10/runpy.py", line 86, in _run_code + exec(code, run_globals) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in + cli_evaluate() + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate + results = evaluator.simple_evaluate( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper + return fn(*args, **kwargs) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate + lm = lm_eval.api.registry.get_model(model).create_from_arg_string( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string + return cls(**args, **args2) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__ + self._get_config( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config + self._config = transformers.AutoConfig.from_pretrained( + File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained + config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict + config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict + resolved_config_file = cached_file( + File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file + raise EnvironmentError( +OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000 does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000/tree/main' for available files. \ No newline at end of file diff --git a/lm-evaluation-harness/wandb/run-20240523_122942-38kvtgkv/files/requirements.txt b/lm-evaluation-harness/wandb/run-20240523_122942-38kvtgkv/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..f675c3016b5332c1acf28f436e0b60adeead9c12 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240523_122942-38kvtgkv/files/requirements.txt @@ -0,0 +1,155 @@ +DataProperty==1.0.1 +GitPython==3.1.43 +Jinja2==3.1.4 +Markdown==3.6 +MarkupSafe==2.1.5 +Pillow-SIMD==7.0.0.post3 +PyYAML==6.0 +Werkzeug==3.0.3 +absl-py==2.1.0 +accelerate==0.30.1 +aiohttp==3.9.5 +aiosignal==1.3.1 +async-timeout==4.0.3 +attrs==23.2.0 +av==9.2.0 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.15.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +cmake==3.29.2 +colorama==0.4.6 +datasets==2.19.1 +deepspeed==0.12.4+hpu.synapse.v1.15.1 +dill==0.3.8 +distlib==0.3.8 +docker-pycreds==0.4.0 +einops==0.8.0 +evaluate==0.4.2 +exceptiongroup==1.2.1 +expecttest==0.2.1 +filelock==3.14.0 +frozenlist==1.4.1 +fsspec==2024.3.1 +gitdb==4.0.11 +google-auth-oauthlib==0.4.6 +google-auth==2.29.0 +grpcio==1.63.0 +habana-media-loader==1.15.1.15 +habana-pyhlml==1.15.1.15 +habana-torch-dataloader==1.15.1.15 +habana-torch-plugin==1.15.1.15 +habana_gpu_migration==1.15.1.15 +habana_quantization_toolkit==1.15.1.15 +hjson==3.1.0 +huggingface-hub==0.23.1 +identify==2.5.36 +idna==3.7 +iniconfig==2.0.0 +joblib==1.4.2 +jsonlines==4.0.0 +lightning-habana==1.4.0 +lightning-utilities==0.11.2 +lightning==2.2.0.post0 +lm_eval==0.4.2 +lm_eval==0.4.2 +lm_eval==0.4.2 +lxml==5.2.2 +mbstrdecoder==1.1.3 +more-itertools==10.2.0 +mpi4py==3.1.4 +mpmath==1.3.0 +multidict==6.0.5 +multiprocess==0.70.16 +networkx==3.3 +ninja==1.11.1.1 +nltk==3.8.1 +nodeenv==1.8.0 +numexpr==2.10.0 +numpy==1.23.5 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a/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/config.yaml b/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9f0c6bf85b06e0abb27e4a70780a3b983cb61e7b --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/config.yaml @@ -0,0 +1,44 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + python_version: 3.10.12 + cli_version: 0.17.0 + framework: huggingface + huggingface_version: 4.36.2 + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1717051575 + t: + 1: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 2: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 3: + - 13 + - 23 + 4: 3.10.12 + 5: 0.17.0 + 6: 4.36.2 + 8: + - 5 + 13: linux-x86_64 diff --git a/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/output.log b/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..da85f429a361abc3ad77b4f5a0da0faf89d321c7 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/output.log @@ -0,0 +1,38 @@ + +2024-05-30:06:46:15,983 INFO [__main__.py:251] Verbosity set to INFO +2024-05-30:06:46:25,242 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'boolq', 'copa', 'mrpc', 'piqa', 'sst2', 'winogrande'] +2024-05-30:06:46:25,243 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 +2024-05-30:06:46:25,243 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step50000', 'tokenizer': '/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k/'} +2024-05-30:06:46:27,533 INFO [huggingface.py:164] Using device 'cuda' +/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way. + warnings.warn( +Traceback (most recent call last): + File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/usr/lib/python3.10/runpy.py", line 86, in _run_code + exec(code, run_globals) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in + cli_evaluate() + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate + results = evaluator.simple_evaluate( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper + return fn(*args, **kwargs) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate + lm = lm_eval.api.registry.get_model(model).create_from_arg_string( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string + return cls(**args, **args2) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 237, in __init__ + self._create_tokenizer( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 630, in _create_tokenizer + self.tokenizer = transformers.AutoTokenizer.from_pretrained( + File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py", line 752, in from_pretrained + config = AutoConfig.from_pretrained( + File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 1082, in from_pretrained + config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 644, in get_config_dict + config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 699, in _get_config_dict + resolved_config_file = cached_file( + File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 360, in cached_file + raise EnvironmentError( +OSError: /mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k/ does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k//main' for available files. \ No newline at end of file diff --git a/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/requirements.txt b/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..7391d0fb5302364497bd6017486c36f0caae0613 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240530_064615-04r3nq4x/files/requirements.txt @@ -0,0 +1,154 @@ +DataProperty==1.0.1 +GitPython==3.1.43 +Jinja2==3.1.4 +Markdown==3.6 +MarkupSafe==2.1.5 +Pillow-SIMD==7.0.0.post3 +PyYAML==6.0 +Werkzeug==3.0.3 +absl-py==2.1.0 +accelerate==0.30.1 +aiohttp==3.9.5 +aiosignal==1.3.1 +async-timeout==4.0.3 +attrs==23.2.0 +av==9.2.0 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.15.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +cmake==3.29.2 +colorama==0.4.6 +datasets==2.19.1 +deepspeed==0.12.4+hpu.synapse.v1.15.1 +dill==0.3.8 +distlib==0.3.8 +docker-pycreds==0.4.0 +einops==0.8.0 +evaluate==0.4.2 +exceptiongroup==1.2.1 +expecttest==0.2.1 +filelock==3.14.0 +frozenlist==1.4.1 +fsspec==2024.3.1 +gitdb==4.0.11 +google-auth-oauthlib==0.4.6 +google-auth==2.29.0 +grpcio==1.63.0 +habana-media-loader==1.15.1.15 +habana-pyhlml==1.15.1.15 +habana-torch-dataloader==1.15.1.15 +habana-torch-plugin==1.15.1.15 +habana_gpu_migration==1.15.1.15 +habana_quantization_toolkit==1.15.1.15 +hjson==3.1.0 +huggingface-hub==0.23.2 +identify==2.5.36 +idna==3.7 +iniconfig==2.0.0 +joblib==1.4.2 +jsonlines==4.0.0 +lightning-habana==1.4.0 +lightning-utilities==0.11.2 +lightning==2.2.0.post0 +lm_eval==0.4.2 +lm_eval==0.4.2 +lm_eval==0.4.2 +lxml==5.2.2 +mbstrdecoder==1.1.3 +more-itertools==10.2.0 +mpi4py==3.1.4 +mpmath==1.3.0 +multidict==6.0.5 +multiprocess==0.70.16 +networkx==3.3 +ninja==1.11.1.1 +nltk==3.8.1 +nodeenv==1.8.0 +numexpr==2.10.0 +numpy==1.23.5 +oauthlib==3.2.2 +packaging==24.0 +pandas==2.0.1 +pathspec==0.12.1 +pathvalidate==3.2.0 +peft==0.11.1 +perfetto==0.7.0 +pip==22.0.2 +pip==23.3.1 +platformdirs==4.2.1 +pluggy==1.5.0 +portalocker==2.8.2 +pre-commit==3.3.3 +pretty-errors==1.2.25 +protobuf==3.20.3 +psutil==5.9.8 +py-cpuinfo==9.0.0 +pyarrow-hotfix==0.6 +pyarrow==16.1.0 +pyasn1==0.6.0 +pyasn1_modules==0.4.0 +pybind11==2.10.4 +pycparser==2.22 +pydantic==1.10.13 +pynvml==8.0.4 +pytablewriter==1.2.0 +pytest==8.2.0 +python-dateutil==2.9.0.post0 +pytorch-lightning==2.2.4 +pytz==2024.1 +regex==2023.5.5 +requests-oauthlib==2.0.0 +requests==2.31.0 +rouge_score==0.1.2 +rsa==4.9 +sacrebleu==2.4.2 +safetensors==0.4.3 +scikit-learn==1.5.0 +scipy==1.13.1 +sentencepiece==0.2.0 +sentry-sdk==2.3.1 +setproctitle==1.3.3 +setuptools==59.6.0 +setuptools==69.5.1 +six==1.16.0 +smmap==5.0.1 +sqlitedict==2.1.0 +symengine==0.11.0 +sympy==1.12 +tabledata==1.3.3 +tabulate==0.9.0 +tcolorpy==0.1.6 +tdqm==0.0.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 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+ value: + python_version: 3.10.12 + cli_version: 0.17.0 + framework: huggingface + huggingface_version: 4.36.2 + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1717051576 + t: + 1: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 2: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 3: + - 13 + - 23 + 4: 3.10.12 + 5: 0.17.0 + 6: 4.36.2 + 8: + - 5 + 13: linux-x86_64 diff --git a/lm-evaluation-harness/wandb/run-20240530_064616-w85c3758/files/output.log b/lm-evaluation-harness/wandb/run-20240530_064616-w85c3758/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..b890135688c301f409fb70fa02e6a1de54dd91de --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240530_064616-w85c3758/files/output.log @@ -0,0 +1,38 @@ + +2024-05-30:06:46:17,407 INFO [__main__.py:251] Verbosity set to INFO +2024-05-30:06:46:27,073 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'boolq', 'copa', 'mrpc', 'piqa', 'sst2', 'winogrande'] +2024-05-30:06:46:27,074 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 +2024-05-30:06:46:27,075 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step70000', 'tokenizer': '/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k/'} +2024-05-30:06:46:29,535 INFO [huggingface.py:164] Using device 'cuda' +/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way. + warnings.warn( +Traceback (most recent call last): + File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/usr/lib/python3.10/runpy.py", line 86, in _run_code + exec(code, run_globals) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in + cli_evaluate() + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate + results = evaluator.simple_evaluate( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper + return fn(*args, **kwargs) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate + lm = lm_eval.api.registry.get_model(model).create_from_arg_string( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string + return cls(**args, **args2) + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 237, in __init__ + self._create_tokenizer( + File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 630, in _create_tokenizer + self.tokenizer = transformers.AutoTokenizer.from_pretrained( + File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py", line 752, in from_pretrained + config = AutoConfig.from_pretrained( + File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 1082, in from_pretrained + config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 644, in get_config_dict + config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 699, in _get_config_dict + resolved_config_file = cached_file( + File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 360, in cached_file + raise EnvironmentError( +OSError: /mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k/ does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k//main' for available files. \ No newline at end of file diff --git a/lm-evaluation-harness/wandb/run-20240530_064616-w85c3758/files/requirements.txt b/lm-evaluation-harness/wandb/run-20240530_064616-w85c3758/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b21c96113204af90a7f13a98ab7ccbc9f5fa95a --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240530_064616-w85c3758/files/requirements.txt @@ -0,0 +1,153 @@ +DataProperty==1.0.1 +GitPython==3.1.43 +Jinja2==3.1.3 +Markdown==3.6 +MarkupSafe==2.1.5 +Pillow-SIMD==7.0.0.post3 +PyYAML==6.0 +Werkzeug==3.0.2 +absl-py==2.1.0 +accelerate==0.30.1 +aiohttp==3.9.4 +aiosignal==1.3.1 +async-timeout==4.0.3 +attrs==23.2.0 +av==9.2.0 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.15.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +cmake==3.29.2 +colorama==0.4.6 +datasets==2.19.1 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null + value: + python_version: 3.10.12 + cli_version: 0.17.0 + framework: huggingface + huggingface_version: 4.36.2 + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1717573927 + t: + 1: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 2: + - 1 + - 5 + - 11 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 100 + 3: + - 2 + - 13 + - 23 + - 62 + 4: 3.10.12 + 5: 0.17.0 + 6: 4.36.2 + 8: + - 5 + 13: linux-x86_64 +task_configs: + desc: null + value: + arc_easy: + task: arc_easy + group: + - ai2_arc + dataset_path: allenai/ai2_arc + dataset_name: ARC-Easy + training_split: train + validation_split: validation + test_split: test + doc_to_text: 'Question: {{question}} + + Answer:' + doc_to_target: '{{choices.label.index(answerKey)}}' + doc_to_choice: '{{choices.text}}' + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + aggregation: mean + higher_is_better: true + - metric: acc_norm + aggregation: mean + higher_is_better: true + output_type: multiple_choice + repeats: 1 + should_decontaminate: true + doc_to_decontamination_query: 'Question: {{question}} + + Answer:' + metadata: + version: 1.0 + boolq: + task: boolq + group: + - super-glue-lm-eval-v1 + dataset_path: super_glue + dataset_name: boolq + training_split: train + validation_split: validation + doc_to_text: '{{passage}} + + Question: {{question}}? + + Answer:' + doc_to_target: label + doc_to_choice: + - 'no' + - 'yes' + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + output_type: multiple_choice + repeats: 1 + should_decontaminate: true + doc_to_decontamination_query: passage + metadata: + version: 2.0 + copa: + task: copa + group: + - super-glue-lm-eval-v1 + dataset_path: super_glue + dataset_name: copa + training_split: train + validation_split: validation + doc_to_text: "def doc_to_text(doc):\n # Drop the period\n connector =\ + \ {\n \"cause\": \"because\",\n \"effect\": \"therefore\",\n\ + \ }[doc[\"question\"]]\n return doc[\"premise\"].strip()[:-1] + f\"\ + \ {connector}\"\n" + doc_to_target: "def doc_to_target(doc):\n correct_choice = doc[\"choice1\"\ + ] if doc[\"label\"] == 0 else doc[\"choice2\"]\n # Connect the sentences\n\ + \ return \" \" + convert_choice(correct_choice)\n" + doc_to_choice: "def doc_to_choice(doc):\n return [\" \" + convert_choice(doc[\"\ + choice1\"]), \" \" + convert_choice(doc[\"choice2\"])]\n" + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + output_type: multiple_choice + repeats: 1 + should_decontaminate: false + metadata: + version: 1.0 + indic_arc_challenge_hi: + task: indic_arc_challenge_hi + group: Cognitive-Lab/Indic-ARC-Challenge + dataset_path: Cognitive-Lab/Indic-ARC-Challenge + dataset_name: hi + test_split: test + doc_to_text: 'Question: {{translated_question}} + + Answer:' + doc_to_target: '{{translated_choices.label.index(answerKey)}}' + doc_to_choice: '{{translated_choices.text}}' + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + aggregation: mean + higher_is_better: true + output_type: multiple_choice + repeats: 1 + should_decontaminate: true + doc_to_decontamination_query: 'Question: {{translated_question}} + + Answer:' + metadata: + version: 1.0 + indic_arc_easy_hi: + task: indic_arc_easy_hi + group: Cognitive-Lab/Indic-ARC-Easy + dataset_path: Cognitive-Lab/Indic-ARC-Easy + dataset_name: hi + test_split: test + doc_to_text: 'Question: {{translated_question}} + + Answer:' + doc_to_target: '{{translated_choices.label.index(answerKey)}}' + doc_to_choice: '{{translated_choices.text}}' + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + aggregation: mean + higher_is_better: true + output_type: multiple_choice + repeats: 1 + should_decontaminate: true + doc_to_decontamination_query: 'Question: {{translated_question}} + + Answer:' + metadata: + version: 1.0 + indic_boolq_hi: + task: indic_boolq_hi + group: Cognitive-Lab/Indic-BoolQ + dataset_path: Cognitive-Lab/Indic-BoolQ + dataset_name: hi + validation_split: validation + doc_to_text: 'Passage: {translated_passage} + + Question: {translated_question.strip()} + + Answer:' + doc_to_target: answer + doc_to_choice: + - 'true' + - 'false' + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + aggregation: mean + higher_is_better: true + output_type: multiple_choice + repeats: 1 + should_decontaminate: false + metadata: + version: 1.0 + mrpc: + task: mrpc + group: glue + dataset_path: glue + dataset_name: mrpc + training_split: train + validation_split: validation + doc_to_text: 'Sentence 1: {{sentence1}} + + Sentence 2: {{sentence2}} + + Question: Do both sentences mean the same thing? + + Answer:' + doc_to_target: label + doc_to_choice: + - 'no' + - 'yes' + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + - metric: f1 + output_type: multiple_choice + repeats: 1 + should_decontaminate: false + metadata: + version: 1.0 + piqa: + task: piqa + dataset_path: piqa + training_split: train + validation_split: validation + doc_to_text: 'Question: {{goal}} + + Answer:' + doc_to_target: label + doc_to_choice: '{{[sol1, sol2]}}' + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + aggregation: mean + higher_is_better: true + - metric: acc_norm + aggregation: mean + higher_is_better: true + output_type: multiple_choice + repeats: 1 + should_decontaminate: true + doc_to_decontamination_query: goal + metadata: + version: 1.0 + sst2: + task: sst2 + group: glue + dataset_path: glue + dataset_name: sst2 + training_split: train + validation_split: validation + doc_to_text: '{{sentence}} + + Question: Is this sentence positive or negative? + + Answer:' + doc_to_target: label + doc_to_choice: + - negative + - positive + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + output_type: multiple_choice + repeats: 1 + should_decontaminate: false + metadata: + version: 1.0 + winogrande: + task: winogrande + dataset_path: winogrande + dataset_name: winogrande_xl + training_split: train + validation_split: validation + doc_to_text: "def doc_to_text(doc):\n answer_to_num = {\"1\": 0, \"2\": 1}\n\ + \ return answer_to_num[doc[\"answer\"]]\n" + doc_to_target: "def doc_to_target(doc):\n idx = doc[\"sentence\"].index(\"\ + _\") + 1\n return doc[\"sentence\"][idx:].strip()\n" + doc_to_choice: "def doc_to_choice(doc):\n idx = doc[\"sentence\"].index(\"\ + _\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"\ + sentence\"][:idx] + opt for opt in options]\n" + description: '' + target_delimiter: ' ' + fewshot_delimiter: ' + + + ' + num_fewshot: 0 + metric_list: + - metric: acc + aggregation: mean + higher_is_better: true + output_type: multiple_choice + repeats: 1 + should_decontaminate: true + doc_to_decontamination_query: sentence + metadata: + version: 1.0 +cli_configs: + desc: null + value: + model: hf + model_args: pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn/llamav2-3b/hf/global_step50000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer + batch_size: auto + batch_sizes: + - 64 + device: null + use_cache: null + limit: null + bootstrap_iters: 100000 + gen_kwargs: null diff --git a/lm-evaluation-harness/wandb/run-20240605_075207-j37ty43h/files/media/table/evaluation/eval_results_1_4e1ddc4a0f749f1dd1f8.table.json b/lm-evaluation-harness/wandb/run-20240605_075207-j37ty43h/files/media/table/evaluation/eval_results_1_4e1ddc4a0f749f1dd1f8.table.json new file mode 100644 index 0000000000000000000000000000000000000000..f714f793dcb90a5ca542a079aae3c9f08ca71460 --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240605_075207-j37ty43h/files/media/table/evaluation/eval_results_1_4e1ddc4a0f749f1dd1f8.table.json @@ -0,0 +1 @@ +{"columns": ["Tasks", "Version", "Filter", "num_fewshot", "Metric", "Value", "Stderr"], "data": [["winogrande", 1.0, "none", 0, "acc", "0.5122336227308603", "0.0140"], ["sst2", 1.0, "none", 0, "acc", "0.5", "0.0169"], ["piqa", 1.0, "none", 0, "acc", "0.5184983677910773", "0.0117"], ["piqa", 1.0, "none", 0, "acc_norm", "0.49836779107725787", "0.0117"], ["mrpc", 1.0, "none", 0, "acc", "0.3161764705882353", "0.0230"], ["mrpc", 1.0, "none", 0, "f1", "0.0", "0.0000"], ["indic_boolq_hi", 1.0, "none", 0, "acc", "0.3782874617737003", "0.0085"], ["indic_arc_easy_hi", 1.0, "none", 0, "acc", "0.25715488215488214", "0.0090"], ["indic_arc_challenge_hi", 1.0, "none", 0, "acc", "0.22013651877133106", "0.0121"], ["copa", 1.0, "none", 0, "acc", "0.62", "0.0488"], ["boolq", 2.0, "none", 0, "acc", "0.3782874617737003", "0.0085"], ["arc_easy", 1.0, "none", 0, "acc", "0.2680976430976431", "0.0091"], ["arc_easy", 1.0, "none", 0, "acc_norm", "0.25252525252525254", "0.0089"]]} \ No newline at end of file diff --git a/lm-evaluation-harness/wandb/run-20240605_075207-j37ty43h/files/output.log b/lm-evaluation-harness/wandb/run-20240605_075207-j37ty43h/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..556517f1c2d127c4063394796a161e8eda42b28d --- /dev/null +++ b/lm-evaluation-harness/wandb/run-20240605_075207-j37ty43h/files/output.log @@ -0,0 +1,746 @@ + +2024-06-05:07:52:08,130 INFO [__main__.py:251] Verbosity set to INFO +2024-06-05:07:52:17,287 INFO [__main__.py:335] Selected Tasks: ['arc_easy', 'boolq', 'copa', 'indic_arc_challenge_hi', 'indic_arc_easy_hi', 'indic_boolq_hi', 'mrpc', 'piqa', 'sst2', 'winogrande'] +2024-06-05:07:52:17,288 INFO [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 +2024-06-05:07:52:17,289 INFO [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/eval/checkpoint-enhibn/llamav2-3b/hf/global_step50000', 'tokenizer': '/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer'} +2024-06-05:07:52:19,592 INFO [huggingface.py:164] Using device 'cuda' +/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way. + warnings.warn( +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +2024-06-05:07:52:46,986 WARNING [task.py:763] [Task: boolq] metric acc is defined, but aggregation is not. using default aggregation=mean +2024-06-05:07:52:46,987 WARNING [task.py:775] [Task: boolq] metric acc is defined, but higher_is_better is not. using default higher_is_better=True +/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for super_glue contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/super_glue +You can avoid this message in future by passing the argument `trust_remote_code=True`. +Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. + warnings.warn( +2024-06-05:07:52:49,416 WARNING [task.py:763] [Task: copa] metric acc is defined, but aggregation is not. using default aggregation=mean +2024-06-05:07:52:49,417 WARNING [task.py:775] [Task: copa] metric acc is defined, but higher_is_better is not. using default higher_is_better=True +2024-06-05:07:52:53,709 WARNING [task.py:322] [Task: indic_arc_challenge_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended. +2024-06-05:07:52:53,709 WARNING [task.py:322] [Task: indic_arc_challenge_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended. +2024-06-05:07:52:55,930 WARNING [task.py:322] [Task: indic_arc_easy_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended. +2024-06-05:07:52:55,930 WARNING [task.py:322] [Task: indic_arc_easy_hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended. +2024-06-05:07:52:58,459 WARNING [task.py:763] [Task: mrpc] metric acc is defined, but aggregation is not. using default aggregation=mean +2024-06-05:07:52:58,459 WARNING [task.py:775] [Task: mrpc] metric acc is defined, but higher_is_better is not. using default higher_is_better=True +2024-06-05:07:52:58,460 WARNING [task.py:763] [Task: mrpc] metric f1 is defined, but aggregation is not. using default aggregation=f1 +2024-06-05:07:52:58,460 WARNING [task.py:775] [Task: mrpc] metric f1 is defined, but higher_is_better is not. using default higher_is_better=True +/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for piqa contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/piqa +You can avoid this message in future by passing the argument `trust_remote_code=True`. +Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. + warnings.warn( +2024-06-05:07:53:07,025 WARNING [task.py:763] [Task: sst2] metric acc is defined, but aggregation is not. using default aggregation=mean +2024-06-05:07:53:07,025 WARNING [task.py:775] [Task: sst2] metric acc is defined, but higher_is_better is not. using default higher_is_better=True +/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for winogrande contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/winogrande +You can avoid this message in future by passing the argument `trust_remote_code=True`. +Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. + warnings.warn( +2024-06-05:07:53:16,528 INFO [task.py:395] Building contexts for winogrande on rank 0... +100%|██████████| 1267/1267 [00:00<00:00, 69883.92it/s] +2024-06-05:07:53:16,611 INFO [task.py:395] Building contexts for sst2 on rank 0... +100%|██████████| 872/872 [00:00<00:00, 2566.98it/s] +2024-06-05:07:53:16,978 INFO [task.py:395] Building contexts for piqa on rank 0... +100%|██████████| 1838/1838 [00:01<00:00, 1095.02it/s] +2024-06-05:07:53:18,730 INFO [task.py:395] Building contexts for mrpc on rank 0... +100%|██████████| 408/408 [00:00<00:00, 1884.86it/s] +2024-06-05:07:53:18,964 INFO [task.py:395] Building contexts for indic_boolq_hi on rank 0... +100%|██████████| 3270/3270 [00:01<00:00, 3170.45it/s] +2024-06-05:07:53:20,166 INFO [task.py:395] Building contexts for indic_arc_easy_hi on rank 0... +100%|██████████| 2376/2376 [00:02<00:00, 1137.10it/s] +2024-06-05:07:53:22,492 INFO [task.py:395] Building contexts for indic_arc_challenge_hi on rank 0... +100%|██████████| 1172/1172 [00:01<00:00, 1135.19it/s] +2024-06-05:07:53:23,642 INFO [task.py:395] Building contexts for copa on rank 0... +100%|██████████| 100/100 [00:00<00:00, 60523.87it/s] +2024-06-05:07:53:23,651 INFO [task.py:395] Building contexts for boolq on rank 0... +100%|██████████| 3270/3270 [00:01<00:00, 1971.71it/s] +2024-06-05:07:53:25,440 INFO [task.py:395] Building contexts for arc_easy on rank 0... +100%|██████████| 2376/2376 [00:02<00:00, 1062.15it/s] +2024-06-05:07:53:27,822 INFO [evaluator.py:379] Running loglikelihood requests +Token indices sequence length is longer than the specified maximum sequence length for this model (1333 > 1024). Running this sequence through the model will result in indexing errors +Running loglikelihood requests: 0%| | 0/45739 [00:00>> from transformers import M2M100Config, M2M100Model + + >>> # Initializing a M2M100 facebook/m2m100_418M style configuration + >>> configuration = M2M100Config() + + >>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration + >>> model = M2M100Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "m2m_100" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} + + def __init__( + self, + vocab_size=128112, + max_position_embeddings=1024, + encoder_layers=12, + encoder_ffn_dim=4096, + encoder_attention_heads=16, + decoder_layers=12, + decoder_ffn_dim=4096, + decoder_attention_heads=16, + encoder_layerdrop=0.05, + decoder_layerdrop=0.05, + use_cache=True, + is_encoder_decoder=True, + activation_function="relu", + d_model=1024, + dropout=0.1, + attention_dropout=0.1, + activation_dropout=0.0, + init_std=0.02, + decoder_start_token_id=2, + scale_embedding=True, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.d_model = d_model + self.encoder_ffn_dim = encoder_ffn_dim + self.encoder_layers = encoder_layers + self.encoder_attention_heads = encoder_attention_heads + self.decoder_ffn_dim = decoder_ffn_dim + self.decoder_layers = decoder_layers + self.decoder_attention_heads = decoder_attention_heads + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_function = activation_function + self.init_std = init_std + self.encoder_layerdrop = encoder_layerdrop + self.decoder_layerdrop = decoder_layerdrop + self.use_cache = use_cache + self.num_hidden_layers = encoder_layers + self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + is_encoder_decoder=is_encoder_decoder, + decoder_start_token_id=decoder_start_token_id, + **kwargs, + ) + + +class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast): + @property + def inputs(self) -> Mapping[str, Mapping[int, str]]: + common_inputs = OrderedDict( + [ + ("input_ids", {0: "batch", 1: "encoder_sequence"}), + ("attention_mask", {0: "batch", 1: "encoder_sequence"}), + ] + ) + + if self.use_past: + common_inputs["decoder_input_ids"] = {0: "batch"} + common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} + else: + common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} + common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"} + + if self.use_past: + self.fill_with_past_key_values_(common_inputs, direction="inputs") + return common_inputs + + # Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering + # A better name would be _generate_dummy_inputs_for_encoder_and_decoder because sequence classification and question + # answering are not supported for M2M100, but this name is preserved to be able to check that the copy matches what + # was done for BART so that it can be updated if need be. + def _generate_dummy_inputs_for_sequence_classification_and_question_answering( + self, + tokenizer: PreTrainedTokenizer, + batch_size: int = -1, + seq_length: int = -1, + is_pair: bool = False, + framework: Optional[TensorType] = None, + ) -> Mapping[str, Any]: + # Copied from OnnxConfig.generate_dummy_inputs + # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity. + # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX + batch_size = compute_effective_axis_dimension( + batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 + ) + + # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX + token_to_add = tokenizer.num_special_tokens_to_add(is_pair) + seq_length = compute_effective_axis_dimension( + seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add + ) + + # Generate dummy inputs according to compute batch and sequence + dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size + common_inputs = dict(tokenizer(dummy_input, return_tensors=framework)) + return common_inputs + + # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._generate_dummy_inputs_for_default_and_seq2seq_lm + def _generate_dummy_inputs_for_default_and_seq2seq_lm( + self, + tokenizer: PreTrainedTokenizer, + batch_size: int = -1, + seq_length: int = -1, + is_pair: bool = False, + framework: Optional[TensorType] = None, + ) -> Mapping[str, Any]: + encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( + tokenizer, batch_size, seq_length, is_pair, framework + ) + + # Generate decoder inputs + decoder_seq_length = seq_length if not self.use_past else 1 + decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( + tokenizer, batch_size, decoder_seq_length, is_pair, framework + ) + decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()} + common_inputs = dict(**encoder_inputs, **decoder_inputs) + + if self.use_past: + if not is_torch_available(): + raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") + else: + import torch + batch, encoder_seq_length = common_inputs["input_ids"].shape + decoder_seq_length = common_inputs["decoder_input_ids"].shape[1] + num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads + encoder_shape = ( + batch, + num_encoder_attention_heads, + encoder_seq_length, + self._config.hidden_size // num_encoder_attention_heads, + ) + decoder_past_length = decoder_seq_length + 3 + decoder_shape = ( + batch, + num_decoder_attention_heads, + decoder_past_length, + self._config.hidden_size // num_decoder_attention_heads, + ) + + common_inputs["decoder_attention_mask"] = torch.cat( + [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1 + ) + + common_inputs["past_key_values"] = [] + # If the number of encoder and decoder layers are present in the model configuration, both are considered + num_encoder_layers, num_decoder_layers = self.num_layers + min_num_layers = min(num_encoder_layers, num_decoder_layers) + max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers + remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" + + for _ in range(min_num_layers): + common_inputs["past_key_values"].append( + ( + torch.zeros(decoder_shape), + torch.zeros(decoder_shape), + torch.zeros(encoder_shape), + torch.zeros(encoder_shape), + ) + ) + # TODO: test this. + shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape + for _ in range(min_num_layers, max_num_layers): + common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) + return common_inputs + + generate_dummy_inputs = _generate_dummy_inputs_for_default_and_seq2seq_lm diff --git a/venv/lib/python3.10/site-packages/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py b/venv/lib/python3.10/site-packages/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..97265fbdcf9346fbda7359a646503c1d2f7c4663 --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py @@ -0,0 +1,85 @@ +# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse + +import torch +from torch import nn + +from transformers import M2M100Config, M2M100ForConditionalGeneration + + +def remove_ignore_keys_(state_dict): + ignore_keys = [ + "encoder.version", + "decoder.version", + "model.encoder.version", + "model.decoder.version", + "decoder.output_projection.weight", + "_float_tensor", + "encoder.embed_positions._float_tensor", + "decoder.embed_positions._float_tensor", + ] + for k in ignore_keys: + state_dict.pop(k, None) + + +def make_linear_from_emb(emb): + vocab_size, emb_size = emb.weight.shape + lin_layer = nn.Linear(vocab_size, emb_size, bias=False) + lin_layer.weight.data = emb.weight.data + return lin_layer + + +def convert_fairseq_m2m100_checkpoint_from_disk(checkpoint_path): + m2m_100 = torch.load(checkpoint_path, map_location="cpu") + args = m2m_100["args"] or m2m_100["cfg"]["model"] + state_dict = m2m_100["model"] + remove_ignore_keys_(state_dict) + vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0] + + config = M2M100Config( + vocab_size=vocab_size, + max_position_embeddings=1024, + encoder_layers=args.encoder_layers, + decoder_layers=args.decoder_layers, + encoder_attention_heads=args.encoder_attention_heads, + decoder_attention_heads=args.decoder_attention_heads, + encoder_ffn_dim=args.encoder_ffn_embed_dim, + decoder_ffn_dim=args.decoder_ffn_embed_dim, + d_model=args.encoder_embed_dim, + encoder_layerdrop=args.encoder_layerdrop, + decoder_layerdrop=args.decoder_layerdrop, + dropout=args.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + activation_function="relu", + ) + + state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"] + model = M2M100ForConditionalGeneration(config) + model.model.load_state_dict(state_dict, strict=False) + model.lm_head = make_linear_from_emb(model.model.shared) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.") + parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") + args = parser.parse_args() + model = convert_fairseq_m2m100_checkpoint_from_disk(args.fairseq_pathß) + model.save_pretrained(args.pytorch_dump_folder_path) diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/__init__.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d87828da69f5d198a7d119c66a1ff056822e337c --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/__init__.py @@ -0,0 +1,141 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_tf_available, + is_tokenizers_available, + is_torch_available, +) + + +_import_structure = { + "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"], + "feature_extraction_whisper": ["WhisperFeatureExtractor"], + "processing_whisper": ["WhisperProcessor"], + "tokenization_whisper": ["WhisperTokenizer"], +} + +try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["tokenization_whisper_fast"] = ["WhisperTokenizerFast"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_whisper"] = [ + "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", + "WhisperForCausalLM", + "WhisperForConditionalGeneration", + "WhisperModel", + "WhisperPreTrainedModel", + "WhisperForAudioClassification", + ] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_whisper"] = [ + "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFWhisperForConditionalGeneration", + "TFWhisperModel", + "TFWhisperPreTrainedModel", + ] + +try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_flax_whisper"] = [ + "FlaxWhisperForConditionalGeneration", + "FlaxWhisperModel", + "FlaxWhisperPreTrainedModel", + "FlaxWhisperForAudioClassification", + ] + + +if TYPE_CHECKING: + from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig + from .feature_extraction_whisper import WhisperFeatureExtractor + from .processing_whisper import WhisperProcessor + from .tokenization_whisper import WhisperTokenizer + + try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .tokenization_whisper_fast import WhisperTokenizerFast + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_whisper import ( + WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, + WhisperForAudioClassification, + WhisperForCausalLM, + WhisperForConditionalGeneration, + WhisperModel, + WhisperPreTrainedModel, + ) + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_whisper import ( + TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, + TFWhisperForConditionalGeneration, + TFWhisperModel, + TFWhisperPreTrainedModel, + ) + + try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_flax_whisper import ( + FlaxWhisperForAudioClassification, + FlaxWhisperForConditionalGeneration, + FlaxWhisperModel, + FlaxWhisperPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/__pycache__/__init__.cpython-310.pyc b/venv/lib/python3.10/site-packages/transformers/models/whisper/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8b1a99e9eb62ccd5e814f0dd0d4b2dc094ea86c9 Binary files /dev/null and b/venv/lib/python3.10/site-packages/transformers/models/whisper/__pycache__/__init__.cpython-310.pyc differ diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/__pycache__/modeling_whisper.cpython-310.pyc b/venv/lib/python3.10/site-packages/transformers/models/whisper/__pycache__/modeling_whisper.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..edef01a4689775c3d06c4d5043b3286556f47d98 Binary files /dev/null and b/venv/lib/python3.10/site-packages/transformers/models/whisper/__pycache__/modeling_whisper.cpython-310.pyc differ diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/configuration_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/configuration_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..ec9c64df1bdb8144338607de9492ad21db8e9699 --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/configuration_whisper.py @@ -0,0 +1,343 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Whisper model configuration""" + +from collections import OrderedDict +from typing import TYPE_CHECKING, Any, Mapping, Optional, Union + +from ...configuration_utils import PretrainedConfig +from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast +from ...utils import logging + + +if TYPE_CHECKING: + from ...feature_extraction_utils import FeatureExtractionMixin + from ...tokenization_utils_base import PreTrainedTokenizerBase + from ...utils import TensorType + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +# fmt: off +NON_SPEECH_TOKENS = [ + 1, 2, 7, 8, 9, 10, 14, 25, + 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, + 63, 90, 91, 92, 93, 357, 366, 438, 532, 685, + 705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377, + 1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211, + 4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786, + 11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791, + 17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409, + 34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361 +] +NON_SPEECH_TOKENS_MULTI = [ + 1, 2, 7, 8, 9, 10, 14, 25, + 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, + 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, + 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, + 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, + 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, + 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, + 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, + 42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362 +] +# fmt: on + + +class WhisperConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a + Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of the Whisper + [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 51865): + Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the + `decoder_input_ids` passed when calling [`WhisperModel`] + num_mel_bins (`int`, *optional*, defaults to 80): + Number of mel features used per input features. Should correspond to the value used in the + `WhisperProcessor` class. + encoder_layers (`int`, *optional*, defaults to 4): + Number of encoder layers. + decoder_layers (`int`, *optional*, defaults to 4): + Number of decoder layers. + encoder_attention_heads (`int`, *optional*, defaults to 6): + Number of attention heads for each attention layer in the Transformer encoder. + decoder_attention_heads (`int`, *optional*, defaults to 6): + Number of attention heads for each attention layer in the Transformer decoder. + encoder_ffn_dim (`int`, *optional*, defaults to 1536): + Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. + decoder_ffn_dim (`int`, *optional*, defaults to 1536): + Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. + encoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + decoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + decoder_start_token_id (`int`, *optional*, defaults to 50257): + Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids` + are provided to the `generate` function. It is used to guide the model`s generation process depending on + the task. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). + is_encoder_decoder (`bool`, *optional*, defaults to `True`): + Whether the model is used as an encoder/decoder or not. + activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + d_model (`int`, *optional*, defaults to 384): + Dimensionality of the layers. + dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + activation_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for activations inside the fully connected layer. + init_std (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + scale_embedding (`bool`, *optional*, defaults to False): + Scale embeddings by diving by sqrt(d_model). + max_source_positions (`int`, *optional*, defaults to 1500): + The maximum sequence length of log-mel filter-bank features that this model might ever be used with. + max_target_positions (`int`, *optional*, defaults to 448): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + pad_token_id (`int`, *optional*, defaults to 50256): + Padding token id. + bos_token_id (`int`, *optional*, defaults to 50256): + Begin of stream token id. + eos_token_id (`int`, *optional*, defaults to 50256): + End of stream token id. + suppress_tokens (`List[int]`, *optional*): + A list containing the non-speech tokens that will be used by the logit processor in the `generate` + function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the + `multilingual` model. + begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`): + A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as + the token for `" "` (`blank_token_id`) and the `eos_token_id` + use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): + Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an + instance of [`WhisperForAudioClassification`]. + classifier_proj_size (`int`, *optional*, defaults to 256): + Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an + instance of [`WhisperForAudioClassification`]. + apply_spec_augment (`bool`, *optional*, defaults to `False`): + Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see + [SpecAugment: A Simple Data Augmentation Method for Automatic Speech + Recognition](https://arxiv.org/abs/1904.08779). + mask_time_prob (`float`, *optional*, defaults to 0.05): + Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking + procecure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If + reasoning from the propability of each feature vector to be chosen as the start of the vector span to be + masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the + actual percentage of masked vectors. This is only relevant if `apply_spec_augment == True`. + mask_time_length (`int`, *optional*, defaults to 10): + Length of vector span along the time axis. + mask_time_min_masks (`int`, *optional*, defaults to 2),: + The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, + irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < + mask_time_min_masks'' + mask_feature_prob (`float`, *optional*, defaults to 0.0): + Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The + masking procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over + the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector + span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap + may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is + True`. + mask_feature_length (`int`, *optional*, defaults to 10): + Length of vector span along the feature axis. + mask_feature_min_masks (`int`, *optional*, defaults to 0),: + The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time + step, irrespectively of `mask_feature_prob`. Only relevant if + `mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks`. + median_filter_width (`int`, *optional*, defaults to 7): + Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps. + Should be an odd number. + + Example: + + ```python + >>> from transformers import WhisperConfig, WhisperModel + + >>> # Initializing a Whisper tiny style configuration + >>> configuration = WhisperConfig() + + >>> # Initializing a model (with random weights) from the tiny style configuration + >>> model = WhisperModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "whisper" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} + + def __init__( + self, + vocab_size=51865, + num_mel_bins=80, + encoder_layers=4, + encoder_attention_heads=6, + decoder_layers=4, + decoder_attention_heads=6, + decoder_ffn_dim=1536, + encoder_ffn_dim=1536, + encoder_layerdrop=0.0, + decoder_layerdrop=0.0, + decoder_start_token_id=50257, + use_cache=True, + is_encoder_decoder=True, + activation_function="gelu", + d_model=384, + dropout=0.0, + attention_dropout=0.0, + activation_dropout=0.0, + init_std=0.02, + scale_embedding=False, + max_source_positions=1500, + max_target_positions=448, + pad_token_id=50256, + bos_token_id=50256, + eos_token_id=50256, + suppress_tokens=None, + begin_suppress_tokens=[220, 50256], + use_weighted_layer_sum=False, + classifier_proj_size=256, + apply_spec_augment=False, + mask_time_prob=0.05, + mask_time_length=10, + mask_time_min_masks=2, + mask_feature_prob=0.0, + mask_feature_length=10, + mask_feature_min_masks=0, + median_filter_width=7, + **kwargs, + ): + self.vocab_size = vocab_size + self.num_mel_bins = num_mel_bins + self.d_model = d_model + self.encoder_layers = encoder_layers + self.encoder_attention_heads = encoder_attention_heads + self.decoder_layers = decoder_layers + self.decoder_attention_heads = decoder_attention_heads + self.decoder_ffn_dim = decoder_ffn_dim + self.encoder_ffn_dim = encoder_ffn_dim + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_function = activation_function + self.init_std = init_std + self.encoder_layerdrop = encoder_layerdrop + self.decoder_layerdrop = decoder_layerdrop + self.use_cache = use_cache + self.num_hidden_layers = encoder_layers + self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True + self.max_source_positions = max_source_positions + self.max_target_positions = max_target_positions + + # Audio Classification-specific parameters. Feel free to ignore for other classes. + self.classifier_proj_size = classifier_proj_size + self.use_weighted_layer_sum = use_weighted_layer_sum + + # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 + self.apply_spec_augment = apply_spec_augment + self.mask_time_prob = mask_time_prob + self.mask_time_length = mask_time_length + self.mask_time_min_masks = mask_time_min_masks + self.mask_feature_prob = mask_feature_prob + self.mask_feature_length = mask_feature_length + self.mask_feature_min_masks = mask_feature_min_masks + + self.median_filter_width = median_filter_width + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + is_encoder_decoder=is_encoder_decoder, + decoder_start_token_id=decoder_start_token_id, + suppress_tokens=suppress_tokens, + begin_suppress_tokens=begin_suppress_tokens, + **kwargs, + ) + + +class WhisperOnnxConfig(OnnxSeq2SeqConfigWithPast): + @property + def inputs(self) -> Mapping[str, Mapping[int, str]]: + common_inputs = OrderedDict( + [ + ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}), + ] + ) + if self.use_past: + common_inputs["decoder_input_ids"] = {0: "batch"} + else: + common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} + + if self.use_past: + self.fill_with_past_key_values_(common_inputs, direction="inputs") + + return common_inputs + + def generate_dummy_inputs( + self, + preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], + batch_size: int = -1, + seq_length: int = -1, + is_pair: bool = False, + framework: Optional["TensorType"] = None, + sampling_rate: int = 22050, + time_duration: float = 5.0, + frequency: int = 220, + ) -> Mapping[str, Any]: + dummy_inputs = OrderedDict() + encoder_inputs = OnnxConfig.generate_dummy_inputs( + self, + preprocessor=preprocessor.feature_extractor, + batch_size=batch_size, + framework=framework, + sampling_rate=sampling_rate, + time_duration=time_duration, + frequency=frequency, + ) + encoder_sequence_length = encoder_inputs["input_features"].shape[2] + seq_length = encoder_sequence_length // 2 if self.use_past else seq_length + + decoder_inputs = super().generate_dummy_inputs( + preprocessor.tokenizer, batch_size, seq_length, is_pair, framework + ) + + dummy_inputs["input_features"] = encoder_inputs.pop("input_features") + dummy_inputs["decoder_input_ids"] = decoder_inputs.pop("decoder_input_ids") + + if "past_key_values" in decoder_inputs: + dummy_inputs["past_key_values"] = decoder_inputs.pop("past_key_values") + + return dummy_inputs + + @property + def atol_for_validation(self) -> float: + return 1e-3 diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/convert_openai_to_hf.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/convert_openai_to_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..6e859dc9dd8d09a952fd0ba74e5bf0fac7f47c82 --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/convert_openai_to_hf.py @@ -0,0 +1,370 @@ +#!/usr/bin/env python +"""Converts a Whisper model in OpenAI format to Hugging Face format.""" +# Copyright 2022 The HuggingFace Inc. team and the OpenAI team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import io +import json +import os +import tempfile +import urllib +import warnings +from typing import Any, List, Optional, Tuple + +import torch +from huggingface_hub.utils import insecure_hashlib +from torch import nn +from tqdm import tqdm + +from transformers import ( + GenerationConfig, + WhisperConfig, + WhisperFeatureExtractor, + WhisperForConditionalGeneration, + WhisperProcessor, + WhisperTokenizer, + WhisperTokenizerFast, +) +from transformers.models.whisper.tokenization_whisper import LANGUAGES, bytes_to_unicode +from transformers.utils.import_utils import _is_package_available + + +_MODELS = { + "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", + "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", + "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", + "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", + "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", + "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", + "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", + "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", + "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", + "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", +} + + +_TOKENIZERS = { + "multilingual": "https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/multilingual.tiktoken", + "english": "https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/gpt2.tiktoken", +} + + +def _get_generation_config( + is_multilingual: bool, + num_languages: int = 100, + openai_version: Optional[str] = None, +) -> GenerationConfig: + """ + Loads the appropriate generation config from HF repo + """ + if openai_version is not None: + repo = f"openai/whisper-{openai_version}" + elif not is_multilingual: + repo = "openai/whisper-medium.en" + elif num_languages < 100: + repo = "openai/whisper-large-v2" + else: + repo = "openai/whisper-large-v3" + + gen_cfg = GenerationConfig.from_pretrained(repo) + if openai_version is None: + gen_cfg.alignment_heads = None + warnings.warn( + "Alignment heads have not been included in the generation config, since they are available " + "only for the original OpenAI checkpoints." + "If you want to use word-level timestamps with a custom version of Whisper," + "see https://github.com/openai/whisper/blob/main/notebooks/Multilingual_ASR.ipynb" + "for the example of how to produce word-level timestamps manually." + ) + + return gen_cfg + + +def remove_ignore_keys_(state_dict): + ignore_keys = ["layers", "blocks"] + for k in ignore_keys: + state_dict.pop(k, None) + + +WHISPER_MAPPING = { + "blocks": "layers", + "mlp.0": "fc1", + "mlp.2": "fc2", + "mlp_ln": "final_layer_norm", + ".attn.query": ".self_attn.q_proj", + ".attn.key": ".self_attn.k_proj", + ".attn.value": ".self_attn.v_proj", + ".attn_ln": ".self_attn_layer_norm", + ".attn.out": ".self_attn.out_proj", + ".cross_attn.query": ".encoder_attn.q_proj", + ".cross_attn.key": ".encoder_attn.k_proj", + ".cross_attn.value": ".encoder_attn.v_proj", + ".cross_attn_ln": ".encoder_attn_layer_norm", + ".cross_attn.out": ".encoder_attn.out_proj", + "decoder.ln.": "decoder.layer_norm.", + "encoder.ln.": "encoder.layer_norm.", + "token_embedding": "embed_tokens", + "encoder.positional_embedding": "encoder.embed_positions.weight", + "decoder.positional_embedding": "decoder.embed_positions.weight", + "ln_post": "layer_norm", +} + + +def rename_keys(s_dict): + keys = list(s_dict.keys()) + for key in keys: + new_key = key + for k, v in WHISPER_MAPPING.items(): + if k in key: + new_key = new_key.replace(k, v) + + print(f"{key} -> {new_key}") + + s_dict[new_key] = s_dict.pop(key) + return s_dict + + +def make_linear_from_emb(emb): + vocab_size, emb_size = emb.weight.shape + lin_layer = nn.Linear(vocab_size, emb_size, bias=False) + lin_layer.weight.data = emb.weight.data + return lin_layer + + +def _download(url: str, root: str) -> Any: + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + model_bytes = open(download_target, "rb").read() + if insecure_hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return torch.load(io.BytesIO(model_bytes)) + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm( + total=int(source.info().get("Content-Length")), ncols=80, unit="iB", unit_scale=True, unit_divisor=1024 + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = open(download_target, "rb").read() + if insecure_hashlib.sha256(model_bytes).hexdigest() != expected_sha256: + raise RuntimeError( + "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." + ) + + return torch.load(io.BytesIO(model_bytes)) + + +def convert_openai_whisper_to_tfms( + checkpoint_path, pytorch_dump_folder_path +) -> Tuple[WhisperForConditionalGeneration, bool, int]: + if ".pt" not in checkpoint_path: + root = os.path.dirname(pytorch_dump_folder_path) or "." + original_checkpoint = _download(_MODELS[checkpoint_path], root) + openai_version = checkpoint_path + else: + original_checkpoint = torch.load(checkpoint_path, map_location="cpu") + openai_version = None + + dimensions = original_checkpoint["dims"] + state_dict = original_checkpoint["model_state_dict"] + proj_out_weights = state_dict["decoder.token_embedding.weight"] + remove_ignore_keys_(state_dict) + rename_keys(state_dict) + tie_embeds = True + ffn_dim = state_dict["decoder.layers.0.fc1.weight"].shape[0] + + # a hacky way to properly set up the bos/eos/pad token ids in the model + endoftext_id = 50257 if dimensions["n_vocab"] > 51865 else 50256 + + config = WhisperConfig( + vocab_size=dimensions["n_vocab"], + encoder_ffn_dim=ffn_dim, + decoder_ffn_dim=ffn_dim, + num_mel_bins=dimensions["n_mels"], + d_model=dimensions["n_audio_state"], + max_target_positions=dimensions["n_text_ctx"], + encoder_layers=dimensions["n_audio_layer"], + encoder_attention_heads=dimensions["n_audio_head"], + decoder_layers=dimensions["n_text_layer"], + decoder_attention_heads=dimensions["n_text_head"], + max_source_positions=dimensions["n_audio_ctx"], + eos_token_id=endoftext_id, + bos_token_id=endoftext_id, + pad_token_id=endoftext_id, + decoder_start_token_id=endoftext_id + 1, + ) + + model = WhisperForConditionalGeneration(config) + missing, unexpected = model.model.load_state_dict(state_dict, strict=False) + if len(missing) > 0 and not set(missing) <= { + "encoder.embed_positions.weights", + "decoder.embed_positions.weights", + }: + raise ValueError( + "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing," + f" but all the following weights are missing {missing}" + ) + + if tie_embeds: + model.proj_out = make_linear_from_emb(model.model.decoder.embed_tokens) + else: + model.proj_out.weight.data = proj_out_weights + + # determine those parameters from a model checkpoint as Whisper repo does + is_multilingual = model.config.vocab_size >= 51865 + num_languages = model.config.vocab_size - 51765 - int(is_multilingual) + + model.generation_config = _get_generation_config( + is_multilingual, + num_languages, + openai_version, + ) + + return model, is_multilingual, num_languages + + +# Adapted from https://github.com/openai/tiktoken/issues/60#issuecomment-1499977960 +def _bpe(mergeable_ranks, token: bytes, max_rank=None) -> List[bytes]: + parts = [bytes([b]) for b in token] + while True: + min_idx = None + min_rank = None + for i, pair in enumerate(zip(parts[:-1], parts[1:])): + rank = mergeable_ranks.get(pair[0] + pair[1]) + if rank is not None and (min_rank is None or rank < min_rank): + min_idx = i + min_rank = rank + if min_rank is None or (max_rank is not None and min_rank >= max_rank): + break + assert min_idx is not None + parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2 :] + return parts + + +def convert_tiktoken_bpe_to_hf(tiktoken_url: str): + bpe_ranks = load_tiktoken_bpe(tiktoken_url) + byte_encoder = bytes_to_unicode() + + def token_bytes_to_string(b): + return "".join([byte_encoder[ord(char)] for char in b.decode("latin-1")]) + + merges = [] + vocab = {} + for token, rank in bpe_ranks.items(): + vocab[token_bytes_to_string(token)] = rank + if len(token) == 1: + continue + merged = tuple(_bpe(bpe_ranks, token, max_rank=rank)) + if len(merged) == 2: # account for empty token + merges.append(" ".join(map(token_bytes_to_string, merged))) + return vocab, merges + + +def convert_tiktoken_to_hf( + multilingual: bool = True, num_languages: int = 100, time_precision=0.02 +) -> WhisperTokenizer: + # requires whisper, unless we use the path to the tiktoken file + tiktoken_tokenizer_path = _TOKENIZERS["multilingual" if multilingual else "english"] + start_of_transcript = ["<|endoftext|>", "<|startoftranscript|>"] + control_tokens = [ + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + ] + # these are special tokens, not normalized + language_tokens = [f"<|{k}|>" for k in list(LANGUAGES)[:num_languages]] + # These are not special but normalized + timestamp_tokens = [("<|%.2f|>" % (i * time_precision)) for i in range(1500 + 1)] + + vocab, merges = convert_tiktoken_bpe_to_hf(tiktoken_tokenizer_path) + + with tempfile.TemporaryDirectory() as tmpdirname: + vocab_file = f"{tmpdirname}/vocab.json" + merge_file = f"{tmpdirname}/merges.txt" + with open(vocab_file, "w", encoding="utf-8") as f: + f.write(json.dumps(vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n") + + with open(merge_file, "w", encoding="utf-8") as writer: + writer.write("#version: 0.2\n") + for bpe_tokens in merges: + writer.write(bpe_tokens + "\n") + + hf_tokenizer = WhisperTokenizer(vocab_file, merge_file) + + hf_tokenizer.add_tokens(start_of_transcript + language_tokens + control_tokens, special_tokens=True) + hf_tokenizer.add_tokens(timestamp_tokens, special_tokens=False) + return hf_tokenizer + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # # Required parameters + parser.add_argument("--checkpoint_path", type=str, help="Path to the downloaded checkpoints") + parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") + parser.add_argument( + "--convert_preprocessor", + type=bool, + default=False, + help="Whether or not the preprocessor (tokenizer + feature extractor) should be converted along with the model.", + ) + args = parser.parse_args() + + model, is_multilingual, num_languages = convert_openai_whisper_to_tfms( + args.checkpoint_path, args.pytorch_dump_folder_path + ) + + if args.convert_preprocessor: + try: + if not _is_package_available("tiktoken"): + raise ModuleNotFoundError( + """`tiktoken` is not installed, use `pip install tiktoken` to convert the tokenizer""" + ) + except Exception as e: + print(e) + else: + from tiktoken.load import load_tiktoken_bpe + + tokenizer = convert_tiktoken_to_hf(is_multilingual, num_languages) + feature_extractor = WhisperFeatureExtractor( + feature_size=model.config.num_mel_bins, + # the rest of default parameters are the same as hardcoded in openai/whisper + ) + processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) + processor.save_pretrained(args.pytorch_dump_folder_path) + + # save fast tokenizer as well + fast_tokenizer = WhisperTokenizerFast.from_pretrained(args.pytorch_dump_folder_path) + fast_tokenizer.save_pretrained(args.pytorch_dump_folder_path, legacy_format=False) + + model.save_pretrained(args.pytorch_dump_folder_path) diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/english_normalizer.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/english_normalizer.py new file mode 100644 index 0000000000000000000000000000000000000000..7f6aab4ad29d848f5fe0e237dd7fa8b5e76fa5e0 --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/english_normalizer.py @@ -0,0 +1,595 @@ +# Copyright 2022 The OpenAI team and The HuggingFace Team. All rights reserved. +# Most of the code is copy pasted from the original whisper repository +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +import unicodedata +from fractions import Fraction +from typing import Iterator, List, Match, Optional, Union + +import regex + + +# non-ASCII letters that are not separated by "NFKD" normalization +ADDITIONAL_DIACRITICS = { + "œ": "oe", + "Œ": "OE", + "ø": "o", + "Ø": "O", + "æ": "ae", + "Æ": "AE", + "ß": "ss", + "ẞ": "SS", + "đ": "d", + "Đ": "D", + "ð": "d", + "Ð": "D", + "þ": "th", + "Þ": "th", + "ł": "l", + "Ł": "L", +} + + +def remove_symbols_and_diacritics(s: str, keep=""): + """ + Replace any other markers, symbols, and punctuations with a space, and drop any diacritics (category 'Mn' and some + manual mappings) + """ + + def replace_character(char): + if char in keep: + return char + elif char in ADDITIONAL_DIACRITICS: + return ADDITIONAL_DIACRITICS[char] + + elif unicodedata.category(char) == "Mn": + return "" + + elif unicodedata.category(char)[0] in "MSP": + return " " + + return char + + return "".join(replace_character(c) for c in unicodedata.normalize("NFKD", s)) + + +def remove_symbols(s: str): + """ + Replace any other markers, symbols, punctuations with a space, keeping diacritics + """ + return "".join(" " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)) + + +class BasicTextNormalizer: + def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): + self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols + self.split_letters = split_letters + + def __call__(self, s: str): + s = s.lower() + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = self.clean(s).lower() + + if self.split_letters: + s = " ".join(regex.findall(r"\X", s, regex.U)) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + + return s + + +class EnglishNumberNormalizer: + """ + Convert any spelled-out numbers into arabic numbers, while handling: + + - remove any commas + - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. + - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` + - spell out `one` and `ones` + - interpret successive single-digit numbers as nominal: `one oh one` -> `101` + """ + + def __init__(self): + super().__init__() + + self.zeros = {"o", "oh", "zero"} + # fmt: off + self.ones = { + name: i + for i, name in enumerate( + ["one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen"], + start=1, + ) + } + # fmt: on + self.ones_plural = { + "sixes" if name == "six" else name + "s": (value, "s") for name, value in self.ones.items() + } + self.ones_ordinal = { + "zeroth": (0, "th"), + "first": (1, "st"), + "second": (2, "nd"), + "third": (3, "rd"), + "fifth": (5, "th"), + "twelfth": (12, "th"), + **{ + name + ("h" if name.endswith("t") else "th"): (value, "th") + for name, value in self.ones.items() + if value > 3 and value != 5 and value != 12 + }, + } + self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} + + self.tens = { + "twenty": 20, + "thirty": 30, + "forty": 40, + "fifty": 50, + "sixty": 60, + "seventy": 70, + "eighty": 80, + "ninety": 90, + } + self.tens_plural = {name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()} + self.tens_ordinal = {name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()} + self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} + + self.multipliers = { + "hundred": 100, + "thousand": 1_000, + "million": 1_000_000, + "billion": 1_000_000_000, + "trillion": 1_000_000_000_000, + "quadrillion": 1_000_000_000_000_000, + "quintillion": 1_000_000_000_000_000_000, + "sextillion": 1_000_000_000_000_000_000_000, + "septillion": 1_000_000_000_000_000_000_000_000, + "octillion": 1_000_000_000_000_000_000_000_000_000, + "nonillion": 1_000_000_000_000_000_000_000_000_000_000, + "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, + } + self.multipliers_plural = {name + "s": (value, "s") for name, value in self.multipliers.items()} + self.multipliers_ordinal = {name + "th": (value, "th") for name, value in self.multipliers.items()} + self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal} + self.decimals = {*self.ones, *self.tens, *self.zeros} + + self.preceding_prefixers = { + "minus": "-", + "negative": "-", + "plus": "+", + "positive": "+", + } + self.following_prefixers = { + "pound": "£", + "pounds": "£", + "euro": "€", + "euros": "€", + "dollar": "$", + "dollars": "$", + "cent": "¢", + "cents": "¢", + } + self.prefixes = set(list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())) + self.suffixers = { + "per": {"cent": "%"}, + "percent": "%", + } + self.specials = {"and", "double", "triple", "point"} + + self.words = { + key + for mapping in [ + self.zeros, + self.ones, + self.ones_suffixed, + self.tens, + self.tens_suffixed, + self.multipliers, + self.multipliers_suffixed, + self.preceding_prefixers, + self.following_prefixers, + self.suffixers, + self.specials, + ] + for key in mapping + } + self.literal_words = {"one", "ones"} + + def process_words(self, words: List[str]) -> Iterator[str]: + prefix: Optional[str] = None + value: Optional[Union[str, int]] = None + skip = False + + def to_fraction(s: str): + try: + return Fraction(s) + except ValueError: + return None + + def output(result: Union[str, int]): + nonlocal prefix, value + result = str(result) + if prefix is not None: + result = prefix + result + value = None + prefix = None + return result + + if len(words) == 0: + return + + for i, current in enumerate(words): + prev = words[i - 1] if i != 0 else None + next = words[i + 1] if i != len(words) - 1 else None + if skip: + skip = False + continue + + next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) + has_prefix = current[0] in self.prefixes + current_without_prefix = current[1:] if has_prefix else current + if re.match(r"^\d+(\.\d+)?$", current_without_prefix): + # arabic numbers (potentially with signs and fractions) + f = to_fraction(current_without_prefix) + if f is None: + raise ValueError("Converting the fraction failed") + + if value is not None: + if isinstance(value, str) and value.endswith("."): + # concatenate decimals / ip address components + value = str(value) + str(current) + continue + else: + yield output(value) + + prefix = current[0] if has_prefix else prefix + if f.denominator == 1: + value = f.numerator # store integers as int + else: + value = current_without_prefix + elif current not in self.words: + # non-numeric words + if value is not None: + yield output(value) + yield output(current) + elif current in self.zeros: + value = str(value or "") + "0" + elif current in self.ones: + ones = self.ones[current] + + if value is None: + value = ones + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: # replace the last zero with the digit + value = value[:-1] + str(ones) + else: + value = str(value) + str(ones) + elif ones < 10: + if value % 10 == 0: + value += ones + else: + value = str(value) + str(ones) + else: # eleven to nineteen + if value % 100 == 0: + value += ones + else: + value = str(value) + str(ones) + elif current in self.ones_suffixed: + # ordinal or cardinal; yield the number right away + ones, suffix = self.ones_suffixed[current] + if value is None: + yield output(str(ones) + suffix) + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: + yield output(value[:-1] + str(ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + elif ones < 10: + if value % 10 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + else: # eleven to nineteen + if value % 100 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + value = None + elif current in self.tens: + tens = self.tens[current] + if value is None: + value = tens + elif isinstance(value, str): + value = str(value) + str(tens) + else: + if value % 100 == 0: + value += tens + else: + value = str(value) + str(tens) + elif current in self.tens_suffixed: + # ordinal or cardinal; yield the number right away + tens, suffix = self.tens_suffixed[current] + if value is None: + yield output(str(tens) + suffix) + elif isinstance(value, str): + yield output(str(value) + str(tens) + suffix) + else: + if value % 100 == 0: + yield output(str(value + tens) + suffix) + else: + yield output(str(value) + str(tens) + suffix) + elif current in self.multipliers: + multiplier = self.multipliers[current] + if value is None: + value = multiplier + elif isinstance(value, str) or value == 0: + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + value = p.numerator + else: + yield output(value) + value = multiplier + else: + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + elif current in self.multipliers_suffixed: + multiplier, suffix = self.multipliers_suffixed[current] + if value is None: + yield output(str(multiplier) + suffix) + elif isinstance(value, str): + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + yield output(str(p.numerator) + suffix) + else: + yield output(value) + yield output(str(multiplier) + suffix) + else: # int + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + yield output(str(value) + suffix) + value = None + elif current in self.preceding_prefixers: + # apply prefix (positive, minus, etc.) if it precedes a number + if value is not None: + yield output(value) + + if next in self.words or next_is_numeric: + prefix = self.preceding_prefixers[current] + else: + yield output(current) + elif current in self.following_prefixers: + # apply prefix (dollars, cents, etc.) only after a number + if value is not None: + prefix = self.following_prefixers[current] + yield output(value) + else: + yield output(current) + elif current in self.suffixers: + # apply suffix symbols (percent -> '%') + if value is not None: + suffix = self.suffixers[current] + if isinstance(suffix, dict): + if next in suffix: + yield output(str(value) + suffix[next]) + skip = True + else: + yield output(value) + yield output(current) + else: + yield output(str(value) + suffix) + else: + yield output(current) + elif current in self.specials: + if next not in self.words and not next_is_numeric: + # apply special handling only if the next word can be numeric + if value is not None: + yield output(value) + yield output(current) + elif current == "and": + # ignore "and" after hundreds, thousands, etc. + if prev not in self.multipliers: + if value is not None: + yield output(value) + yield output(current) + elif current == "double" or current == "triple": + if next in self.ones or next in self.zeros: + repeats = 2 if current == "double" else 3 + ones = self.ones.get(next, 0) + value = str(value or "") + str(ones) * repeats + skip = True + else: + if value is not None: + yield output(value) + yield output(current) + elif current == "point": + if next in self.decimals or next_is_numeric: + value = str(value or "") + "." + else: + # should all have been covered at this point + raise ValueError(f"Unexpected token: {current}") + else: + # all should have been covered at this point + raise ValueError(f"Unexpected token: {current}") + + if value is not None: + yield output(value) + + def preprocess(self, s: str): + # replace " and a half" with " point five" + results = [] + + segments = re.split(r"\band\s+a\s+half\b", s) + for i, segment in enumerate(segments): + if len(segment.strip()) == 0: + continue + if i == len(segments) - 1: + results.append(segment) + else: + results.append(segment) + last_word = segment.rsplit(maxsplit=2)[-1] + if last_word in self.decimals or last_word in self.multipliers: + results.append("point five") + else: + results.append("and a half") + + s = " ".join(results) + + # put a space at number/letter boundary + s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) + s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) + + # but remove spaces which could be a suffix + s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) + + return s + + def postprocess(self, s: str): + def combine_cents(m: Match): + try: + currency = m.group(1) + integer = m.group(2) + cents = int(m.group(3)) + return f"{currency}{integer}.{cents:02d}" + except ValueError: + return m.string + + def extract_cents(m: Match): + try: + return f"¢{int(m.group(1))}" + except ValueError: + return m.string + + # apply currency postprocessing; "$2 and ¢7" -> "$2.07" + s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) + s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) + + # write "one(s)" instead of "1(s)", just for the readability + s = re.sub(r"\b1(s?)\b", r"one\1", s) + + return s + + def __call__(self, s: str): + s = self.preprocess(s) + s = " ".join(word for word in self.process_words(s.split()) if word is not None) + s = self.postprocess(s) + + return s + + +class EnglishSpellingNormalizer: + """ + Applies British-American spelling mappings as listed in [1]. + + [1] https://www.tysto.com/uk-us-spelling-list.html + """ + + def __init__(self, english_spelling_mapping): + self.mapping = english_spelling_mapping + + def __call__(self, s: str): + return " ".join(self.mapping.get(word, word) for word in s.split()) + + +class EnglishTextNormalizer: + def __init__(self, english_spelling_mapping): + self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" + self.replacers = { + # common contractions + r"\bwon't\b": "will not", + r"\bcan't\b": "can not", + r"\blet's\b": "let us", + r"\bain't\b": "aint", + r"\by'all\b": "you all", + r"\bwanna\b": "want to", + r"\bgotta\b": "got to", + r"\bgonna\b": "going to", + r"\bi'ma\b": "i am going to", + r"\bimma\b": "i am going to", + r"\bwoulda\b": "would have", + r"\bcoulda\b": "could have", + r"\bshoulda\b": "should have", + r"\bma'am\b": "madam", + # contractions in titles/prefixes + r"\bmr\b": "mister ", + r"\bmrs\b": "missus ", + r"\bst\b": "saint ", + r"\bdr\b": "doctor ", + r"\bprof\b": "professor ", + r"\bcapt\b": "captain ", + r"\bgov\b": "governor ", + r"\bald\b": "alderman ", + r"\bgen\b": "general ", + r"\bsen\b": "senator ", + r"\brep\b": "representative ", + r"\bpres\b": "president ", + r"\brev\b": "reverend ", + r"\bhon\b": "honorable ", + r"\basst\b": "assistant ", + r"\bassoc\b": "associate ", + r"\blt\b": "lieutenant ", + r"\bcol\b": "colonel ", + r"\bjr\b": "junior ", + r"\bsr\b": "senior ", + r"\besq\b": "esquire ", + # prefect tenses, ideally it should be any past participles, but it's harder.. + r"'d been\b": " had been", + r"'s been\b": " has been", + r"'d gone\b": " had gone", + r"'s gone\b": " has gone", + r"'d done\b": " had done", # "'s done" is ambiguous + r"'s got\b": " has got", + # general contractions + r"n't\b": " not", + r"'re\b": " are", + r"'s\b": " is", + r"'d\b": " would", + r"'ll\b": " will", + r"'t\b": " not", + r"'ve\b": " have", + r"'m\b": " am", + } + self.standardize_numbers = EnglishNumberNormalizer() + self.standardize_spellings = EnglishSpellingNormalizer(english_spelling_mapping) + + def __call__(self, s: str): + s = s.lower() + + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = re.sub(self.ignore_patterns, "", s) + s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe + + for pattern, replacement in self.replacers.items(): + s = re.sub(pattern, replacement, s) + + s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits + s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers + s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics + + s = self.standardize_numbers(s) + s = self.standardize_spellings(s) + + # now remove prefix/suffix symbols that are not preceded/followed by numbers + s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) + s = re.sub(r"([^0-9])%", r"\1 ", s) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + + return s diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/feature_extraction_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/feature_extraction_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..508e85b91ffd2d6be5e7b063c7705335e1d28374 --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/feature_extraction_whisper.py @@ -0,0 +1,315 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Feature extractor class for Whisper +""" +from typing import List, Optional, Union + +import numpy as np + +from ... import is_torch_available +from ...audio_utils import mel_filter_bank, spectrogram, window_function +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...utils import TensorType, logging + + +if is_torch_available(): + import torch + +logger = logging.get_logger(__name__) + + +class WhisperFeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a Whisper feature extractor. + + This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains + most of the main methods. Users should refer to this superclass for more information regarding those methods. + + This class extracts mel-filter bank features from raw speech using a custom numpy implementation of the `Short Time + Fourier Transform` which should match pytorch's `torch.stft` equivalent. + + Args: + feature_size (`int`, defaults to 80): + The feature dimension of the extracted features. + sampling_rate (`int`, defaults to 16000): + The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). + hop_length (`int`, defaults to 160): + Length of the overlaping windows for the STFT used to obtain the Mel Frequency coefficients. + chunk_length (`int`, defaults to 30): + The maximum number of chuncks of `sampling_rate` samples used to trim and pad longer or shorter audio + sequences. + n_fft (`int`, defaults to 400): + Size of the Fourier transform. + padding_value (`float`, *optional*, defaults to 0.0): + Padding value used to pad the audio. Should correspond to silences. + """ + + model_input_names = ["input_features"] + + def __init__( + self, + feature_size=80, + sampling_rate=16000, + hop_length=160, + chunk_length=30, + n_fft=400, + padding_value=0.0, + return_attention_mask=False, # pad inputs to max length with silence token (zero) and no attention mask + **kwargs, + ): + super().__init__( + feature_size=feature_size, + sampling_rate=sampling_rate, + padding_value=padding_value, + return_attention_mask=return_attention_mask, + **kwargs, + ) + self.n_fft = n_fft + self.hop_length = hop_length + self.chunk_length = chunk_length + self.n_samples = chunk_length * sampling_rate + self.nb_max_frames = self.n_samples // hop_length + self.sampling_rate = sampling_rate + self.mel_filters = mel_filter_bank( + num_frequency_bins=1 + n_fft // 2, + num_mel_filters=feature_size, + min_frequency=0.0, + max_frequency=8000.0, + sampling_rate=sampling_rate, + norm="slaney", + mel_scale="slaney", + ) + + def _np_extract_fbank_features(self, waveform_batch: np.array, device: str) -> np.ndarray: + """ + Compute the log-mel spectrogram of the provided audio, gives similar results to Whisper's original torch + implementation with 1e-5 tolerance. + """ + if device != "cpu": + raise ValueError( + f"Got device `{device}` for feature extraction, but feature extraction on CUDA accelerator " + "devices requires torch, which is not installed. Either set `device='cpu'`, or " + "install torch according to the official instructions: https://pytorch.org/get-started/locally/" + ) + log_spec_batch = [] + for waveform in waveform_batch: + log_spec = spectrogram( + waveform, + window_function(self.n_fft, "hann"), + frame_length=self.n_fft, + hop_length=self.hop_length, + power=2.0, + mel_filters=self.mel_filters, + log_mel="log10", + ) + log_spec = log_spec[:, :-1] + log_spec = np.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + log_spec_batch.append(log_spec) + log_spec_batch = np.array(log_spec_batch) + return log_spec_batch + + def _torch_extract_fbank_features(self, waveform: np.array, device: str = "cpu") -> np.ndarray: + """ + Compute the log-mel spectrogram of the audio using PyTorch's GPU-accelerated STFT implementation with batching, + yielding results similar to cpu computing with 1e-5 tolerance. + """ + waveform = torch.from_numpy(waveform).type(torch.float32) + + window = torch.hann_window(self.n_fft) + if device != "cpu": + waveform = waveform.to(device) + window = window.to(device) + stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + + mel_filters = torch.from_numpy(self.mel_filters).type(torch.float32) + if device != "cpu": + mel_filters = mel_filters.to(device) + mel_spec = mel_filters.T @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + if waveform.dim() == 2: + max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0] + log_spec = torch.maximum(log_spec, max_val - 8.0) + else: + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + if device != "cpu": + log_spec = log_spec.detach().cpu() + return log_spec.numpy() + + @staticmethod + # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm + def zero_mean_unit_var_norm( + input_values: List[np.ndarray], attention_mask: List[np.ndarray], padding_value: float = 0.0 + ) -> List[np.ndarray]: + """ + Every array in the list is normalized to have zero mean and unit variance + """ + if attention_mask is not None: + attention_mask = np.array(attention_mask, np.int32) + normed_input_values = [] + + for vector, length in zip(input_values, attention_mask.sum(-1)): + normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7) + if length < normed_slice.shape[0]: + normed_slice[length:] = padding_value + + normed_input_values.append(normed_slice) + else: + normed_input_values = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values] + + return normed_input_values + + def __call__( + self, + raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], + truncation: bool = True, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_attention_mask: Optional[bool] = None, + padding: Optional[str] = "max_length", + max_length: Optional[int] = None, + sampling_rate: Optional[int] = None, + do_normalize: Optional[bool] = None, + device: Optional[str] = "cpu", + **kwargs, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model one or several sequence(s). Implementation uses PyTorch for + the STFT computation if available, otherwise a slower NumPy based one. + + Args: + raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): + The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float + values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not + stereo, i.e. single float per timestep. + truncation (`bool`, *optional*, default to `True`): + Activates truncation to cut input sequences longer than *max_length* to *max_length*. + pad_to_multiple_of (`int`, *optional*, defaults to None): + If set will pad the sequence to a multiple of the provided value. + + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability + `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific feature_extractor's default. + + [What are attention masks?](../glossary#attention-mask) + + + + For Whisper models, `attention_mask` should always be passed for batched inference, to avoid subtle + bugs. + + + + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + sampling_rate (`int`, *optional*): + The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition + pipeline. + padding_value (`float`, defaults to 0.0): + The value that is used to fill the padding values / vectors. + do_normalize (`bool`, *optional*, defaults to `False`): + Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly + improve the performance of the model. + device (`str`, *optional*, defaults to `'cpu'`): + Specifies the device for computation of the log-mel spectrogram of audio signals in the + `_torch_extract_fbank_features` method. (e.g., "cpu", "cuda") + """ + + if sampling_rate is not None: + if sampling_rate != self.sampling_rate: + raise ValueError( + f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a" + f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input" + f" was sampled with {self.sampling_rate} and not {sampling_rate}." + ) + else: + logger.warning( + "It is strongly recommended to pass the `sampling_rate` argument to this function. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 + if is_batched_numpy and len(raw_speech.shape) > 2: + raise ValueError(f"Only mono-channel audio is supported for input to {self}") + is_batched = is_batched_numpy or ( + isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list))) + ) + + if is_batched: + raw_speech = [np.asarray([speech], dtype=np.float32).T for speech in raw_speech] + elif not is_batched and not isinstance(raw_speech, np.ndarray): + raw_speech = np.asarray(raw_speech, dtype=np.float32) + elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64): + raw_speech = raw_speech.astype(np.float32) + + # always return batch + if not is_batched: + raw_speech = [np.asarray([raw_speech]).T] + + batched_speech = BatchFeature({"input_features": raw_speech}) + + # convert into correct format for padding + + padded_inputs = self.pad( + batched_speech, + padding=padding, + max_length=max_length if max_length else self.n_samples, + truncation=truncation, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask or do_normalize, + ) + + # zero-mean and unit-variance normalization + if do_normalize: + padded_inputs["input_features"] = self.zero_mean_unit_var_norm( + padded_inputs["input_features"], + attention_mask=padded_inputs["attention_mask"], + padding_value=self.padding_value, + ) + padded_inputs["input_features"] = np.stack(padded_inputs["input_features"], axis=0) + + # make sure list is in array format + input_features = padded_inputs.get("input_features").transpose(2, 0, 1) + + extract_fbank_features = ( + self._torch_extract_fbank_features if is_torch_available() else self._np_extract_fbank_features + ) + input_features = extract_fbank_features(input_features[0], device) + + if isinstance(input_features[0], List): + padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features] + else: + padded_inputs["input_features"] = input_features + + if return_attention_mask: + # rescale from sample (48000) to feature (3000) + padded_inputs["attention_mask"] = padded_inputs["attention_mask"][:, :: self.hop_length] + + if return_tensors is not None: + padded_inputs = padded_inputs.convert_to_tensors(return_tensors) + + return padded_inputs diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/generation_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/generation_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..b3865140f24ee4bd1cedaf7090913435ab2a7fbe --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/generation_whisper.py @@ -0,0 +1,1622 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import copy +import math +import warnings +import zlib +from typing import Callable, Iterator, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from ...generation.configuration_utils import GenerationConfig +from ...generation.logits_process import ( + LogitsProcessorList, + SuppressTokensAtBeginLogitsProcessor, + SuppressTokensLogitsProcessor, + WhisperNoSpeechDetection, + WhisperTimeStampLogitsProcessor, +) +from ...generation.stopping_criteria import StoppingCriteriaList +from ...modeling_outputs import BaseModelOutput +from ...utils import logging +from .tokenization_whisper import TASK_IDS, TO_LANGUAGE_CODE + + +logger = logging.get_logger(__name__) + + +def _median_filter(inputs: torch.Tensor, filter_width: int) -> torch.Tensor: + """ + Applies a median filter of width `filter_width` along the last dimension of the input. + + The `inputs` tensor is assumed to be 3- or 4-dimensional. + """ + if filter_width <= 0 or filter_width % 2 != 1: + raise ValueError("`filter_width` should be an odd number") + + pad_width = filter_width // 2 + if inputs.shape[-1] <= pad_width: + return inputs + + # Pad the left and right edges. + inputs = nn.functional.pad(inputs, (pad_width, pad_width, 0, 0), mode="reflect") + + # sort() is faster than torch.median (https://github.com/pytorch/pytorch/issues/51450) + result = inputs.unfold(-1, filter_width, 1).sort()[0][..., pad_width] + return result + + +def _dynamic_time_warping(matrix: np.ndarray): + """ + Measures similarity between two temporal sequences: the input audio and the output tokens. Used to generate + token-level timestamps. + """ + output_length, input_length = matrix.shape + cost = np.ones((output_length + 1, input_length + 1), dtype=np.float32) * np.inf + trace = -np.ones((output_length + 1, input_length + 1), dtype=np.float32) + + cost[0, 0] = 0 + for j in range(1, input_length + 1): + for i in range(1, output_length + 1): + c0 = cost[i - 1, j - 1] + c1 = cost[i - 1, j] + c2 = cost[i, j - 1] + + if c0 < c1 and c0 < c2: + c, t = c0, 0 + elif c1 < c0 and c1 < c2: + c, t = c1, 1 + else: + c, t = c2, 2 + + cost[i, j] = matrix[i - 1, j - 1] + c + trace[i, j] = t + + # backtrace + i = trace.shape[0] - 1 + j = trace.shape[1] - 1 + trace[0, :] = 2 + trace[:, 0] = 1 + + text_indices = [] + time_indices = [] + while i > 0 or j > 0: + text_indices.append(i - 1) + time_indices.append(j - 1) + if trace[i, j] == 0: + i -= 1 + j -= 1 + elif trace[i, j] == 1: + i -= 1 + elif trace[i, j] == 2: + j -= 1 + else: + raise RuntimeError( + f"Internal error in dynamic time warping. Unexpected trace[{i}, {j}]. Please file a bug report." + ) + + text_indices = np.array(text_indices)[::-1] + time_indices = np.array(time_indices)[::-1] + return text_indices, time_indices + + +def _get_attr_from_logit_processors(logits_processor, logit_processor_class, attribute_name): + logit_processor = next((cls for cls in logits_processor if isinstance(cls, logit_processor_class)), None) + if logit_processor: + return getattr(logit_processor, attribute_name, None) + return None + + +def _pad_to_max_length(current_segments, pad_token_id, padding="right", bos_token_tensor=None, cut_off_length=None): + max_total_length = 0 + sequences = [] + if padding not in ["right", "left"]: + raise ValueError(f"`padding` must be either 'right' or 'left', not {padding}") + + for current_segment_list in current_segments: + if current_segment_list is not None and len([d["tokens"] for d in current_segment_list]) > 0: + sequence = torch.cat([d["tokens"] for d in current_segment_list], dim=-1) + + if cut_off_length is not None: + sequence = sequence[-cut_off_length:] + + if bos_token_tensor is not None: + sequence = torch.cat([bos_token_tensor, sequence]) + + sequences.append(sequence) + max_total_length = max(max_total_length, len(sequences[-1])) + elif bos_token_tensor is not None: + sequences.append(bos_token_tensor) + else: + sequences.append(torch.tensor([])) + + for i in range(len(current_segments)): + pad_length = max_total_length - len(sequences[i]) + pad = (0, pad_length) if padding == "right" else (pad_length, 0) + sequences[i] = F.pad(sequences[i], pad=pad, value=pad_token_id) + + sequences = torch.stack(sequences, dim=0) + return sequences + + +class WhisperGenerationMixin: + def _extract_token_timestamps(self, generate_outputs, alignment_heads, time_precision=0.02, num_frames=None): + """ + Calculates token-level timestamps using the encoder-decoder cross-attentions and dynamic time-warping (DTW) to + map each output token to a position in the input audio. If `num_frames` is specified, the encoder-decoder + cross-attentions will be cropped before applying DTW. + + Returns: + tensor containing the timestamps in seconds for each predicted token + """ + # Create a list with `decoder_layers` elements, each a tensor of shape + # (batch size, attention_heads, output length, input length). + cross_attentions = [] + for i in range(self.config.decoder_layers): + cross_attentions.append(torch.cat([x[i] for x in generate_outputs.cross_attentions], dim=2)) + + # Select specific cross-attention layers and heads. This is a tensor + # of shape (batch size, num selected, output length, input length). + weights = torch.stack([cross_attentions[l][:, h] for l, h in alignment_heads]) + weights = weights.permute([1, 0, 2, 3]) + + weight_length = None + + if "beam_indices" in generate_outputs: + # If beam search has been used, the output sequences may have been generated for more timesteps than their sequence_lengths + # since the beam search strategy chooses the most probable sequences at the end of the search. + # In that case, the cross_attentions weights are too long and we have to make sure that they have the right output_length + weight_length = (generate_outputs.beam_indices != -1).sum(-1).max() + weights = weights[:, :, :weight_length] + + # If beam index is still -1, it means that the associated token id is EOS + # We need to replace the index with 0 since index_select gives an error if any of the indexes is -1. + beam_indices = generate_outputs.beam_indices[:, :weight_length] + beam_indices = beam_indices.masked_fill(beam_indices == -1, 0) + + # Select the cross attention from the right beam for each output sequences + weights = torch.stack( + [ + torch.index_select(weights[:, :, i, :], dim=0, index=beam_indices[:, i]) + for i in range(beam_indices.shape[1]) + ], + dim=2, + ) + + # make sure timestamps are as long as weights + input_length = weight_length or cross_attentions[0].shape[2] + timestamps = torch.zeros_like(generate_outputs.sequences, dtype=torch.float32)[:, : input_length + 1] + batch_size = timestamps.shape[0] + + if num_frames is not None: + # two cases: + # 1. num_frames is the same for each sample -> compute the DTW matrix for each sample in parallel + # 2. num_frames is different, compute the DTW matrix for each sample sequentially + + # we're using np.unique because num_frames can be int/list/tuple + if len(np.unique(num_frames)) == 1: + # if num_frames is the same, no need to recompute matrix, std and mean for each element of the batch + num_frames = num_frames if isinstance(num_frames, int) else num_frames[0] + + weights = weights[..., : num_frames // 2] + else: + # num_frames is of shape (batch_size,) whereas batch_size is truely batch_size*num_return_sequences + repeat_time = batch_size if isinstance(num_frames, int) else batch_size // len(num_frames) + num_frames = np.repeat(num_frames, repeat_time) + + if num_frames is None or isinstance(num_frames, int): + # Normalize and smoothen the weights. + std = torch.std(weights, dim=-2, keepdim=True, unbiased=False) + mean = torch.mean(weights, dim=-2, keepdim=True) + weights = (weights - mean) / std + weights = _median_filter(weights, self.config.median_filter_width) + + # Average the different cross-attention heads. + weights = weights.mean(dim=1) + + # Perform dynamic time warping on each element of the batch. + for batch_idx in range(batch_size): + if num_frames is not None and isinstance(num_frames, (tuple, list, np.ndarray)): + matrix = weights[batch_idx, ..., : num_frames[batch_idx] // 2] + + # Normalize and smoothen the weights. + std = torch.std(matrix, dim=-2, keepdim=True, unbiased=False) + mean = torch.mean(matrix, dim=-2, keepdim=True) + matrix = (matrix - mean) / std + matrix = _median_filter(matrix, self.config.median_filter_width) + + # Average the different cross-attention heads. + matrix = matrix.mean(dim=0) + else: + matrix = weights[batch_idx] + + text_indices, time_indices = _dynamic_time_warping(-matrix.cpu().double().numpy()) + jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) + jump_times = time_indices[jumps] * time_precision + timestamps[batch_idx, 1:] = torch.tensor(jump_times) + + return timestamps + + def generate( + self, + input_features: Optional[torch.Tensor] = None, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + synced_gpus: bool = False, + return_timestamps: Optional[bool] = None, + task: Optional[str] = None, + language: Optional[str] = None, + is_multilingual: Optional[bool] = None, + prompt_ids: Optional[torch.Tensor] = None, + prompt_condition_type: Optional[str] = None, # first-segment, all-segments + condition_on_prev_tokens: Optional[bool] = None, + temperature: Optional[Union[float, Tuple[float, ...]]] = None, + compression_ratio_threshold: Optional[float] = None, + logprob_threshold: Optional[float] = None, + no_speech_threshold: Optional[float] = None, + num_segment_frames: Optional[int] = None, + attention_mask: Optional[torch.Tensor] = None, + time_precision: float = 0.02, + return_token_timestamps: Optional[bool] = None, + return_segments: bool = False, + return_dict_in_generate: Optional[bool] = None, + **kwargs, + ): + """ + Transcribes or translates log-mel input features to a sequence of auto-regressively generated token ids. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Parameters: + input_features (`torch.Tensor` of shape `(batch_size, feature_size, sequence_length)`, *optional*): + Float values of log-mel features extracted from the raw speech waveform. The raw speech waveform can be obtained by + loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via + the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the + [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a + tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] for details. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + stopping_criteria (`StoppingCriteriaList`, *optional*): + Custom stopping criteria that complement the default stopping criteria built from arguments and a + generation config. If a stopping criteria is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*): + If provided, this function constraints the beam search to allowed tokens only at each step. If not + provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and + `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned + on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful + for constrained generation conditioned on the prefix, as described in [Autoregressive Entity + Retrieval](https://arxiv.org/abs/2010.00904). + synced_gpus (`bool`, *optional*, defaults to `False`): + Whether to continue running the while loop until max_length (needed for ZeRO stage 3) + return_timestamps (`bool`, *optional*): + Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`. + task (`str`, *optional*): + Task to use for generation, either "translate" or "transcribe". The `model.config.forced_decoder_ids` + will be updated accordingly. + language (`str`, *optional*): + Language token to use for generation, can be either in the form of `<|en|>`, `en` or `english`. You can + find all the possible language tokens in the `model.generation_config.lang_to_id` dictionary. + is_multilingual (`bool`, *optional*): + Whether or not the model is multilingual. + prompt_ids (`torch.Tensor`, *optional*): + Rank-1 tensor of token IDs created by passing text to [`~WhisperProcessor.get_prompt_ids`] that is + provided as a prompt to each chunk. This can be used to provide or "prompt-engineer" a context for + transcription, e.g. custom vocabularies or proper nouns to make it more likely to predict those words + correctly. It cannot be used in conjunction with `decoder_start_token_id` as it overwrites this value. + prompt_condition_type (`str`, *optional*): + Only relevant for long-form transcription. Condition type of `prompt_ids`. 'first-segment' means only the first segment is conditioned on `prompt_ids`. 'all-segments' means each segment is conditioned on `prompt_ids`. Make sure to enable `condition_on_prev_tokens` for 'all-segments'. + Defaults to 'first-segment'. For short-term transcription only 'first-segment' is possible. + condition_on_prev_tokens (`bool`, *optional*): + Only relevant for long-form transcription. Whether to condition each segment on the previous segment. + As shown in the [the Whisper paper](https://cdn.openai.com/papers/whisper.pdf), this can help to improve + performance. + temperature (`float` or list of `float`, *optional*): + The temperature to be used for generation. Passing a single `float` value and `do_sample=True` activates + generation using sampling. For long-form transcription, temperature fallback can be activated by passing + a list of float values such as (0.0, 0.2, 0.4, 0.6, 0.8, 1.0). As shown in the [the Whisper paper](https://cdn.openai.com/papers/whisper.pdf), this can help to improve + performance. + compression_ratio_threshold (`float`, *optional*): + Only relevant for long-form transcription. If defined, the zlib compression rate of each segment will be computed. If the compression rate of + a segment is higher than `compression_ratio_threshold`, temperature fallback is activated: the generated segment is discarded and the generation is + repeated using a higher temperature. The intuition behind this feature is that segments with very high compression rates + suffer from a lot of repetition. The unwanted repetition can be reduced by injecting more randomness by increasing the temperature. If `compression_ratio_threshold` is defined + make sure that `temperature` is a list of values. A common value for `compression_ratio_threshold` is 1.35. + As shown in the [the Whisper paper](https://cdn.openai.com/papers/whisper.pdf), this can help to improve + performance. + logprob_threshold (`float`, *optional*): + Only relevant for long-form transcription. If defined, the average log-probability of each segment will be computed. If the log-probability of + a given segment is lower than `logprob_threshold`, temperature fallback is activated: the generated segment is discarded and the generation is + repeated using a higher temperature. The intuition behind this feature is that segments of low log-probability + can be improved by injecting more randomness by increasing the temperature. If `logprob_threshold` is defined + make sure that `temperature` is a list of values. A common value for `logprob_threshold` is -1.0. + As shown in the [the Whisper paper](https://cdn.openai.com/papers/whisper.pdf), this can help to improve + performance. + no_speech_threshold (`float`, *optional*): + Only relevant for long-form transcription. If defined, the "no-speech" token combined with the `logprob_threshold` + is used to determine whether a segment contains only silence. In this case, the transcription for this segment + is skipped. + As shown in the [the Whisper paper](https://cdn.openai.com/papers/whisper.pdf), this can help to improve + performance. + num_segment_frames (`int`, *optional*): + The number of frames a single segment is made of. If not defined, `num_segment_frames` defaults to the model's stride + times the maximum input length. + attention_mask (`torch.Tensor`, *optional*): + `attention_mask` needs to be passed when doing long-form transcription using a batch size > 1. + time_precision (`int`, *optional*, defaults to 0.02): + The duration of output token in seconds. *E.g.* 0.02 means that a generated token on average accounts + for 20 ms. + return_token_timestamps (`bool`, *optional*): + Whether to return token-level timestamps with the text. This can be used with or without the + `return_timestamps` option. To get word-level timestamps, use the tokenizer to group the tokens into + words. + return_segments (`bool`, *optional*, defaults to `False`): + Whether to additionally return a list of all segments. Note that this option can only be enabled + when doing long-form transcription. + return_dict_in_generate (`bool`, *optional*, defaults to `False`): + Whether or not to return a [`~utils.ModelOutput`] instead of just returning the generated tokens. + Note that when doing long-form transcription, `return_dict_in_generate` can only be enabled when + `return_segments` is set True. In this case the generation outputs of each segment is added to each + segment. + kwargs (`Dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder + specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. + + Return: + [`~utils.ModelOutput`] or `torch.LongTensor` or `Dict[str, Any]`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor` or a dict of segments when `return_segments=True`. + + If the passed input is > 30 seconds / > 3000 mel input features and `return_segments=True` then a dictionary of generated sequence ids, called `sequences` and a list of each generated segment is returned. + + else if the passed input is <= 30 seconds / >= 3000 mel input features, the possible [`~utils.ModelOutput`] types are: + + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + + else only the generated output sequence ids are returned. + + Example: + + - *Longform transcription*: To transcribe or translate audios longer than 30 seconds, process the audio files without truncation and pass all mel features at once to generate. + + ```python + >>> import torch + >>> from transformers import AutoProcessor, WhisperForConditionalGeneration + >>> from datasets import load_dataset, Audio + + >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") + >>> model.cuda() # doctest: +IGNORE_RESULT + + >>> # load audios > 30 seconds + >>> ds = load_dataset("distil-whisper/meanwhile", "default")["test"] + >>> # resample to 16kHz + >>> ds = ds.cast_column("audio", Audio(sampling_rate=16000)) + >>> # take first 8 audios and retrieve array + >>> audio = ds[:8]["audio"] + >>> audio = [x["array"] for x in audio] + + >>> # make sure to NOT truncate the input audio, to return the `attention_mask` and to pad to the longest audio + >>> inputs = processor(audio, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000) + >>> inputs = inputs.to("cuda", torch.float32) + + >>> # transcribe audio to ids + >>> generated_ids = model.generate(**inputs) + + >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True) + >>> transcription[0] + " Folks, if you watch the show, you know, I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories developing the central headline pawns, definitely maneuvering an oso topical night to F6, fainting a classic Sicilian, nade door variation on the news, all the while seeing eight moves deep and patiently marshalling the latest press releases into a fisher's shows in Lip Nitsky attack that culminates in the elegant lethal slow-played, all-passant checkmate that is my nightly monologue. But sometimes, sometimes, folks, I. CHEERING AND APPLAUSE Sometimes I startle away, cubside down in the monkey bars of a condemned playground on a super fun site. Get all hept up on goofballs. Rummage that were discarded tag bag of defective toys. Yank out a fist bowl of disembodied doll limbs, toss them on a stained kid's place mat from a defunct dennies. set up a table inside a rusty cargo container down by the Wharf and challenged toothless drifters to the godless bughouse blitz of tournament that is my segment. Meanwhile." + ``` + + - *Shortform transcription*: If passed mel input features are < 30 seconds, the whole audio will be transcribed with a single call to generate. + + ```python + >>> import torch + >>> from transformers import AutoProcessor, WhisperForConditionalGeneration + >>> from datasets import load_dataset + + >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + + >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt") + >>> input_features = inputs.input_features + + >>> generated_ids = model.generate(inputs=input_features) + + >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + >>> transcription + ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' + ``` + + """ + # 0. deprecate old inputs + if "inputs" in kwargs: + input_features = kwargs.pop("inputs") + warnings.warn( + "The input name `inputs` is deprecated. Please make sure to use `input_features` instead.", + FutureWarning, + ) + # 1. prepare generation config + generation_config, kwargs = self._prepare_generation_config(generation_config, **kwargs) + + # 2. set global generate variables + input_stride = self.model.encoder.conv1.stride[0] * self.model.encoder.conv2.stride[0] + num_segment_frames = input_stride * self.config.max_source_positions + batch_size, total_input_frames = self._retrieve_total_input_frames( + input_features=input_features, input_stride=input_stride, kwargs=kwargs + ) + is_shortform = total_input_frames <= num_segment_frames + + if is_shortform: + # warn user of ignored inputs + self._maybe_warn_unused_inputs( + condition_on_prev_tokens=condition_on_prev_tokens, + temperature=temperature, + compression_ratio_threshold=compression_ratio_threshold, + logprob_threshold=logprob_threshold, + no_speech_threshold=no_speech_threshold, + total_input_frames=total_input_frames, + ) + + # 3. Make sure generation config is correctly set + # Make sure the generation config is correctly set depending on whether timestamps are to be returned or not + self._set_return_outputs( + return_dict_in_generate=return_dict_in_generate, + return_token_timestamps=return_token_timestamps, + is_shortform=is_shortform, + logprob_threshold=logprob_threshold, + generation_config=generation_config, + ) + self._set_return_timestamps( + return_timestamps=return_timestamps, is_shortform=is_shortform, generation_config=generation_config + ) + self._set_language_and_task( + language=language, task=task, is_multilingual=is_multilingual, generation_config=generation_config + ) + self._set_num_frames( + return_token_timestamps=return_token_timestamps, generation_config=generation_config, kwargs=kwargs + ) + self._set_thresholds_and_condition( + generation_config=generation_config, + logprob_threshold=logprob_threshold, + compression_ratio_threshold=compression_ratio_threshold, + no_speech_threshold=no_speech_threshold, + condition_on_prev_tokens=condition_on_prev_tokens, + ) + self._set_prompt_condition_type( + generation_config=generation_config, + prompt_condition_type=prompt_condition_type, + ) + + # pass self.config for backward compatibility + init_tokens = self._retrieve_init_tokens( + input_features, + generation_config=generation_config, + config=self.config, + num_segment_frames=num_segment_frames, + kwargs=kwargs, + ) + # passing `decoder_input_ids` is deprecated - the only exception is for assisted generation + # where the input ids are handled explicitly by the generate method + self._check_decoder_input_ids(kwargs=kwargs) + + # 3. Retrieve logits processors + begin_index = len(init_tokens) + logits_processor = self._retrieve_logit_processors( + generation_config=generation_config, + logits_processor=logits_processor, + begin_index=begin_index, # begin index is index of first generated decoder token + is_shortform=is_shortform, + num_beams=generation_config.num_beams, + ) + + # 5. If we're in shortform mode, simple generate the whole input at once and return the output + if is_shortform: + if temperature is not None: + generation_config.temperature = temperature + + decoder_input_ids = kwargs.pop("decoder_input_ids", None) + if decoder_input_ids is None: + one_tensor = torch.ones((batch_size, 1), device=self.device, dtype=torch.long) + decoder_input_ids = torch.cat([t * one_tensor for t in init_tokens], dim=-1) + + if prompt_ids is not None: + decoder_input_ids = torch.cat( + [prompt_ids[None].repeat(decoder_input_ids.shape[0], 1), decoder_input_ids], dim=-1 + ) + + max_new_tokens = generation_config.max_new_tokens if generation_config.max_new_tokens is not None else 0 + if max_new_tokens + decoder_input_ids.shape[-1] > self.config.max_target_positions: + raise ValueError( + f"The length of `decoder_input_ids` equal `prompt_ids` plus special start tokens is {decoder_input_ids.shape[-1]}, and the `max_new_tokens` " + f"is {max_new_tokens}. Thus, the combined length of " + f"`decoder_input_ids` and `max_new_tokens` is: {max_new_tokens + decoder_input_ids.shape[-1]}. This exceeds the " + f"`max_target_positions` of the Whisper model: {self.config.max_target_positions}. " + "You should either reduce the length of your prompt, or reduce the value of `max_new_tokens`, " + f"so that their combined length is less than {self.config.max_target_positions}." + ) + + outputs = super().generate( + input_features, + generation_config=generation_config, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + synced_gpus=synced_gpus, + decoder_input_ids=decoder_input_ids, + **kwargs, + ) + + if generation_config.return_token_timestamps and hasattr(generation_config, "alignment_heads"): + outputs["token_timestamps"] = self._extract_token_timestamps( + outputs, generation_config.alignment_heads, num_frames=generation_config.num_frames + ) + + return outputs + + # 6. Else we're in longform mode which is more complex. + # We need to chunk the audio input depending on when the model generates timestamp tokens + + # 6.1 Set and retrieve global longform generation variables + self._set_condition_on_prev_tokens( + condition_on_prev_tokens=condition_on_prev_tokens, generation_config=generation_config + ) + + timestamp_begin = generation_config.no_timestamps_token_id + 1 + temperatures = [temperature] if not isinstance(temperature, (list, tuple)) else temperature + temperature = temperatures[0] + batch_size = input_features.shape[0] + + max_frames, seek = self._retrieve_max_frames_and_seek( + batch_size=batch_size, attention_mask=attention_mask, total_input_frames=total_input_frames + ) + + # 6.2 Preppare running variables, list for generation + cur_bsz = batch_size + current_segments = self._prepare_segments( + prompt_ids=prompt_ids, + batch_size=batch_size, + generation_config=generation_config, + ) + + batch_idx_map = list(range(batch_size)) + do_condition_on_prev_tokens = [condition_on_prev_tokens for _ in range(batch_size)] + + # 6.2 Transcribe audio until we reach the end of all input audios + while (seek < max_frames).any(): + # 6.3 NOTE: When in longform transcription mode and batch size > 1 we need to dynamically reduce the batch size during the loop + # in case one audio finished earlier than another one. Thus, we need to keep a table of "previous-index-2-current-index" in order + # to know which original audio is being decoded + # Set updated index map, duration of previously decoded chunks and number of max frames of current decoding chunk + input_features, cur_bsz, batch_idx_map = self._maybe_reduce_batch( + input_features=input_features, + seek=seek, + max_frames=max_frames, + cur_bsz=cur_bsz, + batch_idx_map=batch_idx_map, + ) + time_offset = seek * time_precision / input_stride + seek_num_frames = (max_frames - seek).clamp(max=num_segment_frames) + + # 6.4 cut out next 30s segment from input features + segment_input = self._get_input_segment( + input_features=input_features, + seek=seek, + seek_num_frames=seek_num_frames, + num_segment_frames=num_segment_frames, + cur_bsz=cur_bsz, + batch_idx_map=batch_idx_map, + ) + + # 6.5 prepare decoder input ids + suppress_tokens = _get_attr_from_logit_processors( + logits_processor, SuppressTokensLogitsProcessor, "suppress_tokens" + ) + decoder_input_ids, kwargs = self._prepare_decoder_input_ids( + cur_bsz=cur_bsz, + init_tokens=init_tokens, + current_segments=current_segments, + batch_idx_map=batch_idx_map, + do_condition_on_prev_tokens=do_condition_on_prev_tokens, + prompt_ids=prompt_ids, + generation_config=generation_config, + config=self.config, + device=segment_input.device, + suppress_tokens=suppress_tokens, + kwargs=kwargs, + ) + + # 6.6 set max new tokens or max length + self._set_max_new_tokens_and_length( + config=self.config, + decoder_input_ids=decoder_input_ids, + generation_config=generation_config, + ) + + # 6.7 Set current `begin_index` for all logit processors + for proc in logits_processor: + if hasattr(proc, "set_begin_index"): + proc.set_begin_index(decoder_input_ids.shape[-1]) + + # 6.8 Run generate with fallback + seek_sequences, seek_outputs, should_skip, do_condition_on_prev_tokens = self.generate_with_fallback( + segment_input=segment_input, + decoder_input_ids=decoder_input_ids, + cur_bsz=cur_bsz, + batch_idx_map=batch_idx_map, + seek=seek, + num_segment_frames=num_segment_frames, + max_frames=max_frames, + temperatures=temperatures, + generation_config=generation_config, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + synced_gpus=synced_gpus, + return_token_timestamps=return_token_timestamps, + do_condition_on_prev_tokens=do_condition_on_prev_tokens, + kwargs=kwargs, + ) + + # 6.9 In every generated sequence, split by timestamp tokens and extract segments + for i, seek_sequence in enumerate(seek_sequences): + prev_i = batch_idx_map[i] + + if should_skip[i]: + seek[prev_i] += seek_num_frames[prev_i] + continue + + segments, segment_offset = self._retrieve_segment( + seek_sequence=seek_sequence, + seek_outputs=seek_outputs, + time_offset=time_offset, + timestamp_begin=timestamp_begin, + seek_num_frames=seek_num_frames, + time_precision=time_precision, + input_stride=input_stride, + prev_idx=prev_i, + idx=i, + return_token_timestamps=return_token_timestamps, + ) + + current_segments[prev_i] += segments + seek[prev_i] += segment_offset + + # 7. Once all segments are added to the list of all segments, called `current_segments`, we extract the predicted + # output tokens from the list of dicts. If we use batch size > 1, we make sure to pad the output + final_segments = ( + [x[1:] for x in current_segments] + if (prompt_ids is not None and generation_config.prompt_condition_type == "first-segment") + else current_segments + ) + sequences = _pad_to_max_length(final_segments, generation_config.pad_token_id, padding="right") + + # 8. If we return all segments, the predicted output sequences are put under `"sequences"`. + if return_segments: + return {"sequences": sequences, "segments": final_segments} + + return sequences + + def generate_with_fallback( + self, + segment_input, + decoder_input_ids, + cur_bsz, + batch_idx_map, + seek, + num_segment_frames, + max_frames, + temperatures, + generation_config, + logits_processor, + stopping_criteria, + prefix_allowed_tokens_fn, + synced_gpus, + return_token_timestamps, + do_condition_on_prev_tokens, + kwargs, + ): + kwargs = copy.copy(kwargs) + + # 6.6 Batch generate current chunk + seek_sequence_list = [None for _ in range(cur_bsz)] + seek_outputs_list = [None for _ in range(cur_bsz)] + needs_fallback = [False for _ in range(cur_bsz)] + should_skip = [False for _ in range(cur_bsz)] + fallback_index_map = list(range(cur_bsz)) + + if generation_config.no_speech_threshold is not None: + self._setup_no_speech_detection(logits_processor, segment_input, decoder_input_ids, kwargs) + + for fallback_idx, temperature in enumerate(temperatures): + generation_config.do_sample = temperature is not None and temperature > 0.0 + generation_config.temperature = temperature if generation_config.do_sample else 1.0 + if generation_config.do_sample: + generation_config.num_beams = 1 + + generate_kwargs = copy.copy(kwargs) + for key in ["do_sample", "temperature", "num_beams"]: + if key in generate_kwargs: + del generate_kwargs[key] + seek_outputs = super().generate( + segment_input, + generation_config, + logits_processor, + stopping_criteria, + prefix_allowed_tokens_fn, + synced_gpus, + decoder_input_ids=decoder_input_ids, + **generate_kwargs, + ) + + # post-process sequence tokens and outputs to be in list form + seek_sequences, seek_outputs = self._postprocess_outputs( + seek_outputs=seek_outputs, + decoder_input_ids=decoder_input_ids, + return_token_timestamps=return_token_timestamps, + generation_config=generation_config, + ) + + # 6.7 Extract cut sequences from every sequence and check if fallback should be applied + # Loop over each decoded audio individually as each decoding can be of a different length + new_fallback_index_map = [] + new_segment_input = [] + new_decoder_input_ids = [] + new_decoder_attention_mask = [] + + for i, seek_sequence in enumerate(seek_sequences): + # make sure we cut a predicted EOS token if we are not finished with the generation yet + prev_i = batch_idx_map[fallback_index_map[i]] + is_not_final = (seek[prev_i] + num_segment_frames) < max_frames[prev_i] + + # remove eos token id + if is_not_final and seek_sequence[-1] == generation_config.eos_token_id: + seek_sequence = seek_sequence[:-1] + if return_token_timestamps: + seek_outputs[i]["token_timestamps"] = seek_outputs[i]["token_timestamps"][:-1] + + # remove all padding tokens + if seek_sequence[-1] == generation_config.pad_token_id: + num_paddings = (seek_sequence == generation_config.pad_token_id).sum() + seek_sequence = seek_sequence[:-num_paddings] + if return_token_timestamps: + seek_outputs[i]["token_timestamps"] = seek_outputs[i]["token_timestamps"][:-num_paddings] + + # check which sequences in batch need fallback & which should be skipped + needs_fallback[i], should_skip[i] = self._need_fallback( + seek_sequence, + seek_outputs, + i, + logits_processor, + generation_config, + self.config.vocab_size, + temperature, + ) + + seek_sequence_list[fallback_index_map[i]] = seek_sequence + seek_outputs_list[fallback_index_map[i]] = seek_outputs[i] + is_low_temperature = temperature is None or temperature < 0.5 + do_condition_on_prev_tokens[fallback_index_map[i]] = ( + generation_config.condition_on_prev_tokens and is_low_temperature + ) + + if needs_fallback[i]: + new_fallback_index_map.append(fallback_index_map[i]) + new_segment_input.append(segment_input[i]) + new_decoder_input_ids.append(decoder_input_ids[i]) + if "decoder_attention_mask" in kwargs: + new_decoder_attention_mask.append(kwargs["decoder_attention_mask"][i]) + + fallback_index_map = new_fallback_index_map + + # if no sequence needs to be run with temperature fallback, we're finished + if len(fallback_index_map) == 0 or fallback_idx == len(temperatures) - 1: + seek_sequences = seek_sequence_list + seek_outputs = seek_outputs_list + break + + # if we're still in the loop, make sure that decoder_input_ids and segment inputs are tensors + decoder_input_ids = torch.stack(new_decoder_input_ids) + segment_input = torch.stack(new_segment_input) + if "decoder_attention_mask" in kwargs: + kwargs["decoder_attention_mask"] = torch.stack(new_decoder_attention_mask) + + return seek_sequences, seek_outputs, should_skip, do_condition_on_prev_tokens + + @staticmethod + def _prepare_segments(prompt_ids, batch_size, generation_config): + if prompt_ids is not None and generation_config.prompt_condition_type == "first-segment": + prev_sot_token_id = getattr(generation_config, "prev_sot_token_id", None) + prompt_ids = prompt_ids[1:] if prompt_ids[0] == prev_sot_token_id else prompt_ids + current_segments = [[{"tokens": prompt_ids}] for _ in range(batch_size)] + else: + current_segments = [[] for _ in range(batch_size)] + + return current_segments + + def _postprocess_outputs(self, seek_outputs, decoder_input_ids, return_token_timestamps, generation_config): + # remove all previously passed decoder input ids + if isinstance(seek_outputs, torch.Tensor): + seek_outputs = seek_outputs[:, decoder_input_ids.shape[-1] :] + return seek_outputs, seek_outputs + + if return_token_timestamps and hasattr(generation_config, "alignment_heads"): + num_frames = getattr(generation_config, "num_frames", None) + seek_outputs["token_timestamps"] = self._extract_token_timestamps( + seek_outputs, generation_config.alignment_heads, num_frames=num_frames + ) + seek_outputs["token_timestamps"] = seek_outputs["token_timestamps"][:, decoder_input_ids.shape[-1] :] + + seek_outputs["sequences"] = seek_outputs["sequences"][:, decoder_input_ids.shape[-1] :] + + def split_by_batch_index(values, key, batch_idx): + if key == "scores": + return [v[batch_idx].cpu() for v in values] + elif key == "past_key_values": + # we don't save `past_key_values` as this is too costly + return None + elif isinstance(values[batch_idx], tuple) and torch.is_tensor(values[batch_idx][0]): + return tuple(tuple(w[batch_idx][None].cpu() for w in v) for v in values) + return values[batch_idx].cpu() + + sequence_tokens = seek_outputs["sequences"] + seek_outputs = [ + {k: split_by_batch_index(v, k, i) for k, v in seek_outputs.items()} + for i in range(sequence_tokens.shape[0]) + ] + + return sequence_tokens, seek_outputs + + def _need_fallback( + self, + seek_sequence, + seek_outputs, + index, + logits_processor, + generation_config, + vocab_size, + temperature, + ): + needs_fallback = False + should_skip = False + if generation_config.compression_ratio_threshold is not None: + compression_ratio = self._retrieve_compression_ratio(seek_sequence, vocab_size) + + if compression_ratio > generation_config.compression_ratio_threshold: + needs_fallback = True + + if generation_config.logprob_threshold is not None: + if "sequences_scores" in seek_outputs[0]: + logprobs = [s["sequences_scores"] for s in seek_outputs][index] + else: + scores = seek_outputs[index]["scores"] + logprobs = self._retrieve_avg_logprobs( + scores, seek_sequence, generation_config.eos_token_id, temperature + ) + + if logprobs < generation_config.logprob_threshold: + needs_fallback = True + + if generation_config.no_speech_threshold is not None: + no_speech_prob = _get_attr_from_logit_processors( + logits_processor, WhisperNoSpeechDetection, "no_speech_prob" + ) + + if ( + logprobs < generation_config.logprob_threshold + and no_speech_prob[index] > generation_config.no_speech_threshold + ): + needs_fallback = False + should_skip = True + + return needs_fallback, should_skip + + @staticmethod + def _setup_no_speech_detection(logits_processor, segment_input, decoder_input_ids, kwargs): + set_inputs = _get_attr_from_logit_processors(logits_processor, WhisperNoSpeechDetection, "set_inputs") + extra_kwargs = {k: v for k, v in kwargs.items() if torch.is_tensor(v)} + set_inputs({"inputs": segment_input, "decoder_input_ids": decoder_input_ids, **extra_kwargs}) + + @staticmethod + def _retrieve_total_input_frames(input_features, input_stride, kwargs): + if input_features is not None: + return input_features.shape[0], input_features.shape[-1] + + if "encoder_outputs" in kwargs: + encoder_outputs_shape = ( + kwargs["encoder_outputs"][0].shape + if isinstance(kwargs["encoder_outputs"], BaseModelOutput) + else kwargs["encoder_outputs"].shape + ) + return encoder_outputs_shape[0], encoder_outputs_shape[1] * input_stride + + raise ValueError("Make sure to provide either `input_features` or `encoder_outputs` to `generate`.") + + @staticmethod + def _maybe_warn_unused_inputs( + condition_on_prev_tokens, + temperature, + compression_ratio_threshold, + logprob_threshold, + no_speech_threshold, + total_input_frames, + ): + warning_prefix = ( + f"Audio input consists of only {total_input_frames}. " + "Short-form transcription is activated." + "{}, but will be ignored." + ) + if condition_on_prev_tokens is not None: + logger.warning(warning_prefix.format(f"condition_on_prev_tokens is set to {condition_on_prev_tokens}")) + + if compression_ratio_threshold is not None: + logger.warning( + warning_prefix.format(f"compression_ratio_threshold is set to {compression_ratio_threshold}") + ) + + if logprob_threshold is not None: + logger.warning(warning_prefix.format(f"logprob_threshold is set to {logprob_threshold}")) + + if no_speech_threshold is not None: + logger.warning(warning_prefix.format(f"no_speech_threshold is set to {no_speech_threshold}")) + + # when passing temperature as a list it cannot just be ignored => throw error in this case + if isinstance(temperature, (list, tuple)): + raise ValueError( + f"Audio input consists of only {total_input_frames}. Short-form transcription is activated." + f"temperature cannot be set to {temperature} which can only be used for temperature fallback for long-form generation. Make sure to set `temperature` to a float value or `None` for short-form generation." + ) + + @staticmethod + def _set_return_outputs( + return_dict_in_generate, return_token_timestamps, is_shortform, logprob_threshold, generation_config + ): + if return_dict_in_generate is None: + return_dict_in_generate = generation_config.return_dict_in_generate + + generation_config.return_token_timestamps = return_token_timestamps + if return_token_timestamps: + return_dict_in_generate = True + generation_config.output_attentions = True + generation_config.output_scores = True + + if not is_shortform and logprob_threshold is not None: + return_dict_in_generate = True + generation_config.output_scores = True + + generation_config.return_dict_in_generate = return_dict_in_generate + + @staticmethod + def _set_return_timestamps(return_timestamps, is_shortform, generation_config): + if not is_shortform: + if return_timestamps is False: + raise ValueError( + "You have passed more than 3000 mel input features (> 30 seconds) which automatically enables long-form generation which " + "requires the model to predict timestamp tokens. Please either pass `return_timestamps=True` or make sure to pass no more than 3000 mel input features." + ) + + logger.info("Setting `return_timestamps=True` for long-form generation.") + return_timestamps = True + + if return_timestamps and not hasattr(generation_config, "no_timestamps_token_id"): + raise ValueError( + "You are trying to return timestamps, but the generation config is not properly set. " + "Make sure to initialize the generation config with the correct attributes that are needed such as `no_timestamps_token_id`. " + "For more details on how to generate the approtiate config, refer to https://github.com/huggingface/transformers/issues/21878#issuecomment-1451902363" + ) + + generation_config.return_timestamps = return_timestamps + + @staticmethod + def _set_language_and_task(language, task, is_multilingual, generation_config): + if is_multilingual is not None: + if not hasattr(generation_config, "is_multilingual"): + raise ValueError( + "The generation config is outdated and is thus not compatible with the `is_multilingual` argument " + "to `generate`. Please update the generation config as per the instructions " + "https://github.com/huggingface/transformers/issues/25084#issuecomment-1664398224" + ) + generation_config.is_multilingual = is_multilingual + + if hasattr(generation_config, "is_multilingual") and not generation_config.is_multilingual: + if task is not None or language is not None: + raise ValueError( + "Cannot specify `task` or `language` for an English-only model. If the model is intended to be " + "multilingual, pass `is_multilingual=True` to generate, or update the generation config." + ) + + if language is not None: + if not hasattr(generation_config, "lang_to_id"): + raise ValueError( + "The generation config is outdated and is thus not compatible with the `language` argument " + "to `generate`. Either set the language using the `forced_decoder_ids` in the model config, " + "or update the generation config as per the instructions https://github.com/huggingface/transformers/issues/25084#issuecomment-1664398224" + ) + language = language.lower() + generation_config.language = language + + if task is not None: + if not hasattr(generation_config, "task_to_id"): + raise ValueError( + "The generation config is outdated and is thus not compatible with the `task` argument " + "to `generate`. Either set the task using the `forced_decoder_ids` in the model config, " + "or update the generation config as per the instructions https://github.com/huggingface/transformers/issues/25084#issuecomment-1664398224" + ) + generation_config.task = task + + def _retrieve_init_tokens(self, input_features, generation_config, config, num_segment_frames, kwargs): + def replace_or_add(lst: List[int], num: int, itr: Iterator[int]): + """short function to replace num with a itr in lst""" + found = any(i in lst for i in itr) + if found: + lst = [num if i in itr else i for i in lst] + else: + lst.append(num) + return lst + + task = getattr(generation_config, "task", None) + language = getattr(generation_config, "language", None) + + forced_decoder_ids = generation_config.forced_decoder_ids + if forced_decoder_ids is not None: + if language is None and task is None and forced_decoder_ids[0][1] is None: + logger.warning_once( + "Due to a bug fix in https://github.com/huggingface/transformers/pull/28687 transcription using a multilingual Whisper will default to language detection followed by transcription instead of translation to English." + "This might be a breaking change for your use case. If you want to instead always translate your audio to English, make sure to pass `language='en'`." + ) + elif hasattr(config, "forced_decoder_ids") and config.forced_decoder_ids is not None: + forced_decoder_ids = config.forced_decoder_ids + + if forced_decoder_ids is not None and task is not None: + logger.info( + f"You have passed task={task}, but also have set `forced_decoder_ids` to {forced_decoder_ids} which creates a conflict. `forced_decoder_ids` will be ignored in favor of task={task}." + ) + forced_decoder_ids = None + elif forced_decoder_ids is not None and language is not None: + logger.info( + f"You have passed language={language}, but also have set `forced_decoder_ids` to {forced_decoder_ids} which creates a conflict. `forced_decoder_ids` will be ignored in favor of language={language}." + ) + forced_decoder_ids = None + + init_tokens = [generation_config.decoder_start_token_id] + if forced_decoder_ids is not None and forced_decoder_ids[0][0] == 1: + i = 1 + while len(forced_decoder_ids) > 0 and forced_decoder_ids[0][0] == i: + init_tokens += [forced_decoder_ids[0][1]] + forced_decoder_ids = forced_decoder_ids[1:] + i += 1 + + if len(forced_decoder_ids) > 0: + raise ValueError( + f"You are using token ids in `forced_decoder_ids` that do not seem to correctly follow the prompt pattern of Whisper. Make sure that {forced_decoder_ids} has an entry for all indices >= 1 and < {forced_decoder_ids[0][0]}.", + ) + + # from v4.39 the forced decoder ids are always None in favour of decoder input ids + generation_config.forced_decoder_ids = None + + is_lang_id_undefined = len(init_tokens) <= 1 or (len(init_tokens) > 1 and init_tokens[1] is None) + if language is not None: + if language in generation_config.lang_to_id.keys(): + language_token = language + elif language in TO_LANGUAGE_CODE.keys(): + language_token = f"<|{TO_LANGUAGE_CODE[language]}|>" + elif language in TO_LANGUAGE_CODE.values(): + language_token = f"<|{language}|>" + else: + is_language_code = len(language) == 2 + raise ValueError( + f"Unsupported language: {language}. Language should be one of:" + f" {list(TO_LANGUAGE_CODE.values()) if is_language_code else list(TO_LANGUAGE_CODE.keys())}." + ) + if language_token not in generation_config.lang_to_id: + raise ValueError( + f"{language_token} is not supported by this specific model as it is not in the `generation_config.lang_to_id`." + "(You should just add it to the generation config)" + ) + + lang_id = generation_config.lang_to_id[language_token] + + # if language is defined it'll overwrite language ids that might have already been defined via the generation_config + replace_or_add(init_tokens, lang_id, generation_config.lang_to_id.values()) + elif hasattr(generation_config, "lang_to_id") and is_lang_id_undefined: + # language is not defined or intentially set to `None` to trigger language detection + lang_ids = self.detect_language( + input_features=input_features, + encoder_outputs=kwargs.get("encoder_outputs", None), + generation_config=generation_config, + num_segment_frames=num_segment_frames, + ) + + if torch.unique(lang_ids).shape[0] > 1: + raise ValueError( + "Multiple languages detected when trying to predict the most likely target language for transcription. It is currently not supported to transcribe to different languages in a single batch. Please make sure to either force a single language by passing `language='...'` or make sure all input audio is of the same language." + ) + + lang_id = lang_ids[0].item() + + # append or replace lang_id to init_tokens + if len(init_tokens) > 1: + init_tokens[1] = lang_id + else: + init_tokens.append(lang_id) + + if task is not None: + if task in TASK_IDS: + init_tokens.append(generation_config.task_to_id[generation_config.task]) + task_id = generation_config.task_to_id[generation_config.task] + + # if task is defined it'll overwrite task ids that might have already been defined via the generation_config + replace_or_add(init_tokens, task_id, generation_config.task_to_id.values()) + else: + raise ValueError(f"The `{task}`task is not supported. The task should be one of `{TASK_IDS}`") + elif language is not None and hasattr(generation_config, "task_to_id"): + # if language is defined, but no task id is in `init_tokens`, default to transcribe + if not any(i in init_tokens for i in generation_config.task_to_id.values()): + init_tokens.append(generation_config.task_to_id["transcribe"]) + + if ( + not generation_config.return_timestamps + and hasattr(generation_config, "no_timestamps_token_id") + and init_tokens[-1] != generation_config.no_timestamps_token_id + ): + init_tokens.append(generation_config.no_timestamps_token_id) + elif generation_config.return_timestamps and init_tokens[-1] == generation_config.no_timestamps_token_id: + logger.info( + "<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `'True'`." + ) + init_tokens = init_tokens[:-1] + + # let's make sure we don't pass `None` tokens as prompt tokens + init_tokens = [t for t in init_tokens if t is not None] + + return init_tokens + + def detect_language( + self, + input_features: Optional[torch.FloatTensor] = None, + encoder_outputs: Optional[Union[torch.FloatTensor, BaseModelOutput]] = None, + generation_config: Optional[GenerationConfig] = None, + num_segment_frames: int = 3000, + ) -> torch.Tensor: + """ + Detects language from log-mel input features or encoder_outputs + + Parameters: + input_features (`torch.Tensor` of shape `(batch_size, feature_size, sequence_length)`, *optional*): + Float values of log-mel features extracted from the raw speech waveform. The raw speech waveform can be obtained by + loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via + the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the + [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a + tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] for details. + encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + num_segment_frames (`int`, defaults to 3000): + The number of log-mel frames the model expects + + Return: + A `torch.LongTensor` representing the detected language ids. + """ + if input_features is None and encoder_outputs is None: + raise ValueError("You have to specify either `input_features` or `encoder_outputs`") + elif input_features is not None and encoder_outputs is not None: + raise ValueError("Make sure to specificy only one of `input_features` or `encoder_outputs` - not both!") + elif input_features is not None: + inputs = {"input_features": input_features[:, :, :num_segment_frames]} + batch_size = input_features.shape[0] + elif encoder_outputs is not None: + inputs = {"encoder_outputs": encoder_outputs} + batch_size = ( + encoder_outputs[0].shape[0] if isinstance(encoder_outputs, BaseModelOutput) else encoder_outputs[0] + ) + + generation_config = generation_config or self.generation_config + decoder_input_ids = ( + torch.ones((batch_size, 1), device=self.device, dtype=torch.long) + * generation_config.decoder_start_token_id + ) + + with torch.no_grad(): + logits = self(**inputs, decoder_input_ids=decoder_input_ids).logits[:, -1] + + non_lang_mask = torch.ones_like(logits[0], dtype=torch.bool) + non_lang_mask[list(generation_config.lang_to_id.values())] = False + + logits[:, non_lang_mask] = -np.inf + + lang_ids = logits.argmax(-1) + + return lang_ids + + @staticmethod + def _check_decoder_input_ids(kwargs): + decoder_input_ids = kwargs.get("decoder_input_ids", None) + assistant_model = kwargs.get("assistant_model", None) + if decoder_input_ids is not None and assistant_model is not None: + raise ValueError( + "Passing `decoder_input_ids` is deprecated. Consider passing `prompt_ids` instead.", + ) + + @staticmethod + def _set_num_frames(return_token_timestamps, generation_config, kwargs): + if return_token_timestamps: + if getattr(generation_config, "task", None) == "translate": + logger.warning("Token-level timestamps may not be reliable for task 'translate'.") + if not hasattr(generation_config, "alignment_heads"): + raise ValueError( + "Model generation config has no `alignment_heads`, token-level timestamps not available. " + "See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config." + ) + generation_config.num_frames = kwargs.pop("num_frames", None) + + @staticmethod + def _set_thresholds_and_condition( + generation_config, + logprob_threshold, + compression_ratio_threshold, + no_speech_threshold, + condition_on_prev_tokens, + ): + generation_config.logprob_threshold = ( + logprob_threshold + if logprob_threshold is not None + else getattr(generation_config, "logprob_threshold", None) + ) + generation_config.compression_ratio_threshold = ( + compression_ratio_threshold + if compression_ratio_threshold is not None + else getattr(generation_config, "compression_ratio_threshold", None) + ) + generation_config.no_speech_threshold = ( + no_speech_threshold + if no_speech_threshold is not None + else getattr(generation_config, "no_speech_threshold", None) + ) + generation_config.condition_on_prev_tokens = ( + condition_on_prev_tokens + if condition_on_prev_tokens is not None + else getattr(generation_config, "condition_on_prev_tokens", None) + ) + + @staticmethod + def _set_prompt_condition_type(generation_config, prompt_condition_type): + allowed_cond_types = ["first-segment", "all-segments"] + + # default to "first-segment" + prompt_condition_type = prompt_condition_type or allowed_cond_types[0] + + if prompt_condition_type not in allowed_cond_types: + raise ValueError( + f"`prompt_condition_type={prompt_condition_type} does not exist. Make sure to set `prompt_condition_type` to one of {', '.join(allowed_cond_types)}" + ) + + if generation_config.condition_on_prev_tokens is not True and prompt_condition_type == "all-segments": + raise ValueError( + "Make sure to set `condition_on_prev_tokens=True` when setting `prompt_condition_type='all-segments'`." + ) + + generation_config.prompt_condition_type = prompt_condition_type + + @staticmethod + def _set_condition_on_prev_tokens(condition_on_prev_tokens, generation_config): + condition_on_prev_tokens = ( + condition_on_prev_tokens + if condition_on_prev_tokens is not None + else getattr(generation_config, "condition_on_prev_tokens", False) + ) + generation_config.condition_on_prev_tokens = condition_on_prev_tokens + + @staticmethod + def _retrieve_max_frames_and_seek(batch_size, attention_mask, total_input_frames): + if batch_size > 1 and attention_mask is None: + raise ValueError( + "When doing batched long-form audio transcription, make sure to pass an `attention_mask`. You can retrieve the `attention_mask` by doing `processor(audio, ..., return_attention_mask=True)` " + ) + elif batch_size > 1: + max_frames = attention_mask.sum(-1).cpu().to(torch.long) + seek = torch.zeros((batch_size,), dtype=torch.long) + else: + max_frames = torch.ones((1,), dtype=torch.long) * total_input_frames + seek = torch.zeros((1,), dtype=torch.long) + + return max_frames, seek + + def _retrieve_logit_processors(self, generation_config, logits_processor, begin_index, is_shortform, num_beams): + if generation_config.return_timestamps is True: + timestamp_processor = WhisperTimeStampLogitsProcessor(generation_config, begin_index=begin_index) + logits_processor = ( + [timestamp_processor] if logits_processor is None else [timestamp_processor] + logits_processor + ) + + if generation_config.suppress_tokens is not None: + suppress_tokens_processor = SuppressTokensLogitsProcessor(generation_config.suppress_tokens) + logits_processor = ( + [suppress_tokens_processor] + if logits_processor is None + else [suppress_tokens_processor] + logits_processor + ) + generation_config.suppress_tokens = None + + if generation_config.begin_suppress_tokens is not None: + begin_suppress_processor = SuppressTokensAtBeginLogitsProcessor( + generation_config.begin_suppress_tokens, begin_index=begin_index + ) + logits_processor = ( + [begin_suppress_processor] + if logits_processor is None + else [begin_suppress_processor] + logits_processor + ) + generation_config.begin_suppress_tokens = None + + if generation_config.no_speech_threshold is not None and not is_shortform: + no_speech_detector = WhisperNoSpeechDetection( + no_speech_token=generation_config.no_timestamps_token_id - 1, + begin_index=begin_index, + scores_is_logprobs=num_beams > 1, + ) + logits_processor = ( + [no_speech_detector] if logits_processor is None else [no_speech_detector] + logits_processor + ) + no_speech_detector.set_model(self) + + return logits_processor + + @staticmethod + def _maybe_reduce_batch(input_features, seek, max_frames, cur_bsz, batch_idx_map): + prev_bsz = cur_bsz + new_batch_idx_map = [] + for i in range(prev_bsz): + prev_i = batch_idx_map[i] + if seek[prev_i] >= max_frames[prev_i]: + cut_index = i + (cur_bsz - prev_bsz) + cur_bsz -= 1 + input_features = torch.cat([input_features[:cut_index], input_features[cut_index + 1 :]], dim=0) + else: + # cut out index that goes away + new_batch_idx_map.append(prev_i) + + return input_features, cur_bsz, new_batch_idx_map + + @staticmethod + def _get_input_segment(input_features, seek, seek_num_frames, num_segment_frames, cur_bsz, batch_idx_map): + segment_input = [] + for i in range(cur_bsz): + prev_i = batch_idx_map[i] + segment_input_slice = input_features[i : i + 1, :, seek[prev_i] : seek[prev_i] + seek_num_frames[prev_i]] + + if segment_input_slice.shape[-1] < num_segment_frames: + # pad to 3000 if necessary + segment_input_slice = F.pad( + segment_input_slice, pad=(0, num_segment_frames - segment_input_slice.shape[-1]) + ) + + segment_input.append(segment_input_slice) + + segment_input = torch.cat(segment_input, dim=0) + + return segment_input + + @staticmethod + def _prepare_decoder_input_ids( + cur_bsz, + init_tokens, + current_segments, + batch_idx_map, + do_condition_on_prev_tokens, + prompt_ids, + generation_config, + config, + device, + suppress_tokens, + kwargs, + ): + cut_off_length = config.max_target_positions // 2 - 1 + + one_tensor = torch.ones((cur_bsz, 1), device=device, dtype=torch.long) + decoder_input_ids = torch.cat([t * one_tensor for t in init_tokens], dim=-1) + + prev_start_of_text = getattr(generation_config, "prev_sot_token_id", None) + if prev_start_of_text is None: + prev_start_of_text = suppress_tokens[-2] if suppress_tokens is not None else None + + if any(do_condition_on_prev_tokens) and len(current_segments[0]) > 0: + # according to https://github.com/openai/whisper/blob/e58f28804528831904c3b6f2c0e473f346223433/whisper/decoding.py#L609 + active_segments = [current_segments[i] if do_condition_on_prev_tokens[i] else None for i in batch_idx_map] + + if prompt_ids is not None and generation_config.prompt_condition_type == "all-segments": + prev_ids = prompt_ids + else: + prev_ids = prev_start_of_text * one_tensor[0] if prev_start_of_text is not None else None + + prev_tokens = _pad_to_max_length( + active_segments, + generation_config.pad_token_id, + padding="left", + bos_token_tensor=prev_ids, + cut_off_length=cut_off_length, + ) + decoder_input_ids = torch.cat([prev_tokens, decoder_input_ids], dim=-1) + + kwargs["decoder_attention_mask"] = decoder_input_ids != generation_config.pad_token_id + elif prompt_ids is not None: + prev_tokens = prompt_ids[None].repeat(decoder_input_ids.shape[0], 1) + decoder_input_ids = torch.cat([prev_tokens, decoder_input_ids], dim=-1) + # make sure `"decoder_attention_mask"` is not passed to forward + kwargs.pop("decoder_attention_mask", None) + else: + # make sure `"decoder_attention_mask"` is not passed to forward + kwargs.pop("decoder_attention_mask", None) + + return decoder_input_ids, kwargs + + @staticmethod + def _set_max_new_tokens_and_length(config, decoder_input_ids, generation_config): + num_initial_tokens = min(config.max_target_positions // 2 - 1, decoder_input_ids.shape[-1] - 1) + + # Make sure we don't get larger than `max_length` + if generation_config.max_length is not None and generation_config.max_new_tokens is None: + max_length = min(generation_config.max_length + num_initial_tokens, config.max_target_positions) + logger.info( + f"Increase max_length from {generation_config.max_length} to {max_length} since input is conditioned on previous segment." + ) + elif ( + generation_config.max_new_tokens is not None + and generation_config.max_new_tokens + decoder_input_ids.shape[-1] > config.max_target_positions + ): + max_new_tokens = config.max_target_positions - decoder_input_ids.shape[-1] + generation_config.max_new_tokens = max_new_tokens + + @staticmethod + def _retrieve_compression_ratio(tokens, vocab_size): + """Compute byte length of zlib compressed token bytes vs. byte length of raw token bytes""" + length = int(math.log2(vocab_size) / 8) + 1 + token_bytes = b"".join([t.to_bytes(length, "little") for t in tokens.tolist()]) + compression_ratio = len(token_bytes) / len(zlib.compress(token_bytes)) + + return compression_ratio + + @staticmethod + def _retrieve_avg_logprobs(scores, tokens, eos_token_id, temperature): + rescale_temperature = temperature if temperature > 0.0 else 1 + scores = torch.stack(scores).to(tokens.device) + + if scores.shape[0] > tokens.shape[0]: + scores = scores[: tokens.shape[0]] + else: + tokens = tokens[-scores.shape[0] :] + + logprobs = F.log_softmax((scores * rescale_temperature).float(), dim=-1).to(scores.dtype) + + # retrieve logprob of selected tokens and sum + sum_logprobs = sum((logprobs[i][tokens[i]] * (tokens[i] != eos_token_id)) for i in range(logprobs.shape[0])) + length = (tokens != eos_token_id).sum(-1) if eos_token_id is not None else tokens.shape[0] + + avg_logprobs = sum_logprobs / (length + 1) + return avg_logprobs + + @staticmethod + def _retrieve_segment( + seek_sequence, + seek_outputs, + time_offset, + timestamp_begin, + seek_num_frames, + time_precision, + input_stride, + prev_idx, + idx, + return_token_timestamps, + ): + # find the predicted "end of segment" predictions of Whisper + # "end of segment" predictions occur whenever Whisper predicts a timestamp token + timestamp_tokens: torch.Tensor = seek_sequence.ge(timestamp_begin) + single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True] + timestamp_segment_indices = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + timestamp_segment_indices.add_(1) + token_timestamps = seek_outputs[idx]["token_timestamps"] if return_token_timestamps else [] + + # If whisper predicted a "end of segment" via a timestep token, let's go ever each + # "end of segment" prediction and slice the decoding into segments accordingly + if len(timestamp_segment_indices) > 0: + # if the output contains two consecutive timestamp tokens + slices = timestamp_segment_indices.tolist() + segments = [] + if single_timestamp_ending: + slices.append(len(seek_sequence)) + + last_slice = 0 + # Add each segment to list of all segments + for current_slice in slices: + sliced_tokens = seek_sequence[last_slice:current_slice] + start_timestamp_pos = sliced_tokens[0].item() - timestamp_begin + end_timestamp_pos = sliced_tokens[-1].item() - timestamp_begin + segments.append( + { + "start": time_offset[prev_idx] + start_timestamp_pos * time_precision, + "end": time_offset[prev_idx] + end_timestamp_pos * time_precision, + "tokens": sliced_tokens, + "result": seek_outputs[idx], + } + ) + if return_token_timestamps: + segments[-1]["token_timestamps"] = ( + token_timestamps[last_slice:current_slice] + time_offset[prev_idx] + ) + last_slice = current_slice + + if single_timestamp_ending: + # single timestamp at the end means no speech after the last timestamp. + segment_offset = seek_num_frames[prev_idx] + else: + # otherwise, ignore the unfinished segment and seek to the last timestamp + # here we throw away all predictions after the last predicted "end of segment" + # since we are cutting right in the middle of an audio + last_timestamp_pos = seek_sequence[last_slice - 1].item() - timestamp_begin + segment_offset = last_timestamp_pos * input_stride + else: + # If whisper does not predict any "end of segment" token, then + # the whole decoding is considered a segment and we add it to the list of segments + timestamps = seek_sequence[timestamp_tokens.nonzero().flatten()] + last_timestamp_pos = seek_num_frames[prev_idx] + if timestamps.numel() > 0 and timestamps[-1].item() != timestamp_begin: + # no consecutive timestamps but it has a timestamp; use the last one. + last_timestamp_pos = timestamps[-1].item() - timestamp_begin + segments = [ + { + "start": time_offset[prev_idx], + "end": time_offset[prev_idx] + last_timestamp_pos * time_precision, + "tokens": seek_sequence, + "result": seek_outputs[idx], + } + ] + if return_token_timestamps: + segments[-1]["token_timestamps"] = token_timestamps + time_offset[prev_idx] + segment_offset = seek_num_frames[prev_idx] + + return segments, segment_offset diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_flax_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_flax_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..14ce899ab16cd8dd49ca16356796ed9c6bbc373b --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_flax_whisper.py @@ -0,0 +1,1696 @@ +# coding=utf-8 +# Copyright 2022 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Flax whisper model.""" + +import math +import random +from functools import partial +from typing import Optional, Tuple + +import flax.linen as nn +import jax +import jax.numpy as jnp +from flax.core.frozen_dict import FrozenDict, freeze, unfreeze +from flax.linen import combine_masks, make_causal_mask +from flax.linen import partitioning as nn_partitioning +from flax.linen.attention import dot_product_attention_weights +from flax.traverse_util import flatten_dict, unflatten_dict +from jax import lax +from jax.random import PRNGKey + +from ...generation.flax_logits_process import FlaxWhisperTimeStampLogitsProcessor +from ...modeling_flax_outputs import ( + FlaxBaseModelOutput, + FlaxBaseModelOutputWithPastAndCrossAttentions, + FlaxCausalLMOutputWithCrossAttentions, + FlaxSeq2SeqLMOutput, + FlaxSeq2SeqModelOutput, + FlaxSequenceClassifierOutput, +) +from ...modeling_flax_utils import ( + ACT2FN, + FlaxPreTrainedModel, + append_call_sample_docstring, + append_replace_return_docstrings, + overwrite_call_docstring, +) +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_whisper import WhisperConfig + + +logger = logging.get_logger(__name__) + + +_CHECKPOINT_FOR_DOC = "openai/whisper-tiny" +_CONFIG_FOR_DOC = "WhisperConfig" + +remat = nn_partitioning.remat + + +def sinusoidal_embedding_init(key, shape, dtype=jnp.float_) -> jax.Array: + """Returns sinusoids for positional embedding""" + length, channels = shape + if channels % 2 != 0: + raise ValueError( + f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels." + ) + log_timescale_increment = math.log(10000) / (channels // 2 - 1) + inv_timescales = jnp.exp(-log_timescale_increment * jnp.arange(channels // 2)) + scaled_time = jnp.arange(length).reshape(-1, 1) * inv_timescales.reshape(1, -1) + return jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1).astype(dtype) + + +WHISPER_START_DOCSTRING = r""" + This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its models (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) This model is also a Flax Linen + [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a + regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. + Finally, this model supports inherent JAX features such as: + - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) + - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) + - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) + - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) + + Parameters: + config ([`WhisperConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. + dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): + The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and + `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision + inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. + **Note that this only specifies the dtype of the computation and does not influence the dtype of model + parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] + and [`~FlaxPreTrainedModel.to_bf16`]. +""" + +WHISPER_INPUTS_DOCSTRING = r""" + Args: + input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`): + Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by + loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via + the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the + [`WhisperFeatureExtractor`] should be used for extracting the features, padding and conversion into a + tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`] + attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but + is not used. By default the silence in the input log mel spectrogram are ignored. + decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using + [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + [What are decoder input IDs?](../glossary#decoder-input-ids) Whisper uses the `decoder_start_token_id` as + the starting token for `decoder_input_ids` generation. + decoder_attention_mask (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 + in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. + position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Whisper does not use `position_ids` in the encoder as `input_features` is always the same size and doesn't + use masking, but this argument is preserved for compatibility. By default the silence in the input log mel + spectrogram are ignored. + decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the + range `[0, config.max_position_embeddings - 1]`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +WHISPER_ENCODE_INPUTS_DOCSTRING = r""" + Args: + input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`): + Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by + loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via + the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the + [`WhisperFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a + tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`]. + attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but + is not used. By default the silence in the input log mel spectrogram are ignored. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +WHISPER_DECODE_INPUTS_DOCSTRING = r""" + Args: + decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`): + Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using + [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + [What are decoder input IDs?](../glossary#decoder-input-ids) + encoder_outputs (`tuple(tuple(numpy.ndarray)`): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. + encoder_attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, + but it is not used. By default the silence in the input log mel spectrogram are ignored. + decoder_attention_mask (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 + in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. + decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the + range `[0, config.max_position_embeddings - 1]`. + past_key_values (`Dict[str, numpy.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): + Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast + auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class FlaxWhisperAttention(nn.Module): + config: WhisperConfig + embed_dim: int + num_heads: int + dropout: float = 0.0 + causal: bool = False + bias: bool = True + dtype: jnp.dtype = jnp.float32 + + def setup(self) -> None: + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {self.num_heads})." + ) + + dense = partial( + nn.Dense, + self.embed_dim, + dtype=self.dtype, + kernel_init=jax.nn.initializers.normal(self.config.init_std), + ) + + self.q_proj = dense(use_bias=self.bias) + self.k_proj = dense(use_bias=False) + self.v_proj = dense(use_bias=self.bias) + self.out_proj = dense(use_bias=self.bias) + + if self.causal: + self.causal_mask = make_causal_mask( + jnp.ones((1, self.config.max_target_positions), dtype="bool"), dtype="bool" + ) + + def __call__( + self, + hidden_states: jnp.ndarray, + key_value_states: Optional[jnp.ndarray] = None, + attention_mask: Optional[jnp.ndarray] = None, + init_cache: bool = False, + deterministic: bool = True, + ) -> Tuple[jnp.ndarray]: + is_cross_attention = key_value_states is not None + batch_size = hidden_states.shape[0] + + query_states = self.q_proj(hidden_states) + + if is_cross_attention: + key_states = self.k_proj(key_value_states) + value_states = self.v_proj(key_value_states) + else: + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = self._split_heads(query_states) + key_states = self._split_heads(key_states) + value_states = self._split_heads(value_states) + + if self.causal: + query_length, key_length = query_states.shape[1], key_states.shape[1] + if self.has_variable("cache", "cached_key"): + mask_shift = self.variables["cache"]["cache_index"] + max_decoder_length = self.variables["cache"]["cached_key"].shape[1] + causal_mask = lax.dynamic_slice( + self.causal_mask, + (0, 0, mask_shift, 0), + (1, 1, query_length, max_decoder_length), + ) + else: + causal_mask = self.causal_mask[:, :, :query_length, :key_length] + causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) + + # combine masks if needed + if attention_mask is not None and self.causal: + attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) + attention_mask = combine_masks(attention_mask, causal_mask) + elif self.causal: + attention_mask = causal_mask + elif attention_mask is not None: + attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) + + # During fast autoregressive decoding, we feed one position at a time, + # and cache the keys and values step by step. + + if self.causal and (self.has_variable("cache", "cached_key") or init_cache): + key_states, value_states, attention_mask = self._concatenate_to_cache( + key_states, value_states, query_states, attention_mask + ) + + # Convert the boolean attention mask to an attention bias. + if attention_mask is not None: + # attention mask in the form of attention bias + attention_bias = lax.select( + attention_mask > 0, + jnp.full(attention_mask.shape, 0.0).astype(self.dtype), + jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), + ) + else: + attention_bias = None + + dropout_rng = None + if not deterministic and self.dropout > 0.0: + dropout_rng = self.make_rng("dropout") + + attn_weights = dot_product_attention_weights( + query_states, + key_states, + bias=attention_bias, + dropout_rng=dropout_rng, + dropout_rate=self.dropout, + broadcast_dropout=True, + deterministic=deterministic, + dtype=self.dtype, + precision=None, + ) + + attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) + attn_output = self._merge_heads(attn_output) + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + def _split_heads(self, hidden_state) -> jnp.ndarray: + return hidden_state.reshape(hidden_state.shape[:2] + (self.num_heads, self.head_dim)) + + def _merge_heads(self, hidden_state) -> jnp.ndarray: + return hidden_state.reshape(hidden_state.shape[:2] + (self.embed_dim,)) + + @nn.compact + def _concatenate_to_cache(self, key, value, query, attention_mask) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + # detect if we're initializing by absence of existing cache data. + is_initialized = self.has_variable("cache", "cached_key") + cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) + cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) + cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) + + if is_initialized: + *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape + # update key, value caches with our new 1d spatial slices + cur_index = cache_index.value + indices = (0,) * len(batch_dims) + (cur_index, 0, 0) + key = lax.dynamic_update_slice(cached_key.value, key, indices) + value = lax.dynamic_update_slice(cached_value.value, value, indices) + cached_key.value = key + cached_value.value = value + num_updated_cache_vectors = query.shape[1] + cache_index.value = cache_index.value + num_updated_cache_vectors + # causal mask for cached decoder self-attention: our single query position should only + # attend to those key positions that have already been generated and cached, not the + # remaining zero elements. + pad_mask = jnp.broadcast_to( + jnp.arange(max_length) < cur_index + num_updated_cache_vectors, + tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), + ) + attention_mask = combine_masks(pad_mask, attention_mask) + + return key, value, attention_mask + + +# Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartEncoderLayer with MBart->Whisper +class FlaxWhisperEncoderLayer(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 + + def setup(self) -> None: + self.embed_dim = self.config.d_model + self.self_attn = FlaxWhisperAttention( + config=self.config, + embed_dim=self.embed_dim, + num_heads=self.config.encoder_attention_heads, + dropout=self.config.attention_dropout, + dtype=self.dtype, + ) + self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) + self.dropout_layer = nn.Dropout(rate=self.config.dropout) + self.activation_fn = ACT2FN[self.config.activation_function] + self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) + self.fc1 = nn.Dense( + self.config.encoder_ffn_dim, + dtype=self.dtype, + kernel_init=jax.nn.initializers.normal(self.config.init_std), + ) + self.fc2 = nn.Dense( + self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) + ) + self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) + + def __call__( + self, + hidden_states: jnp.ndarray, + attention_mask: jnp.ndarray, + output_attentions: bool = True, + deterministic: bool = True, + ) -> Tuple[jnp.ndarray]: + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask) + hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) + hidden_states = self.fc2(hidden_states) + hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class FlaxWhisperEncoderLayerCollection(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + gradient_checkpointing: bool = False + + def setup(self): + if self.gradient_checkpointing: + FlaxWhisperEncoderCheckpointLayer = remat(FlaxWhisperEncoderLayer, static_argnums=(2, 3)) + self.layers = [ + FlaxWhisperEncoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype) + for i in range(self.config.encoder_layers) + ] + else: + self.layers = [ + FlaxWhisperEncoderLayer(self.config, name=str(i), dtype=self.dtype) + for i in range(self.config.encoder_layers) + ] + self.layerdrop = self.config.encoder_layerdrop + + def __call__( + self, + hidden_states, + attention_mask, + deterministic: bool = True, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + all_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + for encoder_layer in self.layers: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = random.uniform(0, 1) + if not deterministic and (dropout_probability < self.layerdrop): # skip the layer + layer_outputs = (None, None) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + output_attentions, + deterministic, + ) + hidden_states = layer_outputs[0] + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + outputs = (hidden_states, all_hidden_states, all_attentions) + + if not return_dict: + return tuple(v for v in outputs if v is not None) + + return FlaxBaseModelOutput( + last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions + ) + + +# Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartDecoderLayer with MBart->Whisper +class FlaxWhisperDecoderLayer(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 + + def setup(self) -> None: + self.embed_dim = self.config.d_model + self.self_attn = FlaxWhisperAttention( + config=self.config, + embed_dim=self.embed_dim, + num_heads=self.config.decoder_attention_heads, + dropout=self.config.attention_dropout, + causal=True, + dtype=self.dtype, + ) + self.dropout_layer = nn.Dropout(rate=self.config.dropout) + self.activation_fn = ACT2FN[self.config.activation_function] + self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) + + self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) + self.encoder_attn = FlaxWhisperAttention( + config=self.config, + embed_dim=self.embed_dim, + num_heads=self.config.decoder_attention_heads, + dropout=self.config.attention_dropout, + dtype=self.dtype, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) + self.fc1 = nn.Dense( + self.config.decoder_ffn_dim, + dtype=self.dtype, + kernel_init=jax.nn.initializers.normal(self.config.init_std), + ) + self.fc2 = nn.Dense( + self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) + ) + self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) + + def __call__( + self, + hidden_states: jnp.ndarray, + attention_mask: jnp.ndarray, + encoder_hidden_states: Optional[jnp.ndarray] = None, + encoder_attention_mask: Optional[jnp.ndarray] = None, + init_cache: bool = False, + output_attentions: bool = True, + deterministic: bool = True, + ) -> Tuple[jnp.ndarray]: + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache + ) + hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) + hidden_states = residual + hidden_states + + # Cross-Attention Block + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + + hidden_states = self.encoder_attn_layer_norm(hidden_states) + hidden_states, cross_attn_weights = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + ) + hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) + hidden_states = self.fc2(hidden_states) + hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + return outputs + + +class FlaxWhisperDecoderLayerCollection(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + gradient_checkpointing: bool = False + + def setup(self): + if self.gradient_checkpointing: + FlaxWhisperDecoderCheckpointLayer = remat(FlaxWhisperDecoderLayer, static_argnums=(4, 5, 6)) + self.layers = [ + FlaxWhisperDecoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype) + for i in range(self.config.decoder_layers) + ] + else: + self.layers = [ + FlaxWhisperDecoderLayer(self.config, name=str(i), dtype=self.dtype) + for i in range(self.config.decoder_layers) + ] + self.layerdrop = self.config.decoder_layerdrop + + def __call__( + self, + hidden_states, + attention_mask, + encoder_hidden_states: Optional[jnp.ndarray] = None, + encoder_attention_mask: Optional[jnp.ndarray] = None, + deterministic: bool = True, + init_cache: bool = False, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = random.uniform(0, 1) + if not deterministic and (dropout_probability < self.layerdrop): + layer_outputs = (None, None, None) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + init_cache, + output_attentions, + deterministic, + ) + + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions] + + if not return_dict: + return tuple(v for v in outputs if v is not None) + + return FlaxBaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +class FlaxWhisperEncoder(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 + gradient_checkpointing: bool = False + + def setup(self) -> None: + self.conv1 = nn.Conv( + self.config.d_model, + kernel_size=(3,), + padding=1, + kernel_init=jax.nn.initializers.normal(self.config.init_std), + dtype=self.dtype, + ) + self.conv2 = nn.Conv( + self.config.d_model, + kernel_size=(3,), + strides=2, + padding=1, + kernel_init=jax.nn.initializers.normal(self.config.init_std), + dtype=self.dtype, + ) + + self.dropout_layer = nn.Dropout(rate=self.config.dropout) + + self.layers = FlaxWhisperEncoderLayerCollection( + self.config, + dtype=self.dtype, + gradient_checkpointing=self.gradient_checkpointing, + ) + + self.embed_positions = nn.Embed( + self.config.max_source_positions, + self.config.d_model, + dtype=self.dtype, + embedding_init=sinusoidal_embedding_init, + ) + + self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) + + def __call__( + self, + input_features: jnp.ndarray, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + deterministic: bool = True, + ) -> Tuple[jnp.ndarray]: + if input_features.shape[1:] != (self.config.num_mel_bins, self.config.max_source_positions * 2): + raise ValueError( + "input_features.shape[1:], must be equal to (self.config.num_mel_bins," + f" self.config.max_source_positions * 2) (got {input_features.shape[1:]}, but should be" + f" ({self.config.num_mel_bins}, {self.config.max_source_positions * 2}))" + ) + + input_features = input_features.transpose(0, 2, 1) + hidden_states = jax.nn.gelu(self.conv1(input_features), approximate=False) + hidden_states = jax.nn.gelu(self.conv2(hidden_states), approximate=False) + + embed_positions = self.embed_positions(jnp.arange(self.config.max_source_positions)) + # freeze the sinusoidal embeddings by stopping the back-prop + embed_positions = jax.lax.stop_gradient(embed_positions) + hidden_states = hidden_states + embed_positions + + hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) + + outputs = self.layers( + hidden_states, + attention_mask=None, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_states = outputs[0] + last_hidden_states = self.layer_norm(last_hidden_states) + + # update the last element in `hidden_states` after applying `layernorm` above + hidden_states = None + if output_hidden_states: + hidden_states = outputs[1] + hidden_states = hidden_states[:-1] + (last_hidden_states,) + + if not return_dict: + outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) + return tuple(v for v in outputs if v is not None) + + return FlaxBaseModelOutput( + last_hidden_state=last_hidden_states, + hidden_states=hidden_states, + attentions=outputs.attentions, + ) + + +class FlaxWhisperDecoder(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 + gradient_checkpointing: bool = False + + def setup(self) -> None: + self.embed_tokens = nn.Embed(self.config.vocab_size, self.config.d_model, dtype=self.dtype) + self.embed_positions = nn.Embed(self.config.max_target_positions, self.config.d_model, dtype=self.dtype) + + self.layers = FlaxWhisperDecoderLayerCollection( + self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing + ) + + self.dropout_layer = nn.Dropout(rate=self.config.dropout) + + self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-5) + + def __call__( + self, + input_ids: jnp.ndarray, + attention_mask: jnp.ndarray, + position_ids: jnp.ndarray, + encoder_hidden_states: Optional[jnp.ndarray] = None, + init_cache: bool = False, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + deterministic: bool = True, + ) -> Tuple[jnp.ndarray]: + input_embeds = self.embed_tokens(input_ids) + position_embeds = self.embed_positions(position_ids) + + hidden_states = input_embeds + position_embeds + hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) + + outputs = self.layers( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + deterministic=deterministic, + init_cache=init_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_states = outputs[0] + last_hidden_states = self.layer_norm(last_hidden_states) + + # update the last element in `hidden_states` after applying `layernorm` above + hidden_states = None + if output_hidden_states: + hidden_states = outputs[1] + hidden_states = hidden_states[:-1] + (last_hidden_states,) + + if not return_dict: + outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) + return tuple(v for v in outputs if v is not None) + + return FlaxBaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=last_hidden_states, + hidden_states=hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +class FlaxWhisperModule(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 + gradient_checkpointing: bool = False + + def setup(self) -> None: + self.encoder = FlaxWhisperEncoder( + self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing + ) + self.decoder = FlaxWhisperDecoder( + self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing + ) + + def __call__( + self, + input_features: jnp.ndarray, + decoder_input_ids: jnp.ndarray, + decoder_attention_mask: jnp.ndarray, + decoder_position_ids: jnp.ndarray, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + deterministic: bool = True, + ): + encoder_outputs = self.encoder( + input_features, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=deterministic, + ) + + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + position_ids=decoder_position_ids, + encoder_hidden_states=encoder_outputs[0], + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=deterministic, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return FlaxSeq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + def _get_encoder_module(self): + return self.encoder + + def _get_decoder_module(self): + return self.decoder + + +class FlaxWhisperPreTrainedModel(FlaxPreTrainedModel): + config_class = WhisperConfig + base_model_prefix: str = "model" + main_input_name = "input_features" + module_class: nn.Module = None + + def __init__( + self, + config: WhisperConfig, + input_shape: Tuple[int] = None, + seed: int = 0, + dtype: jnp.dtype = jnp.float32, + _do_init: bool = True, + gradient_checkpointing: bool = False, + **kwargs, + ): + module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs) + if input_shape is None: + input_shape = (1, config.num_mel_bins, 2 * config.max_source_positions) + super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) + + def enable_gradient_checkpointing(self): + self._module = self.module_class( + config=self.config, + dtype=self.dtype, + gradient_checkpointing=True, + ) + + def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: + # init input tensors + input_features = jnp.zeros(input_shape, dtype="f4") + input_features = input_features.at[(..., -1)].set(self.config.eos_token_id) + + decoder_input_ids = jnp.zeros((input_shape[0], 1), dtype="i4") + decoder_attention_mask = jnp.ones_like(decoder_input_ids) + + batch_size, sequence_length = decoder_input_ids.shape + decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) + + params_rng, dropout_rng = jax.random.split(rng) + rngs = {"params": params_rng, "dropout": dropout_rng} + + random_params = self.module.init( + rngs, + input_features=input_features, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + decoder_position_ids=decoder_position_ids, + )["params"] + + if params is not None: + random_params = flatten_dict(unfreeze(random_params)) + params = flatten_dict(unfreeze(params)) + for missing_key in self._missing_keys: + params[missing_key] = random_params[missing_key] + self._missing_keys = set() + return freeze(unflatten_dict(params)) + else: + return random_params + + # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->Whisper + def init_cache(self, batch_size, max_length, encoder_outputs): + r""" + Args: + batch_size (`int`): + batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. + max_length (`int`): + maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized + cache. + encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): + `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: + `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) + is a sequence of hidden-states at the output of the last layer of the encoder. Used in the + cross-attention of the decoder. + """ + # init input variables to retrieve cache + decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") + decoder_attention_mask = jnp.ones_like(decoder_input_ids) + decoder_position_ids = jnp.broadcast_to( + jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape + ) + + def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): + decoder_module = module._get_decoder_module() + return decoder_module( + decoder_input_ids, + decoder_attention_mask, + decoder_position_ids, + **kwargs, + ) + + init_variables = self.module.init( + jax.random.PRNGKey(0), + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + decoder_position_ids=decoder_position_ids, + encoder_hidden_states=encoder_outputs[0], + init_cache=True, + method=_decoder_forward, # we only need to call the decoder to init the cache + ) + return unfreeze(init_variables["cache"]) + + @add_start_docstrings(WHISPER_ENCODE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=WhisperConfig) + def encode( + self, + input_features: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + **kwargs, + ): + r""" + Returns: + + Example: + + ```python + >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration + >>> from datasets import load_dataset + + >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True) + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np") + >>> input_features = inputs.input_features + >>> encoder_outputs = model.encode(input_features=input_features) + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + def _encoder_forward(module, input_features, **kwargs): + encode_module = module._get_encoder_module() + return encode_module(input_features, **kwargs) + + return self.module.apply( + {"params": params or self.params}, + input_features=jnp.array(input_features, dtype="f4"), + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=not train, + rngs=rngs, + method=_encoder_forward, + ) + + @add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=WhisperConfig) + def decode( + self, + decoder_input_ids, + encoder_outputs, + encoder_attention_mask: Optional[jnp.ndarray] = None, + decoder_attention_mask: Optional[jnp.ndarray] = None, + decoder_position_ids: Optional[jnp.ndarray] = None, + past_key_values: dict = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + ): + r""" + Returns: + + Example: + + ```python + >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration + >>> from datasets import load_dataset + >>> import jax.numpy as jnp + + >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True) + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> input_features = processor(ds[0]["audio"]["array"], return_tensors="np").input_features + + >>> encoder_outputs = model.encode(input_features=input_features) + >>> decoder_start_token_id = model.config.decoder_start_token_id + + >>> decoder_input_ids = jnp.ones((input_features.shape[0], 1), dtype="i4") * decoder_start_token_id + + >>> outputs = model.decode(decoder_input_ids, encoder_outputs) + >>> last_decoder_hidden_states = outputs.last_hidden_state + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + encoder_hidden_states = encoder_outputs[0] + + batch_size, sequence_length = decoder_input_ids.shape + if decoder_position_ids is None: + if past_key_values is not None: + raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") + + if decoder_attention_mask is not None: + decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1 + else: + decoder_position_ids = jnp.broadcast_to( + jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) + ) + + if decoder_attention_mask is None: + decoder_attention_mask = jnp.ones((batch_size, sequence_length)) + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + inputs = {"params": params or self.params} + + # if past_key_values are passed then cache is already initialized a private flag init_cache has to be + # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that + # it can be changed by FlaxWhisperAttention module + if past_key_values: + inputs["cache"] = past_key_values + mutable = ["cache"] + else: + mutable = False + + def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): + decoder_module = module._get_decoder_module() + return decoder_module( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + position_ids=decoder_position_ids, + **kwargs, + ) + + outputs = self.module.apply( + inputs, + decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), + decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), + decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), + encoder_hidden_states=encoder_hidden_states, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=not train, + rngs=rngs, + mutable=mutable, + method=_decoder_forward, + ) + + # add updated cache to model output + if past_key_values is not None and return_dict: + outputs, past = outputs + outputs["past_key_values"] = unfreeze(past["cache"]) + return outputs + elif past_key_values is not None and not return_dict: + outputs, past = outputs + outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] + + return outputs + + @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) + def __call__( + self, + input_features: jnp.ndarray, + decoder_input_ids: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + decoder_attention_mask: Optional[jnp.ndarray] = None, + position_ids: Optional[jnp.ndarray] = None, + decoder_position_ids: Optional[jnp.ndarray] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # prepare decoder inputs + if decoder_position_ids is None: + if decoder_attention_mask is not None: + decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1 + else: + batch_size, sequence_length = decoder_input_ids.shape + decoder_position_ids = jnp.broadcast_to( + jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) + ) + if decoder_attention_mask is None: + decoder_attention_mask = jnp.ones_like(decoder_input_ids) + + # Handle any PRNG if needed + rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} + + return self.module.apply( + {"params": params or self.params}, + input_features=jnp.array(input_features, dtype="f4"), + decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), + decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), + decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=not train, + rngs=rngs, + ) + + +@add_start_docstrings( + "The bare Whisper Model transformer outputting raw hidden-states without any specific head on top.", + WHISPER_START_DOCSTRING, +) +class FlaxWhisperModel(FlaxWhisperPreTrainedModel): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + module_class = FlaxWhisperModule + + +append_call_sample_docstring(FlaxWhisperModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC) + + +class FlaxWhisperForConditionalGenerationModule(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 + gradient_checkpointing: bool = False + + def setup(self) -> None: + self.model = FlaxWhisperModule( + config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing + ) + self.lm_head = nn.Dense( + self.config.vocab_size, + use_bias=False, + dtype=self.dtype, + kernel_init=jax.nn.initializers.normal(self.config.init_std), + ) + + def _get_encoder_module(self): + return self.model.encoder + + def _get_decoder_module(self): + return self.model.decoder + + def __call__( + self, + input_features, + decoder_input_ids, + decoder_attention_mask: jnp.ndarray = None, + decoder_position_ids: jnp.ndarray = None, + position_ids: jnp.ndarray = None, + attention_mask: jnp.ndarray = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + deterministic: bool = True, + ): + outputs = self.model( + input_features=input_features, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + decoder_position_ids=decoder_position_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=deterministic, + ) + + hidden_states = outputs[0] + + if self.config.tie_word_embeddings: + shared_embedding = self.model.decoder.embed_tokens.variables["params"]["embedding"] + lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) + else: + lm_logits = self.lm_head(hidden_states) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return output + + return FlaxSeq2SeqLMOutput( + logits=lm_logits, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + +@add_start_docstrings("The Whisper Model with a language modeling head.", WHISPER_START_DOCSTRING) +class FlaxWhisperForConditionalGeneration(FlaxWhisperPreTrainedModel): + module_class = FlaxWhisperForConditionalGenerationModule + dtype: jnp.dtype = jnp.float32 + + @add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=WhisperConfig) + def decode( + self, + decoder_input_ids, + encoder_outputs, + encoder_attention_mask: Optional[jnp.ndarray] = None, + decoder_attention_mask: Optional[jnp.ndarray] = None, + decoder_position_ids: Optional[jnp.ndarray] = None, + past_key_values: dict = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + ): + r""" + Returns: + + Example: + + ```python + >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration + >>> from datasets import load_dataset + + >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True) + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np") + >>> input_features = inputs.input_features + >>> encoder_outputs = model.encode(input_features=input_features) + >>> decoder_start_token_id = model.config.decoder_start_token_id + + >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id + + >>> outputs = model.decode(decoder_input_ids, encoder_outputs) + >>> last_decoder_hidden_states = outputs.last_hidden_state + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + encoder_hidden_states = encoder_outputs[0] + + batch_size, sequence_length = decoder_input_ids.shape + if decoder_position_ids is None: + if past_key_values is not None: + raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") + + if decoder_attention_mask is not None: + decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1 + else: + decoder_position_ids = jnp.broadcast_to( + jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) + ) + if decoder_attention_mask is None: + decoder_attention_mask = jnp.ones((batch_size, sequence_length), dtype="i4") + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + inputs = {"params": params or self.params} + + # if past_key_values are passed then cache is already initialized a private flag init_cache has to be + # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that + # it can be changed by FlaxWhisperAttention module + if past_key_values: + inputs["cache"] = past_key_values + mutable = ["cache"] + else: + mutable = False + + def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): + decoder_module = module._get_decoder_module() + outputs = decoder_module( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + position_ids=decoder_position_ids, + **kwargs, + ) + hidden_states = outputs[0] + + if self.config.tie_word_embeddings: + shared_embedding = module.model.decoder.embed_tokens.variables["params"]["embedding"] + lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) + else: + lm_logits = module.lm_head(hidden_states) + + return lm_logits, outputs + + outputs = self.module.apply( + inputs, + decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), + decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), + decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), + encoder_hidden_states=encoder_hidden_states, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=not train, + rngs=rngs, + mutable=mutable, + method=_decoder_forward, + ) + + if past_key_values is None: + lm_logits, decoder_outputs = outputs + else: + (lm_logits, decoder_outputs), past = outputs + + if return_dict: + outputs = FlaxCausalLMOutputWithCrossAttentions( + logits=lm_logits, + hidden_states=decoder_outputs.hidden_states, + attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + ) + else: + outputs = (lm_logits,) + decoder_outputs[1:] + + # add updated cache to model output + if past_key_values is not None and return_dict: + outputs["past_key_values"] = unfreeze(past["cache"]) + return outputs + elif past_key_values is not None and not return_dict: + outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] + + return outputs + + def generate( + self, + input_features, + generation_config=None, + logits_processor=None, + return_timestamps=None, + task=None, + language=None, + is_multilingual=None, + **kwargs, + ): + if generation_config is None: + generation_config = self.generation_config + + if return_timestamps is not None: + generation_config.return_timestamps = return_timestamps + + if task is not None: + generation_config.task = task + + if is_multilingual is not None: + generation_config.is_multilingual = is_multilingual + + if language is not None: + generation_config.language = language + + if kwargs is not None and "decoder_input_ids" in kwargs: + decoder_input_length = len(kwargs["decoder_input_ids"]) + else: + decoder_input_length = 1 + + forced_decoder_ids = [] + + if hasattr(generation_config, "is_multilingual") and generation_config.is_multilingual: + if hasattr(generation_config, "language"): + forced_decoder_ids.append((1, generation_config.lang_to_id[generation_config.language])) + else: + forced_decoder_ids.append((1, None)) + + if hasattr(generation_config, "task"): + forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task])) + else: + forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"])) + + if ( + hasattr(generation_config, "return_timestamps") and generation_config.return_timestamps + ) or return_timestamps: + logits_processor = [ + FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, decoder_input_length) + ] + else: + if forced_decoder_ids and forced_decoder_ids[-1][0] != generation_config.no_timestamps_token_id: + idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1 + forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id)) + + if len(forced_decoder_ids) > 0: + generation_config.forced_decoder_ids = forced_decoder_ids + + return super().generate( + input_features, + generation_config, + logits_processor=logits_processor, + **kwargs, + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + max_length, + attention_mask: Optional[jax.Array] = None, + decoder_attention_mask: Optional[jax.Array] = None, + encoder_outputs=None, + **kwargs, + ): + # initializing the cache + batch_size, seq_length = decoder_input_ids.shape + + past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) + # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. + # But since the decoder uses a causal mask, those positions are masked anyways. + # Thus we can create a single static attention_mask here, which is more efficient for compilation + extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") + if decoder_attention_mask is not None: + position_ids = decoder_attention_mask.cumsum(-1) - 1 + extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) + else: + position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) + + return { + "past_key_values": past_key_values, + "encoder_outputs": encoder_outputs, + "encoder_attention_mask": attention_mask, + "decoder_attention_mask": extended_attention_mask, + "decoder_position_ids": position_ids, + } + + def update_inputs_for_generation(self, model_outputs, model_kwargs): + model_kwargs["past_key_values"] = model_outputs.past_key_values + model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 + return model_kwargs + + +FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING = r""" + Returns: + + Transcription example: + + ```python + >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration + >>> from datasets import load_dataset + + >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True) + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np") + >>> input_features = inputs.input_features + >>> generated_ids = model.generate(input_ids=input_features) + >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + >>> transcription + ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' + ``` +""" + +overwrite_call_docstring( + FlaxWhisperForConditionalGeneration, WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING +) +append_replace_return_docstrings( + FlaxWhisperForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC +) + + +class FlaxWhisperForAudioClassificationModule(nn.Module): + config: WhisperConfig + dtype: jnp.dtype = jnp.float32 + gradient_checkpointing: bool = False + + def setup(self) -> None: + self.encoder = FlaxWhisperEncoder( + config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing + ) + self.config.is_encoder_decoder = False + num_layers = self.config.num_hidden_layers + 1 + if self.config.use_weighted_layer_sum: + self.layer_weights = jnp.repeat(1 / num_layers, num_layers) + self.projector = nn.Dense(self.config.classifier_proj_size, dtype=self.dtype) + self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) + + def __call__( + self, + input_features, + encoder_outputs=None, + output_attentions=None, + output_hidden_states: bool = True, + return_dict: bool = True, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_features, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = jnp.stack(encoder_outputs, axis=1) + norm_weights = jax.nn.softmax(self.layer_weights, axis=-1) + hidden_states = jnp.sum(hidden_states * jnp.reshape(norm_weights, [-1, 1, 1]), axis=1) + else: + hidden_states = encoder_outputs[0] + + hidden_states = self.projector(hidden_states) + pooled_output = jnp.mean(hidden_states, axis=1) + + logits = self.classifier(pooled_output) + + if not return_dict: + return (logits,) + encoder_outputs[1:] + + return FlaxSequenceClassifierOutput( + logits=logits, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings("The Whisper Model with an audio classification head on top.", WHISPER_START_DOCSTRING) +class FlaxWhisperForAudioClassification(FlaxWhisperPreTrainedModel): + module_class = FlaxWhisperForAudioClassificationModule + dtype: jnp.dtype = jnp.float32 + + def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: + # init input tensors + input_features = jnp.zeros(input_shape, dtype="f4") + input_features = input_features.at[(..., -1)].set(self.config.eos_token_id) + + params_rng, dropout_rng = jax.random.split(rng) + rngs = {"params": params_rng, "dropout": dropout_rng} + + random_params = self.module.init( + rngs, + input_features=input_features, + )["params"] + + if params is not None: + random_params = flatten_dict(unfreeze(random_params)) + params = flatten_dict(unfreeze(params)) + for missing_key in self._missing_keys: + params[missing_key] = random_params[missing_key] + self._missing_keys = set() + return freeze(unflatten_dict(params)) + else: + return random_params + + @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) + def __call__( + self, + input_features: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + **kwargs, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + return self.module.apply( + {"params": params or self.params}, + input_features=jnp.array(input_features, dtype="f4"), + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + rngs=rngs, + ) + + +FLAX_WHISPER_AUDIO_CLASSIFICATION_DOCSTRING = r""" + Returns: + + Transcription example: + + ```python + >>> import jax.numpy as jnp + >>> from transformers import AutoFeatureExtractor, FlaxWhisperForAudioClassification + >>> from datasets import load_dataset + + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id") + >>> model = FlaxWhisperForAudioClassification.from_pretrained( + ... "sanchit-gandhi/whisper-medium-fleurs-lang-id", from_pt=True + ... ) + >>> ds = load_dataset("google/fleurs", "all", split="validation", streaming=True) + + >>> sample = next(iter(ds)) + + >>> inputs = feature_extractor( + ... sample["audio"]["array"], sampling_rate=sample["audio"]["sampling_rate"], return_tensors="np" + ... ) + >>> input_features = inputs.input_features + + >>> logits = model(input_features).logits + + >>> predicted_class_ids = jnp.argmax(logits).item() + >>> predicted_label = model.config.id2label[predicted_class_ids] + >>> predicted_label + 'af_za' + ``` +""" + +overwrite_call_docstring( + FlaxWhisperForAudioClassification, WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_AUDIO_CLASSIFICATION_DOCSTRING +) +append_replace_return_docstrings( + FlaxWhisperForAudioClassification, output_type=FlaxSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC +) diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_tf_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_tf_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..4d5dda71e8aaf3e14c3575c9f7e2e60e58cba821 --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_tf_whisper.py @@ -0,0 +1,1762 @@ +# coding=utf-8 +# Copyright 2022 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" TensorFlow Whisper model.""" + + +from __future__ import annotations + +import math +import random +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import tensorflow as tf + +from ...activations_tf import get_tf_activation +from ...generation.configuration_utils import GenerationConfig +from ...generation.tf_logits_process import TFLogitsProcessorList +from ...modeling_tf_outputs import ( + TFBaseModelOutput, + TFBaseModelOutputWithPastAndCrossAttentions, + TFSeq2SeqLMOutput, + TFSeq2SeqModelOutput, +) +from ...modeling_tf_utils import ( + TFCausalLanguageModelingLoss, + TFModelInputType, + TFPreTrainedModel, + keras, + keras_serializable, + unpack_inputs, +) +from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_whisper import WhisperConfig +from .tokenization_whisper import TASK_IDS, TO_LANGUAGE_CODE + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "WhisperConfig" + + +from ..deprecated._archive_maps import TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +LARGE_NEGATIVE = -1e8 + + +def sinusoidal_embedding_init(shape, dtype=tf.float32) -> tf.Tensor: + """Returns sinusoids for positional embedding""" + length, channels = shape + if channels % 2 != 0: + raise ValueError( + f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels." + ) + log_timescale_increment = math.log(10000) / (channels // 2 - 1) + inv_timescales = tf.exp(-log_timescale_increment * tf.range(channels // 2, dtype=tf.float32)) + scaled_time = tf.reshape(tf.range(length, dtype=tf.float32), (-1, 1)) * tf.reshape(inv_timescales, (1, -1)) + return tf.cast(tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1), dtype) + + +# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right +def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): + pad_token_id = tf.cast(pad_token_id, input_ids.dtype) + decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype) + start_tokens = tf.fill( + (shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype) + ) + shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids = tf.where( + shifted_input_ids == -100, + tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)), + shifted_input_ids, + ) + + # "Verify that `labels` has only positive values and -100" + assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype)) + + # Make sure the assertion op is called by wrapping the result in an identity no-op + with tf.control_dependencies([assert_gte0]): + shifted_input_ids = tf.identity(shifted_input_ids) + + return shifted_input_ids + + +# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask +def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz = input_ids_shape[0] + tgt_len = input_ids_shape[1] + mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE + mask_cond = tf.range(shape_list(mask)[-1]) + + mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask) + + if past_key_values_length > 0: + mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1) + + return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1)) + + +# Copied from transformers.models.bart.modeling_tf_bart._expand_mask +def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + src_len = shape_list(mask)[1] + tgt_len = tgt_len if tgt_len is not None else src_len + one_cst = tf.constant(1.0) + mask = tf.cast(mask, dtype=one_cst.dtype) + expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1)) + + return (one_cst - expanded_mask) * LARGE_NEGATIVE + + +class TFWhisperPositionalEmbedding(keras.layers.Layer): + def __init__( + self, + num_positions: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + embedding_initializer=None, + **kwargs, + ): + super().__init__(**kwargs) + self.num_positions = num_positions + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.embedding_initializer = keras.initializers.get(embedding_initializer) + + def build(self, input_shape): + self.weight = self.add_weight( + name="weight", + shape=[self.num_positions, self.embedding_dim], + initializer=self.embedding_initializer, + trainable=True, + ) + super().build(input_shape) + + def call(self, input_ids, past_key_values_length=0): + past_key_values_length = tf.cast(past_key_values_length, tf.int32) + gather_indices = tf.range(tf.shape(input_ids)[1], delta=1) + past_key_values_length + return tf.gather(self.weight, gather_indices) + + +class TFWhisperAttention(keras.layers.Layer): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + **kwargs, + ): + super().__init__(**kwargs) + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = keras.layers.Dropout(dropout) + self.head_dim = embed_dim // num_heads + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + + self.k_proj = keras.layers.Dense(embed_dim, use_bias=False, name="k_proj") + self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj") + self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj") + self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj") + + # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention._shape with BART->whisper + def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): + return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3)) + + # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention.call with BART->whisper + def call( + self, + hidden_states: tf.Tensor, + key_value_states: tf.Tensor | None = None, + past_key_value: Tuple[Tuple[tf.Tensor]] | None = None, + attention_mask: tf.Tensor | None = None, + layer_head_mask: tf.Tensor | None = None, + training: Optional[bool] = False, + ) -> Tuple[tf.Tensor, tf.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + bsz, tgt_len, embed_dim = shape_list(hidden_states) + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = tf.concat([past_key_value[0], key_states], axis=2) + value_states = tf.concat([past_key_value[1], value_states], axis=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape) + key_states = tf.reshape(key_states, proj_shape) + value_states = tf.reshape(value_states, proj_shape) + + src_len = shape_list(key_states)[1] + attn_weights = tf.matmul(query_states, key_states, transpose_b=True) + + tf.debugging.assert_equal( + shape_list(attn_weights), + [bsz * self.num_heads, tgt_len, src_len], + message=( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {shape_list(attn_weights)}" + ), + ) + + if attention_mask is not None: + tf.debugging.assert_equal( + shape_list(attention_mask), + [bsz, 1, tgt_len, src_len], + message=( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" + f" {shape_list(attention_mask)}" + ), + ) + + attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype) + attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask + attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) + + attn_weights = stable_softmax(attn_weights, axis=-1) + + if layer_head_mask is not None: + tf.debugging.assert_equal( + shape_list(layer_head_mask), + [self.num_heads], + message=( + f"Head mask for a single layer should be of size {(self.num_heads)}, but is" + f" {shape_list(layer_head_mask)}" + ), + ) + + attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape( + attn_weights, (bsz, self.num_heads, tgt_len, src_len) + ) + attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) + + attn_probs = self.dropout(attn_weights, training=training) + attn_output = tf.matmul(attn_probs, value_states) + + tf.debugging.assert_equal( + shape_list(attn_output), + [bsz * self.num_heads, tgt_len, self.head_dim], + message=( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {shape_list(attn_output)}" + ), + ) + + attn_output = tf.transpose( + tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3) + ) + attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim)) + + attn_output = self.out_proj(attn_output) + attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + + return attn_output, attn_weights, past_key_value + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "k_proj", None) is not None: + with tf.name_scope(self.k_proj.name): + self.k_proj.build([None, None, self.embed_dim]) + if getattr(self, "v_proj", None) is not None: + with tf.name_scope(self.v_proj.name): + self.v_proj.build([None, None, self.embed_dim]) + if getattr(self, "q_proj", None) is not None: + with tf.name_scope(self.q_proj.name): + self.q_proj.build([None, None, self.embed_dim]) + if getattr(self, "out_proj", None) is not None: + with tf.name_scope(self.out_proj.name): + self.out_proj.build([None, None, self.embed_dim]) + + +# Copied from transformers.models.speech_to_text.modeling_tf_speech_to_text.TFSpeech2TextEncoderLayer with Speech2Text->Whisper +class TFWhisperEncoderLayer(keras.layers.Layer): + def __init__(self, config: WhisperConfig, **kwargs): + super().__init__(**kwargs) + self.embed_dim = config.d_model + self.self_attn = TFWhisperAttention( + self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn" + ) + self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") + self.dropout = keras.layers.Dropout(config.dropout) + self.activation_fn = get_tf_activation(config.activation_function) + self.activation_dropout = keras.layers.Dropout(config.activation_dropout) + self.fc1 = keras.layers.Dense(config.encoder_ffn_dim, name="fc1") + self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2") + self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") + self.config = config + + def call( + self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: bool = False + ): + """ + Args: + hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`tf.Tensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)` + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, self_attn_weights, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + training=training, + ) + + tf.debugging.assert_equal( + shape_list(hidden_states), + shape_list(residual), + message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}", + ) + + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = self.activation_dropout(hidden_states, training=training) + hidden_states = self.fc2(hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = residual + hidden_states + + return hidden_states, self_attn_weights + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "self_attn", None) is not None: + with tf.name_scope(self.self_attn.name): + self.self_attn.build(None) + if getattr(self, "self_attn_layer_norm", None) is not None: + with tf.name_scope(self.self_attn_layer_norm.name): + self.self_attn_layer_norm.build([None, None, self.embed_dim]) + if getattr(self, "fc1", None) is not None: + with tf.name_scope(self.fc1.name): + self.fc1.build([None, None, self.embed_dim]) + if getattr(self, "fc2", None) is not None: + with tf.name_scope(self.fc2.name): + self.fc2.build([None, None, self.config.encoder_ffn_dim]) + if getattr(self, "final_layer_norm", None) is not None: + with tf.name_scope(self.final_layer_norm.name): + self.final_layer_norm.build([None, None, self.embed_dim]) + + +# Copied from transformers.models.speech_to_text.modeling_tf_speech_to_text.TFSpeech2TextDecoderLayer with Speech2Text->Whisper +class TFWhisperDecoderLayer(keras.layers.Layer): + def __init__(self, config: WhisperConfig, **kwargs): + super().__init__(**kwargs) + self.embed_dim = config.d_model + + self.self_attn = TFWhisperAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + name="self_attn", + is_decoder=True, + ) + self.dropout = keras.layers.Dropout(config.dropout) + self.activation_fn = get_tf_activation(config.activation_function) + self.activation_dropout = keras.layers.Dropout(config.activation_dropout) + + self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") + self.encoder_attn = TFWhisperAttention( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + name="encoder_attn", + is_decoder=True, + ) + self.encoder_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm") + self.fc1 = keras.layers.Dense(config.decoder_ffn_dim, name="fc1") + self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2") + self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") + self.config = config + + def call( + self, + hidden_states, + attention_mask: tf.Tensor | None = None, + encoder_hidden_states: tf.Tensor | None = None, + encoder_attention_mask: tf.Tensor | None = None, + layer_head_mask: tf.Tensor | None = None, + cross_attn_layer_head_mask: tf.Tensor | None = None, + past_key_value: Tuple[tf.Tensor] | None = None, + training=False, + ) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]: + """ + Args: + hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`tf.Tensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`tf.Tensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`tf.Tensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size + `(decoder_attention_heads,)` + cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module. + `(decoder_attention_heads,)` + past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + # add present self-attn cache to positions 1,2 of present_key_value tuple + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=self_attn_past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + training=training, + ) + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = residual + hidden_states + + # Cross-Attention Block + cross_attn_present_key_value = None + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + layer_head_mask=cross_attn_layer_head_mask, + past_key_value=cross_attn_past_key_value, + training=training, + ) + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = residual + hidden_states + + # add cross-attn to positions 3,4 of present_key_value tuple + present_key_value = present_key_value + cross_attn_present_key_value + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = self.activation_dropout(hidden_states, training=training) + hidden_states = self.fc2(hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = residual + hidden_states + + return ( + hidden_states, + self_attn_weights, + cross_attn_weights, + present_key_value, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "self_attn", None) is not None: + with tf.name_scope(self.self_attn.name): + self.self_attn.build(None) + if getattr(self, "self_attn_layer_norm", None) is not None: + with tf.name_scope(self.self_attn_layer_norm.name): + self.self_attn_layer_norm.build([None, None, self.embed_dim]) + if getattr(self, "encoder_attn", None) is not None: + with tf.name_scope(self.encoder_attn.name): + self.encoder_attn.build(None) + if getattr(self, "encoder_attn_layer_norm", None) is not None: + with tf.name_scope(self.encoder_attn_layer_norm.name): + self.encoder_attn_layer_norm.build([None, None, self.embed_dim]) + if getattr(self, "fc1", None) is not None: + with tf.name_scope(self.fc1.name): + self.fc1.build([None, None, self.embed_dim]) + if getattr(self, "fc2", None) is not None: + with tf.name_scope(self.fc2.name): + self.fc2.build([None, None, self.config.decoder_ffn_dim]) + if getattr(self, "final_layer_norm", None) is not None: + with tf.name_scope(self.final_layer_norm.name): + self.final_layer_norm.build([None, None, self.embed_dim]) + + +class TFWhisperPreTrainedModel(TFPreTrainedModel): + config_class = WhisperConfig + base_model_prefix = "model" + main_input_name = "input_features" + + def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor) -> int: + """ + Computes the output length of the convolutional layers + """ + input_lengths = (input_lengths - 1) // 2 + 1 + + return input_lengths + + @property + def dummy_inputs(self) -> Dict[str, tf.Tensor]: + """ + Dummy inputs to build the network. + + Returns: + `Dict[str, tf.Tensor]`: The dummy inputs. + """ + return { + self.main_input_name: tf.random.uniform( + [1, self.config.num_mel_bins, self.config.max_source_positions * 2 - 1], dtype=tf.float32 + ), + "decoder_input_ids": tf.constant([[1, 3]], dtype=tf.int32), + } + + @property + def input_signature(self): + return { + "input_features": tf.TensorSpec((None, self.config.num_mel_bins, None), tf.float32, name="input_features"), + "decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"), + "decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"), + } + + +WHISPER_START_DOCSTRING = r""" + This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it + as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`WhisperConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. +""" + +WHISPER_INPUTS_DOCSTRING = r""" + Args: + input_features (`tf.Tensor` of shape `(batch_size, feature_size, sequence_length)`): + Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained + by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* + via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the + [`AutoFeatureExtractor`] should be used for extracting the fbank features, padding and conversion into a + tensor of type `tf.Tensor`. See [`~WhisperFeatureExtractor.__call__`] + decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`SpeechToTextTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + SpeechToText uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read + [`modeling_whisper._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the + paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. + head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. + past_key_values (`tuple(tuple(tf.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(tf.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + decoder_inputs_embeds (`tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded + representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be + input (see `past_key_values`). This is useful if you want more control over how to convert + `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@keras_serializable +class TFWhisperEncoder(keras.layers.Layer): + config_class = WhisperConfig + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`TFWhisperEncoderLayer`]. + + Args: + config: WhisperConfig + embed_tokens (TFWhisperEmbedding): output embedding + """ + + def __init__(self, config: WhisperConfig, **kwargs): + super().__init__(**kwargs) + self.config = config + self.layerdrop = config.encoder_layerdrop + + self.embed_dim = config.d_model + self.num_mel_bins = config.num_mel_bins + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_source_positions + self.embed_scale = math.sqrt(self.embed_dim) if config.scale_embedding else 1.0 + + # Padding is added in call() to match the PyTorch implementation + self.conv1 = keras.layers.Conv1D(self.embed_dim, kernel_size=3, strides=1, padding="valid", name="conv1") + self.conv2 = keras.layers.Conv1D(self.embed_dim, kernel_size=3, strides=2, padding="valid", name="conv2") + + self.embed_positions = TFWhisperPositionalEmbedding( + num_positions=self.max_source_positions, + embedding_dim=self.embed_dim, + embedding_initializer=sinusoidal_embedding_init, + name="embed_positions", + ) + self.embed_positions.trainable = False + + self.encoder_layers = [TFWhisperEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)] + self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") + + self.dropout = keras.layers.Dropout(config.dropout) + + @unpack_inputs + def call( + self, + input_features=None, + head_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + training=False, + ): + r""" + Args: + input_features (`tf.Tensor` of shape `(batch_size, feature_size, sequence_length)`): + Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be + obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a + `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into + `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the fbank features, + padding and conversion into a tensor of type `tf.Tensor`. See [`~WhisperFeatureExtractor.__call__`] + head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # TF 2.0 layers can't use channels first format when running on CPU. + input_features = tf.transpose(input_features, perm=(0, 2, 1)) + input_features = tf.pad(input_features, [[0, 0], [1, 1], [0, 0]]) + inputs_embeds = keras.activations.gelu(self.conv1(input_features)) + inputs_embeds = tf.pad(inputs_embeds, [[0, 0], [1, 1], [0, 0]]) + inputs_embeds = keras.activations.gelu(self.conv2(inputs_embeds)) + inputs_embeds = tf.transpose(inputs_embeds, perm=(0, 1, 2)) + + embed_pos = self.embed_positions(input_ids=tf.zeros((1, self.max_source_positions), dtype=tf.int32)) + + hidden_states = inputs_embeds + embed_pos + hidden_states = self.dropout(hidden_states, training=training) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + # check if head_mask has a correct number of layers specified if desired + if head_mask is not None: + tf.debugging.assert_equal( + shape_list(head_mask)[0], + len(self.encoder_layers), + message=( + f"The head_mask should be specified for {len(self.encoder_layers)} layers, but it is for" + f" {shape_list(head_mask)[0]}." + ), + ) + + for idx, encoder_layer in enumerate(self.encoder_layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = random.uniform(0, 1) + if training and (dropout_probability < self.layerdrop): # skip the layer + continue + + hidden_states, attn = encoder_layer( + hidden_states, + None, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + training=training, + ) + + if output_attentions: + all_attentions += (attn,) + + hidden_states = self.layer_norm(hidden_states) + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return TFBaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "conv1", None) is not None: + with tf.name_scope(self.conv1.name): + self.conv1.build([None, None, self.num_mel_bins]) + if getattr(self, "conv2", None) is not None: + with tf.name_scope(self.conv2.name): + self.conv2.build([None, None, self.embed_dim]) + if getattr(self, "embed_positions", None) is not None: + with tf.name_scope(self.embed_positions.name): + self.embed_positions.build(None) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.config.d_model]) + if getattr(self, "encoder_layers", None) is not None: + for layer in self.encoder_layers: + with tf.name_scope(layer.name): + layer.build(None) + + +@keras_serializable +class TFWhisperDecoder(keras.layers.Layer): + config_class = WhisperConfig + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFWhisperDecoderLayer`] + + Args: + config: WhisperConfig + """ + + def __init__(self, config: WhisperConfig, **kwargs): + super().__init__(**kwargs) + self.config = config + self.dropout = keras.layers.Dropout(config.dropout) + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_target_positions + self.max_source_positions = config.max_source_positions + self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = keras.layers.Embedding( + input_dim=config.vocab_size, + output_dim=config.d_model, + embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std), + name="embed_tokens", + ) + self.embed_positions = TFWhisperPositionalEmbedding( + self.max_target_positions, config.d_model, name="embed_positions" + ) + + self.decoder_layers = [TFWhisperDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)] + + self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + batch_size, seq_len = input_shape[0], input_shape[1] + + combined_attention_mask = tf.cond( + tf.math.greater(seq_len, 1), + lambda: _make_causal_mask(input_shape, past_key_values_length=past_key_values_length), + lambda: _expand_mask(tf.ones((batch_size, seq_len + past_key_values_length)), tgt_len=seq_len), + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, tgt_len=input_shape[-1]) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + return combined_attention_mask + + @unpack_inputs + def call( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + encoder_hidden_states=None, + head_mask=None, + cross_attn_head_mask=None, + past_key_values=None, + inputs_embeds=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + training=False, + ): + r""" + Args: + input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the + range `[0, config.max_position_embeddings - 1]`. + encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention + on hidden heads. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(tf.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(tf.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input_shape = tf.shape(input_ids) + input_ids = tf.reshape(input_ids, (-1, input_shape[-1])) + elif inputs_embeds is not None: + input_shape = tf.shape(inputs_embeds)[:-1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + # past_key_values_length + past_key_values_length = tf.shape(past_key_values[0][0])[2] if past_key_values is not None else 0 + + if inputs_embeds is None: + check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim) + inputs_embeds = self.embed_tokens(input_ids) + + attention_mask = self._prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values_length) + + # embed positions + filled_past_positions = past_key_values_length if position_ids is None else position_ids[0, -1] + positions = self.embed_positions(input_ids, past_key_values_length=filled_past_positions) + + hidden_states = inputs_embeds + positions + hidden_states = self.dropout(hidden_states, training=training) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + next_decoder_cache = () if use_cache else None + + # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired + for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]: + if attn_mask is not None: + tf.debugging.assert_equal( + shape_list(attn_mask)[0], + len(self.decoder_layers), + message=( + f"The {attn_mask_name} should be specified for {len(self.decoder_layers)} layers, but it is" + f" for {shape_list(attn_mask)[0]}." + ), + ) + + for idx, decoder_layer in enumerate(self.decoder_layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + dropout_probability = random.uniform(0, 1) + if training and (dropout_probability < self.layerdrop): + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None), + past_key_value=past_key_value, + training=training, + ) + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[3],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + hidden_states = self.layer_norm(hidden_states) + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return TFBaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "embed_tokens", None) is not None: + with tf.name_scope(self.embed_tokens.name): + self.embed_tokens.build(None) + if getattr(self, "embed_positions", None) is not None: + with tf.name_scope(self.embed_positions.name): + self.embed_positions.build(None) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.config.d_model]) + if getattr(self, "decoder_layers", None) is not None: + for layer in self.decoder_layers: + with tf.name_scope(layer.name): + layer.build(None) + + +@add_start_docstrings( + "The bare Whisper Model outputting raw hidden-states without any specific head on top.", + WHISPER_START_DOCSTRING, +) +@keras_serializable +class TFWhisperMainLayer(keras.layers.Layer): + config_class = WhisperConfig + + def __init__(self, config: WhisperConfig, **kwargs): + super().__init__(**kwargs) + self.config = config + self.encoder = TFWhisperEncoder(config, name="encoder") + self.decoder = TFWhisperDecoder(config, name="decoder") + + def get_input_embeddings(self): + return self.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.decoder.embed_tokens = value + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + @unpack_inputs + def call( + self, + input_features=None, + decoder_input_ids=None, + decoder_attention_mask=None, + decoder_position_ids=None, + head_mask=None, + decoder_head_mask=None, + cross_attn_head_mask=None, + encoder_outputs=None, + past_key_values=None, + decoder_inputs_embeds=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + training=False, + ): + r""" + Returns: + + Example: + + ```python + >>> import tensorflow as tf + >>> from transformers import TFWhisperModel, AutoFeatureExtractor + >>> from datasets import load_dataset + + >>> model = TFWhisperModel.from_pretrained("openai/whisper-base") + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base") + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="tf") + >>> input_features = inputs.input_features + >>> decoder_input_ids = tf.convert_to_tensor([[1, 1]]) * model.config.decoder_start_token_id + >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state + >>> list(last_hidden_state.shape) + [1, 2, 512] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_features, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput): + encoder_outputs = TFBaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + position_ids=decoder_position_ids, + encoder_hidden_states=encoder_outputs[0], + head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return TFSeq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "encoder", None) is not None: + with tf.name_scope(self.encoder.name): + self.encoder.build(None) + if getattr(self, "decoder", None) is not None: + with tf.name_scope(self.decoder.name): + self.decoder.build(None) + + +@add_start_docstrings( + "The bare Whisper Model outputting raw hidden-states without any specific head on top.", + WHISPER_START_DOCSTRING, +) +class TFWhisperModel(TFWhisperPreTrainedModel): + def __init__(self, config: WhisperConfig, **kwargs): + super().__init__(config, **kwargs) + + self.model = TFWhisperMainLayer(config, name="model") + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + def get_encoder(self): + return self.model.encoder + + def get_decoder(self): + return self.model.decoder + + def decoder(self): + return self.model.decoder + + def encoder(self): + return self.model.encoder + + @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) + @unpack_inputs + def call( + self, + input_features: TFModelInputType | None = None, + decoder_input_ids: np.ndarray | tf.Tensor | None = None, + decoder_attention_mask: np.ndarray | tf.Tensor | None = None, + decoder_position_ids: np.ndarray | tf.Tensor | None = None, + head_mask: np.ndarray | tf.Tensor | None = None, + decoder_head_mask: np.ndarray | tf.Tensor | None = None, + cross_attn_head_mask: np.ndarray | tf.Tensor | None = None, + encoder_outputs: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, + past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, + decoder_inputs_embeds: Optional[Tuple[Union[np.ndarray, tf.Tensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]: + r""" + Returns: + + Example: + + ```python + >>> import tensorflow as tf + >>> from transformers import TFWhisperModel, AutoFeatureExtractor + >>> from datasets import load_dataset + + >>> model = TFWhisperModel.from_pretrained("openai/whisper-base") + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base") + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="tf") + >>> input_features = inputs.input_features + >>> decoder_input_ids = tf.convert_to_tensor([[1, 1]]) * model.config.decoder_start_token_id + >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state + >>> list(last_hidden_state.shape) + [1, 2, 512] + ```""" + outputs = self.model( + input_features=input_features, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + decoder_position_ids=decoder_position_ids, + head_mask=head_mask, + decoder_head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + encoder_outputs=encoder_outputs, + past_key_values=past_key_values, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + return outputs + + def serving_output(self, output): + pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None + dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None + dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None + cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None + enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None + enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None + + return TFSeq2SeqModelOutput( + last_hidden_state=output.last_hidden_state, + past_key_values=pkv, + decoder_hidden_states=dec_hs, + decoder_attentions=dec_attns, + cross_attentions=cross_attns, + encoder_last_hidden_state=output.encoder_last_hidden_state, + encoder_hidden_states=enc_hs, + encoder_attentions=enc_attns, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "model", None) is not None: + with tf.name_scope(self.model.name): + self.model.build(None) + + +@add_start_docstrings( + "The Whisper Model with a language modeling head. Can be used for automatic speech recognition.", + WHISPER_START_DOCSTRING, +) +class TFWhisperForConditionalGeneration(TFWhisperPreTrainedModel, TFCausalLanguageModelingLoss): + base_model_prefix = "model" + _keys_to_ignore_on_load_missing = [ + r"encoder.version", + r"decoder.version", + r"proj_out.weight", + ] + _keys_to_ignore_on_save = [ + r"proj_out.weight", + ] + + def __init__(self, config: WhisperConfig, **kwargs): + super().__init__(config, **kwargs) + self.model = TFWhisperMainLayer(config, name="model") + + def get_encoder(self): + return self.model.get_encoder() + + def get_decoder(self): + return self.model.get_decoder() + + def get_output_embeddings(self): + return self.get_input_embeddings() + + def set_output_embeddings(self, value): + self.set_input_embeddings(value) + + def resize_token_embeddings(self, new_num_tokens: int) -> keras.layers.Embedding: + new_embeddings = super().resize_token_embeddings(new_num_tokens) + return new_embeddings + + @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + @unpack_inputs + def call( + self, + input_features: TFModelInputType | None = None, + decoder_input_ids: np.ndarray | tf.Tensor | None = None, + decoder_attention_mask: np.ndarray | tf.Tensor | None = None, + decoder_position_ids: np.ndarray | tf.Tensor | None = None, + head_mask: np.ndarray | tf.Tensor | None = None, + decoder_head_mask: np.ndarray | tf.Tensor | None = None, + cross_attn_head_mask: np.ndarray | tf.Tensor | None = None, + encoder_outputs: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, + past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, + decoder_inputs_embeds: Optional[Tuple[Union[np.ndarray, tf.Tensor]]] = None, + labels: np.ndarray | tf.Tensor | None = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]: + r""" + labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` + or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is + only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> import tensorflow as tf + >>> from transformers import AutoProcessor, TFWhisperForConditionalGeneration + >>> from datasets import load_dataset + + >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + + >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="tf") + >>> input_features = inputs.input_features + + >>> generated_ids = model.generate(input_features=input_features) + + >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + >>> transcription + ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if labels is not None: + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs = self.model( + input_features, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + decoder_position_ids=decoder_position_ids, + head_mask=head_mask, + decoder_head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + decoder_last_hidden_state = outputs[0] + # Decoder and encoder embeddings are tied + lm_logits = tf.matmul(decoder_last_hidden_state, self.get_output_embeddings().weights, transpose_b=True) + + loss = None if labels is None else self.hf_compute_loss(labels, lm_logits) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return TFSeq2SeqLMOutput( + loss=loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def generate( + self, + inputs: Optional[tf.Tensor] = None, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[TFLogitsProcessorList] = None, + seed: Optional[List[int]] = None, + return_timestamps: Optional[bool] = None, + task: Optional[str] = None, + language: Optional[str] = None, + is_multilingual: Optional[bool] = None, + prompt_ids: Optional[tf.Tensor] = None, + return_token_timestamps=None, + **kwargs, + ): + r""" + Generates sequences of token ids for models with a language modeling head. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate, e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](../generation_strategies). + + + + Parameters: + inputs (`tf.Tensor` of varying shape depending on the modality, *optional*): + The sequence used as a prompt for the generation or as model inputs to the encoder. If unset the method + initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs` should of in + the format of `input_ids`. For encoder-decoder models *inputs* can represent any of `input_ids`, + `input_values`, `input_features`, or `pixel_values`. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + seed (`List[int]`, *optional*): + Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the + `seed` argument from stateless functions in `tf.random`. + return_timestamps (`bool`, *optional*): + Whether to return the timestamps with the text. This enables the `TFWhisperTimestampsLogitsProcessor`. + task (`str`, *optional*): + Task to use for generation, either "translate" or "transcribe". The `model.config.forced_decoder_ids` + will be updated accordingly. + language (`str`, *optional*): + Language token to use for generation, can be either in the form of `<|en|>`, `en` or `english`. You can + find all the possible language tokens in the `model.generation_config.lang_to_id` dictionary. + is_multilingual (`bool`, *optional*): + Whether or not the model is multilingual. + prompt_ids (`tf.Tensor`, *optional*): + Rank-1 tensor of token IDs created by passing text to [`~WhisperProcessor.get_prompt_ids`] that is + provided as a prompt to each chunk. This can be used to provide or "prompt-engineer" a context for + transcription, e.g. custom vocabularies or proper nouns to make it more likely to predict those words + correctly. It cannot be used in conjunction with `decoder_start_token_id` as it overwrites this value. + return_token_timestamps (`bool`, *optional*): + Whether to return token-level timestamps with the text. This can be used with or without the + `return_timestamps` option. To get word-level timestamps, use the tokenizer to group the tokens into + words. + kwargs (`Dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder + specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. + + Return: + [`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when + `config.return_dict_in_generate=True`) or a `tf.Tensor`. + + If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible + [`~utils.ModelOutput`] types are: + + - [`~generation.TFGreedySearchDecoderOnlyOutput`], + - [`~generation.TFSampleDecoderOnlyOutput`], + - [`~generation.TFBeamSearchDecoderOnlyOutput`], + - [`~generation.TFBeamSampleDecoderOnlyOutput`] + + If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible + [`~utils.ModelOutput`] types are: + + - [`~generation.TFGreedySearchEncoderDecoderOutput`], + - [`~generation.TFSampleEncoderDecoderOutput`], + - [`~generation.TFBeamSearchEncoderDecoderOutput`], + - [`~generation.TFBeamSampleEncoderDecoderOutput`] + + """ + if generation_config is None: + generation_config = self.generation_config + + if return_timestamps is not None: + if not hasattr(generation_config, "no_timestamps_token_id"): + raise ValueError( + "You are trying to return timestamps, but the generation config is not properly set. " + "Make sure to initialize the generation config with the correct attributes that are needed such as `no_timestamps_token_id`. " + "For more details on how to generate the approtiate config, refer to https://github.com/huggingface/transformers/issues/21878#issuecomment-1451902363" + ) + + generation_config.return_timestamps = return_timestamps + else: + generation_config.return_timestamps = False + + if language is not None: + language = language.lower() + generation_config.language = language + if task is not None: + generation_config.task = task + + forced_decoder_ids = None + + # Legacy code for backward compatibility + if hasattr(self.config, "forced_decoder_ids") and self.config.forced_decoder_ids is not None: + forced_decoder_ids = self.config.forced_decoder_ids + elif ( + hasattr(self.generation_config, "forced_decoder_ids") + and self.generation_config.forced_decoder_ids is not None + ): + forced_decoder_ids = self.generation_config.forced_decoder_ids + else: + forced_decoder_ids = kwargs.get("forced_decoder_ids", None) + + if task is not None or language is not None or (forced_decoder_ids is None and prompt_ids is not None): + forced_decoder_ids = [] + if hasattr(generation_config, "language"): + if generation_config.language in generation_config.lang_to_id.keys(): + language_token = generation_config.language + elif generation_config.language in TO_LANGUAGE_CODE.keys(): + language_token = f"<|{TO_LANGUAGE_CODE[generation_config.language]}|>" + elif generation_config.language in TO_LANGUAGE_CODE.values(): + language_token = f"<|{generation_config.language}|>" + else: + is_language_code = len(generation_config.language) == 2 + raise ValueError( + f"Unsupported language: {generation_config.language}. Language should be one of:" + f" {list(TO_LANGUAGE_CODE.values()) if is_language_code else list(TO_LANGUAGE_CODE.keys())}." + ) + if language_token not in generation_config.lang_to_id: + raise ValueError( + f"{language_token} is not supported by this specific model as it is not in the `generation_config.lang_to_id`." + "(You should just add it to the generation config)" + ) + forced_decoder_ids.append((1, generation_config.lang_to_id[language_token])) + else: + forced_decoder_ids.append((1, None)) # automatically detect the language + + if hasattr(generation_config, "task"): + if generation_config.task in TASK_IDS: + forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task])) + else: + raise ValueError( + f"The `{generation_config.task}`task is not supported. The task should be one of `{TASK_IDS}`" + ) + elif hasattr(generation_config, "task_to_id"): + forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"])) # defaults to transcribe + if hasattr(generation_config, "no_timestamps_token_id") and not generation_config.return_timestamps: + idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1 + forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id)) + + if forced_decoder_ids is not None: + generation_config.forced_decoder_ids = forced_decoder_ids + + if prompt_ids is not None: + if kwargs.get("decoder_start_token_id") is not None: + raise ValueError( + "When specifying `prompt_ids`, you cannot also specify `decoder_start_token_id` as it gets overwritten." + ) + prompt_ids = prompt_ids.tolist() + decoder_start_token_id, *text_prompt_ids = prompt_ids + # Slicing the text prompt ids in a manner consistent with the OpenAI implementation + # to accomodate context space for the prefix (see https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/decoding.py#L599) + text_prompt_ids = text_prompt_ids[-self.config.max_length // 2 - 1 :] + # Set the decoder_start_token_id to <|startofprev|> + kwargs.update({"decoder_start_token_id": decoder_start_token_id}) + + # Update the max generation length to include the prompt + specified_max_length = kwargs.pop("max_new_tokens", None) or kwargs.pop("max_length", None) + default_max_length = generation_config.max_new_tokens or generation_config.max_length + non_prompt_max_length = specified_max_length or default_max_length + kwargs["max_new_tokens"] = non_prompt_max_length + len(text_prompt_ids) + + # Reformat the forced_decoder_ids to incorporate the prompt + non_prompt_forced_decoder_ids = ( + kwargs.pop("forced_decoder_ids", None) or generation_config.forced_decoder_ids + ) + forced_decoder_ids = [ + *text_prompt_ids, + generation_config.decoder_start_token_id, + *[token for _rank, token in non_prompt_forced_decoder_ids], + ] + forced_decoder_ids = [(rank + 1, token) for rank, token in enumerate(forced_decoder_ids)] + generation_config.forced_decoder_ids = forced_decoder_ids + + # TODO: Implement `WhisperTimeStampLogitsProcessor`. + if generation_config.return_timestamps: + # logits_processor = [TFWhisperTimeStampLogitsProcessor(generation_config)] + raise ValueError("`TFWhisperForConditionalGeneration` doesn't support returning the timestamps yet.") + + if return_token_timestamps: + kwargs["output_attentions"] = True + kwargs["return_dict_in_generate"] = True + + if getattr(generation_config, "task", None) == "translate": + logger.warning("Token-level timestamps may not be reliable for task 'translate'.") + if not hasattr(generation_config, "alignment_heads"): + raise ValueError( + "Model generation config has no `alignment_heads`, token-level timestamps not available. " + "See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config." + ) + + outputs = super().generate( + inputs, + generation_config, + logits_processor, + **kwargs, + ) + + if return_token_timestamps and hasattr(generation_config, "alignment_heads"): + outputs["token_timestamps"] = self._extract_token_timestamps(outputs, generation_config.alignment_heads) + + return outputs + + def serving_output(self, output): + pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None + dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None + dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None + cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None + enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None + enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None + + return TFSeq2SeqLMOutput( + logits=output.logits, + past_key_values=pkv, + decoder_hidden_states=dec_hs, + decoder_attentions=dec_attns, + cross_attentions=cross_attns, + encoder_last_hidden_state=output.encoder_last_hidden_state, + encoder_hidden_states=enc_hs, + encoder_attentions=enc_attns, + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + past_key_values=None, + use_cache=None, + encoder_outputs=None, + attention_mask=None, + decoder_attention_mask=None, + **kwargs, + ): + # cut decoder_input_ids if past is used + if past_key_values is not None: + decoder_input_ids = decoder_input_ids[:, -1:] + + if decoder_attention_mask is not None: # xla + decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:] + elif past_key_values is not None: # no xla + past + decoder_position_ids = past_key_values[0][0].shape[2] + else: # no xla + no past + decoder_position_ids = tf.range(decoder_input_ids.shape[1]) + decoder_position_ids = tf.broadcast_to(decoder_position_ids, decoder_input_ids.shape) + + return { + "input_features": None, # Needs to be passed to make Keras.layer.__call__ happy + "encoder_outputs": encoder_outputs, + "past_key_values": past_key_values, + "decoder_input_ids": decoder_input_ids, + "use_cache": use_cache, + "decoder_attention_mask": decoder_attention_mask, + "decoder_position_ids": decoder_position_ids, + } + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "model", None) is not None: + with tf.name_scope(self.model.name): + self.model.build(None) diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..b85375a4098d768a72051b337cc14cd55d51c193 --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/modeling_whisper.py @@ -0,0 +1,2213 @@ +# coding=utf-8 +# Copyright 2022 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Whisper model.""" +import math +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...activations import ACT2FN +from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, + SequenceClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from .configuration_whisper import WhisperConfig +from .generation_whisper import WhisperGenerationMixin + + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + +logger = logging.get_logger(__name__) + +_HIDDEN_STATES_START_POSITION = 1 + +_CONFIG_FOR_DOC = "WhisperConfig" +_CHECKPOINT_FOR_DOC = "openai/whisper-tiny" + + +from ..deprecated._archive_maps import WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def sinusoids(length: int, channels: int, max_timescale: float = 10000) -> torch.Tensor: + """Returns sinusoids for positional embedding""" + if channels % 2 != 0: + raise ValueError( + f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels." + ) + log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length).view(-1, 1) * inv_timescales.view(1, -1) + return torch.cat([scaled_time.sin(), scaled_time.cos()], dim=1) + + +# Copied from transformers.models.bart.modeling_bart.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices +def _compute_mask_indices( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: Optional[torch.LongTensor] = None, + min_masks: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for + ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on + CPU as part of the preprocessing during training. + + Args: + shape: The shape for which to compute masks. This should be of a tuple of size 2 where + the first element is the batch size and the second element is the length of the axis to span. + mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of + independently generated mask spans of length `mask_length` is computed by + `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the + actual percentage will be smaller. + mask_length: size of the mask + min_masks: minimum number of masked spans + attention_mask: A (right-padded) attention mask which independently shortens the feature axis of + each batch dimension. + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" + f" and `sequence_length`: {sequence_length}`" + ) + + # epsilon is used for probabilistic rounding + epsilon = np.random.rand(1).item() + + def compute_num_masked_span(input_length): + """Given input length, compute how many spans should be masked""" + num_masked_span = int(mask_prob * input_length / mask_length + epsilon) + num_masked_span = max(num_masked_span, min_masks) + + # make sure num masked span <= sequence_length + if num_masked_span * mask_length > sequence_length: + num_masked_span = sequence_length // mask_length + + # make sure num_masked span is also <= input_length - (mask_length - 1) + if input_length - (mask_length - 1) < num_masked_span: + num_masked_span = max(input_length - (mask_length - 1), 0) + + return num_masked_span + + # compute number of masked spans in batch + input_lengths = ( + attention_mask.sum(-1).detach().tolist() + if attention_mask is not None + else [sequence_length for _ in range(batch_size)] + ) + + # SpecAugment mask to fill + spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) + spec_aug_mask_idxs = [] + + max_num_masked_span = compute_num_masked_span(sequence_length) + + if max_num_masked_span == 0: + return spec_aug_mask + + for input_length in input_lengths: + # compute num of masked spans for this input + num_masked_span = compute_num_masked_span(input_length) + + # get random indices to mask + spec_aug_mask_idx = np.random.choice( + np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False + ) + + # pick first sampled index that will serve as a dummy index to pad vector + # to ensure same dimension for all batches due to probabilistic rounding + # Picking first sample just pads those vectors twice. + if len(spec_aug_mask_idx) == 0: + # this case can only happen if `input_length` is strictly smaller then + # `sequence_length` in which case the last token has to be a padding + # token which we can use as a dummy mask id + dummy_mask_idx = sequence_length - 1 + else: + dummy_mask_idx = spec_aug_mask_idx[0] + + spec_aug_mask_idx = np.concatenate( + [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] + ) + spec_aug_mask_idxs.append(spec_aug_mask_idx) + + spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) + + # expand masked indices to masked spans + spec_aug_mask_idxs = np.broadcast_to( + spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) + ) + spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) + + # add offset to the starting indexes so that indexes now create a span + offsets = np.arange(mask_length)[None, None, :] + offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( + batch_size, max_num_masked_span * mask_length + ) + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # ensure that we cannot have indices larger than sequence_length + if spec_aug_mask_idxs.max() > sequence_length - 1: + spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 + + # scatter indices to mask + np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) + + return spec_aug_mask + + +class WhisperPositionalEmbedding(nn.Embedding): + def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None): + super().__init__(num_positions, embedding_dim) + + def forward(self, input_ids, past_key_values_length=0, position_ids=None): + if position_ids is None: + return self.weight[past_key_values_length : past_key_values_length + input_ids.shape[1]] + else: + return self.weight[position_ids] + + +class WhisperAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: Optional[WhisperConfig] = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + # Copied from transformers.models.bart.modeling_bart.BartAttention._shape with BART->whisper + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + # Copied from transformers.models.bart.modeling_bart.BartAttention.forward with BART->whisper + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError( + f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}" + ) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped, past_key_value + + +# Copied from transformers.models.bart.modeling_bart.BartFlashAttention2 with Bart->Whisper +class WhisperFlashAttention2(WhisperAttention): + """ + Whisper flash attention module. This module inherits from `WhisperAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # WhisperFlashAttention2 attention does not support output_attentions + if output_attentions: + raise ValueError("WhisperFlashAttention2 attention does not support output_attentions") + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, q_len, _ = hidden_states.size() + + # get query proj + query_states = self._reshape(self.q_proj(hidden_states), -1, bsz) + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0].transpose(1, 2) + value_states = past_key_value[1].transpose(1, 2) + elif is_cross_attention: + # cross_attentions + key_states = self._reshape(self.k_proj(key_value_states), -1, bsz) + value_states = self._reshape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) + value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1) + value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states], dim=1) + else: + # self_attention + key_states = self._reshape(self.k_proj(hidden_states), -1, bsz) + value_states = self._reshape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states.transpose(1, 2), value_states.transpose(1, 2)) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (LlamaRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = self._flash_attention_forward( + query_states, key_states, value_states, attention_mask, q_len, dropout=self.dropout + ) + + attn_output = attn_output.reshape(bsz, q_len, -1) + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward + def _flash_attention_forward( + self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`float`): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + attn_output = flash_attn_func( + query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal + ) + + return attn_output + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +class WhisperSdpaAttention(WhisperAttention): + # Copied from transformers.models.bart.modeling_bart.BartSdpaAttention.forward with BART->whisper, Bart->Whisper + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + if output_attentions or layer_head_mask is not None: + # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "WhisperModel is using WhisperSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention" + ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states, + key_value_states=key_value_states, + past_key_value=past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + query_states = self._shape(query_states, tgt_len, bsz) + + # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask, + # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577 + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.dropout if self.training else 0.0, + # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1. + is_causal=self.is_causal and attention_mask is None and tgt_len > 1, + ) + + if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, None, past_key_value + + +WHISPER_ATTENTION_CLASSES = { + "eager": WhisperAttention, + "flash_attention_2": WhisperFlashAttention2, + "sdpa": WhisperSdpaAttention, +} + + +# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Whisper, MBART->WHISPER +class WhisperEncoderLayer(nn.Module): + def __init__(self, config: WhisperConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation]( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + layer_head_mask: torch.Tensor, + output_attentions: bool = False, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weights, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + if hidden_states.dtype == torch.float16 and ( + torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() + ): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->Whisper, MBART->WHISPER +class WhisperDecoderLayer(nn.Module): + def __init__(self, config: WhisperConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation]( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + is_causal=True, + config=config, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation]( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + config=config, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + cross_attn_layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = True, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of + size `(decoder_attention_heads,)`. + past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + # add present self-attn cache to positions 1,2 of present_key_value tuple + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=self_attn_past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Cross-Attention Block + cross_attn_present_key_value = None + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + layer_head_mask=cross_attn_layer_head_mask, + past_key_value=cross_attn_past_key_value, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # add cross-attn to positions 3,4 of present_key_value tuple + present_key_value = present_key_value + cross_attn_present_key_value + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +class WhisperPreTrainedModel(PreTrainedModel): + config_class = WhisperConfig + base_model_prefix = "model" + main_input_name = "input_features" + supports_gradient_checkpointing = True + _no_split_modules = ["WhisperEncoderLayer", "WhisperDecoderLayer"] + _supports_flash_attn_2 = True + _supports_sdpa = True + + def _init_weights(self, module): + std = self.config.init_std + if isinstance(module, (nn.Linear, nn.Conv1d)): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, WhisperEncoder): + with torch.no_grad(): + embed_positions = module.embed_positions.weight + embed_positions.copy_(sinusoids(*embed_positions.shape)) + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + input_lengths = (input_lengths - 1) // 2 + 1 + + return input_lengths + + +WHISPER_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`WhisperConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +WHISPER_INPUTS_DOCSTRING = r""" + Args: + input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`): + Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by + loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via + the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the + [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a + tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] + attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing *SpecAugment* data augmentation on padding token indices. Mask values selected in + `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Whisper uses the `decoder_start_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read + [`modeling_whisper._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the BART + paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. + head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded + representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be + input (see `past_key_values`). This is useful if you want more control over how to convert + `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +WHISPER_ENCODER_INPUTS_DOCSTRING = r""" + Args: + input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`): + Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by + loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via + the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the + [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a + tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] + head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class WhisperEncoder(WhisperPreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`WhisperEncoderLayer`]. + + Args: + config: WhisperConfig + """ + + def __init__(self, config: WhisperConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.num_mel_bins = config.num_mel_bins + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_source_positions + self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1) + self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1) + + self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim) + self.embed_positions.requires_grad_(False) + + self.layers = nn.ModuleList([WhisperEncoderLayer(config) for _ in range(config.encoder_layers)]) + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def get_input_embeddings(self) -> nn.Module: + return self.conv1 + + def set_input_embeddings(self, value: nn.Module): + self.conv1 = value + + def forward( + self, + input_features, + attention_mask=None, + head_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Args: + input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`): + Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be + obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a + `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into + `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding + and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] + attention_mask (`torch.Tensor`)`, *optional*): + Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, + but it is not used. By default the silence in the input log mel spectrogram are ignored. + head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + + expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0] + if input_features.shape[-1] != expected_seq_length: + raise ValueError( + f"Whisper expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}." + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + inputs_embeds = nn.functional.gelu(self.conv1(input_features)) + inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds)) + + inputs_embeds = inputs_embeds.permute(0, 2, 1) + embed_pos = self.embed_positions.weight + + hidden_states = inputs_embeds + embed_pos + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + # check if head_mask has a correct number of layers specified if desired + if head_mask is not None: + assert head_mask.size()[0] == ( + len(self.layers) + ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}." + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + None, + (head_mask[idx] if head_mask is not None else None), + output_attentions, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + None, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +class WhisperDecoder(WhisperPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`WhisperDecoderLayer`] + + Args: + config: WhisperConfig + """ + + main_input_name = "input_ids" + + def __init__(self, config: WhisperConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_target_positions + self.max_source_positions = config.max_source_positions + self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) + self.embed_positions = WhisperPositionalEmbedding(self.max_target_positions, config.d_model) + + self.layers = nn.ModuleList([WhisperDecoderLayer(config) for _ in range(config.decoder_layers)]) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self._use_sdpa = config._attn_implementation == "sdpa" + + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def forward( + self, + input_ids=None, + attention_mask=None, + encoder_hidden_states=None, + head_mask=None, + cross_attn_head_mask=None, + past_key_values=None, + inputs_embeds=None, + position_ids=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention + on hidden heads. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of + shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing + `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more + control over how to convert `input_ids` indices into associated vectors than the model's internal + embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if self._use_flash_attention_2: + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._use_sdpa and head_mask is None and not output_attentions: + # output_attentions=True & head_mask can not be supported when using SDPA. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + + # embed positions + if input_ids is not None: + positions = self.embed_positions( + input_ids, past_key_values_length=past_key_values_length, position_ids=position_ids + ) + else: + positions = self.embed_positions( + inputs_embeds, past_key_values_length=past_key_values_length, position_ids=position_ids + ) + + hidden_states = inputs_embeds + positions + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`..." + ) + use_cache = False + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + next_decoder_cache = () if use_cache else None + + # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): + if attn_mask is not None: + assert attn_mask.size()[0] == (len(self.layers)), ( + f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + encoder_hidden_states, + None, # encoder attention mask + head_mask[idx] if head_mask is not None else None, + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, + None, # past_key_value + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + cross_attn_layer_head_mask=( + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None + ), + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + hidden_states = self.layer_norm(hidden_states) + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +@add_start_docstrings( + "The bare Whisper Model outputting raw hidden-states without any specific head on top.", + WHISPER_START_DOCSTRING, +) +class WhisperModel(WhisperPreTrainedModel): + def __init__(self, config: WhisperConfig): + super().__init__(config) + + self.encoder = WhisperEncoder(config) + self.decoder = WhisperDecoder(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.decoder.embed_tokens = value + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + def freeze_encoder(self): + """ + Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will + not be updated during training. + """ + self.encoder._freeze_parameters() + + def _mask_input_features( + self, + input_features: torch.FloatTensor, + attention_mask: Optional[torch.LongTensor] = None, + ): + """ + Masks extracted features along time axis and/or along feature axis according to + [SpecAugment](https://arxiv.org/abs/1904.08779). + """ + + # `config.apply_spec_augment` can set masking to False + if not getattr(self.config, "apply_spec_augment", True): + return input_features + + # generate indices & apply SpecAugment along time axis + batch_size, hidden_size, sequence_length = input_features.size() + + if self.config.mask_time_prob > 0 and self.training: + # generate indices & apply SpecAugment along time axis + mask_time_indices = _compute_mask_indices( + (batch_size, sequence_length), + mask_prob=self.config.mask_time_prob, + mask_length=self.config.mask_time_length, + attention_mask=attention_mask, + min_masks=self.config.mask_time_min_masks, + ) + mask_time_indices = torch.tensor(mask_time_indices, device=input_features.device, dtype=torch.bool) + mask_time_indices = mask_time_indices[:, None].expand(-1, hidden_size, -1) + input_features[mask_time_indices] = 0 + + if self.config.mask_feature_prob > 0 and self.training: + # generate indices & apply SpecAugment along feature axis + mask_feature_indices = _compute_mask_indices( + (batch_size, hidden_size), + mask_prob=self.config.mask_feature_prob, + mask_length=self.config.mask_feature_length, + min_masks=self.config.mask_feature_min_masks, + ) + mask_feature_indices = torch.tensor(mask_feature_indices, device=input_features.device, dtype=torch.bool) + input_features[mask_feature_indices] = 0 + + return input_features + + @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_features: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None, + decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]: + r""" + Returns: + + Example: + ```python + >>> import torch + >>> from transformers import AutoFeatureExtractor, WhisperModel + >>> from datasets import load_dataset + + >>> model = WhisperModel.from_pretrained("openai/whisper-base") + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base") + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") + >>> input_features = inputs.input_features + >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id + >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state + >>> list(last_hidden_state.shape) + [1, 2, 512] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if encoder_outputs is None: + input_features = self._mask_input_features(input_features, attention_mask=attention_mask) + + encoder_outputs = self.encoder( + input_features, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + position_ids=decoder_position_ids, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + "The Whisper Model with a language modeling head. Can be used for automatic speech recognition.", + WHISPER_START_DOCSTRING, +) +class WhisperForConditionalGeneration(WhisperGenerationMixin, WhisperPreTrainedModel): + base_model_prefix = "model" + _tied_weights_keys = ["proj_out.weight"] + + def __init__(self, config: WhisperConfig): + super().__init__(config) + self.model = WhisperModel(config) + self.proj_out = nn.Linear(config.d_model, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.model.get_encoder() + + def get_decoder(self): + return self.model.get_decoder() + + def get_output_embeddings(self): + return self.proj_out + + def set_output_embeddings(self, new_embeddings): + self.proj_out = new_embeddings + + def get_input_embeddings(self) -> nn.Module: + return self.model.get_input_embeddings() + + def freeze_encoder(self): + """ + Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will + not be updated during training. + """ + self.model.encoder._freeze_parameters() + + @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_features: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + decoder_head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None, + decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` + or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is + only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> import torch + >>> from transformers import AutoProcessor, WhisperForConditionalGeneration + >>> from datasets import load_dataset + + >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") + >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + + >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt") + >>> input_features = inputs.input_features + + >>> generated_ids = model.generate(inputs=input_features) + + >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + >>> transcription + ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if labels is not None: + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs = self.model( + input_features, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + head_mask=head_mask, + decoder_head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + decoder_inputs_embeds=decoder_inputs_embeds, + decoder_position_ids=decoder_position_ids, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + lm_logits = self.proj_out(outputs[0]) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + # move labels to correct device to enable PP + labels = labels.to(lm_logits.device) + loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.reshape(-1)) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return Seq2SeqLMOutput( + loss=loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + past_key_values=None, + use_cache=None, + encoder_outputs=None, + attention_mask=None, + decoder_attention_mask=None, + **kwargs, + ): + decoder_position_ids = None + if decoder_attention_mask is not None: + decoder_position_ids = (decoder_attention_mask.cumsum(-1) - 1).clamp(min=0) + + if past_key_values is not None: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if decoder_input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = decoder_input_ids.shape[1] - 1 + + decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] + + if decoder_position_ids is not None and decoder_position_ids.shape[1] > decoder_input_ids.shape[1]: + decoder_position_ids = decoder_position_ids[:, remove_prefix_length:] + + return { + "encoder_outputs": encoder_outputs, + "past_key_values": past_key_values, + "decoder_input_ids": decoder_input_ids, + "use_cache": use_cache, + "decoder_attention_mask": decoder_attention_mask, + "decoder_position_ids": decoder_position_ids, + } + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +class WhisperDecoderWrapper(WhisperPreTrainedModel): + """ + This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is + used in combination with the [`EncoderDecoderModel`] framework. + """ + + def __init__(self, config): + super().__init__(config) + config.is_encoder_decoder = False + self.decoder = WhisperDecoder(config) + + def get_input_embeddings(self): + return self.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.decoder.embed_tokens = value + + def forward(self, *args, **kwargs): + return self.decoder(*args, **kwargs) + + +@add_start_docstrings( + """ + Whisper decoder with a language modeling head on top (linear layer with weights tied to the input embeddings). + """, + WHISPER_START_DOCSTRING, +) +class WhisperForCausalLM(WhisperPreTrainedModel): + _tied_weights_keys = ["proj_out.weight"] + main_input_name = "input_ids" + + def __init__(self, config): + super().__init__(config) + config.is_encoder_decoder = False + self.model = WhisperDecoderWrapper(config) + + self.proj_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.proj_out + + def set_output_embeddings(self, new_embeddings): + self.proj_out = new_embeddings + + def get_input_embeddings(self) -> nn.Module: + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + def set_decoder(self, decoder): + self.model.decoder = decoder + + def get_decoder(self): + return self.model.decoder + + @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None, + head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + [What are attention masks?](../glossary#attention-mask) + encoder_outputs (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + if the model is configured as a decoder. + head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional + tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains + pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If + `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + + Returns: + + Example: + + ```python + >>> from transformers import WhisperForCausalLM, WhisperForConditionalGeneration, WhisperProcessor + >>> import torch + >>> from datasets import load_dataset + + >>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2") + >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") + + >>> assistant_model = WhisperForCausalLM.from_pretrained("distil-whisper/distil-large-v2") + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> sample = ds[0]["audio"] + >>> input_features = processor( + ... sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt" + ... ).input_features + + >>> predicted_ids = model.generate(input_features, assistant_model=assistant_model) + + >>> # decode token ids to text + >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] + >>> transcription + ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # If the user passed a tuple or `BaseModelOutput` for encoder_outputs, we extract only the hidden states + if isinstance(encoder_outputs, (BaseModelOutput, tuple, list)): + encoder_outputs = encoder_outputs[0] + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_outputs, + head_mask=head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = self.proj_out(outputs[0]) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + use_cache=None, + encoder_outputs=None, + attention_mask=None, + **kwargs, + ): + if past_key_values is not None: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = input_ids.shape[1] - 1 + + input_ids = input_ids[:, remove_prefix_length:] + + return { + "encoder_outputs": encoder_outputs, + "past_key_values": past_key_values, + "input_ids": input_ids, + "use_cache": use_cache, + "attention_mask": attention_mask, + } + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks + like SUPERB Keyword Spotting. + """, + WHISPER_ENCODER_INPUTS_DOCSTRING, +) +class WhisperForAudioClassification(WhisperPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.encoder = WhisperEncoder(config) + num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings + if config.use_weighted_layer_sum: + self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) + self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size) + self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + def freeze_encoder(self): + """ + Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will + not be updated during training. Only the projection layers and classification head will be updated. + """ + self.encoder._freeze_parameters() + + def get_input_embeddings(self) -> nn.Module: + return self.encoder.get_input_embeddings() + + def set_input_embeddings(self, value: nn.Module): + self.encoder.set_input_embeddings(value) + + @add_start_docstrings_to_model_forward(WHISPER_ENCODER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_features: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Returns: + + Example: + + ```python + >>> import torch + >>> from transformers import AutoFeatureExtractor, WhisperForAudioClassification + >>> from datasets import load_dataset + + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id") + >>> model = WhisperForAudioClassification.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id") + + >>> ds = load_dataset("google/fleurs", "all", split="validation", streaming=True) + >>> sample = next(iter(ds)) + + >>> inputs = feature_extractor( + ... sample["audio"]["array"], sampling_rate=sample["audio"]["sampling_rate"], return_tensors="pt" + ... ) + >>> input_features = inputs.input_features + + >>> with torch.no_grad(): + ... logits = model(input_features).logits + + >>> predicted_class_ids = torch.argmax(logits).item() + >>> predicted_label = model.config.id2label[predicted_class_ids] + >>> predicted_label + 'Afrikaans' + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + if self.config.use_weighted_layer_sum: + output_hidden_states = True + elif output_hidden_states is None: + output_hidden_states = self.config.output_hidden_states + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_features, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = encoder_outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = torch.stack(hidden_states, dim=1) + norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) + hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) + else: + hidden_states = encoder_outputs[0] + + hidden_states = self.projector(hidden_states) + pooled_output = hidden_states.mean(dim=1) + + logits = self.classifier(pooled_output) + + loss = None + + if labels is not None: + loss_fct = CrossEntropyLoss() + # move labels to correct device to enable PP + labels = labels.to(logits.device) + loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + encoder_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/processing_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/processing_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..cae72aa0e13f83a488f05764f046ebac6f6fe46a --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/processing_whisper.py @@ -0,0 +1,98 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Speech processor class for Whisper +""" + + +from ...processing_utils import ProcessorMixin + + +class WhisperProcessor(ProcessorMixin): + r""" + Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single + processor. + + [`WhisperProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`WhisperTokenizer`]. See + the [`~WhisperProcessor.__call__`] and [`~WhisperProcessor.decode`] for more information. + + Args: + feature_extractor (`WhisperFeatureExtractor`): + An instance of [`WhisperFeatureExtractor`]. The feature extractor is a required input. + tokenizer (`WhisperTokenizer`): + An instance of [`WhisperTokenizer`]. The tokenizer is a required input. + """ + + feature_extractor_class = "WhisperFeatureExtractor" + tokenizer_class = "WhisperTokenizer" + + def __init__(self, feature_extractor, tokenizer): + super().__init__(feature_extractor, tokenizer) + self.current_processor = self.feature_extractor + self._in_target_context_manager = False + + def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True): + return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps) + + def __call__(self, *args, **kwargs): + """ + Forwards the `audio` argument to WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] and the `text` + argument to [`~WhisperTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more + information. + """ + # For backward compatibility + if self._in_target_context_manager: + return self.current_processor(*args, **kwargs) + + audio = kwargs.pop("audio", None) + sampling_rate = kwargs.pop("sampling_rate", None) + text = kwargs.pop("text", None) + if len(args) > 0: + audio = args[0] + args = args[1:] + + if audio is None and text is None: + raise ValueError("You need to specify either an `audio` or `text` input to process.") + + if audio is not None: + inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs) + if text is not None: + encodings = self.tokenizer(text, **kwargs) + + if text is None: + return inputs + + elif audio is None: + return encodings + else: + inputs["labels"] = encodings["input_ids"] + return inputs + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to WhisperTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to WhisperTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + def get_prompt_ids(self, text: str, return_tensors="np"): + return self.tokenizer.get_prompt_ids(text, return_tensors=return_tensors) diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/tokenization_whisper.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/tokenization_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..25e80d477fda3bd41c3394a623c2454fae9c22be --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/tokenization_whisper.py @@ -0,0 +1,1329 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for Whisper.""" +import json +import os +import warnings +from functools import lru_cache +from typing import List, Optional, Tuple, Union + +import numpy as np +import regex as re + +from ...tokenization_utils import AddedToken, PreTrainedTokenizer +from ...utils import logging +from .english_normalizer import BasicTextNormalizer, EnglishTextNormalizer + + +VOCAB_FILES_NAMES = { + "vocab_file": "vocab.json", + "tokenizer_file": "tokenizer.json", + "merges_file": "merges.txt", + "normalizer_file": "normalizer.json", +} + + +MAX_MODEL_INPUT_SIZES = { + "openai/whisper-base": 448, +} + + +# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control + characters the bpe code barfs on. + + The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab + if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for + decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup + tables between utf-8 bytes and unicode strings. + """ + bs = ( + list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) + ) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs +def get_pairs(word): + """ + Return set of symbol pairs in a word. + + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", + "yue": "cantonese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", + "mandarin": "zh", +} + +TASK_IDS = ["translate", "transcribe"] + + +class WhisperTokenizer(PreTrainedTokenizer): + """ + Construct a Whisper tokenizer. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to + the superclass for more information regarding such methods. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + merges_file (`str`): + Path to the merges file. + normalizer_file (`str`, *optional*): + Path to the normalizer_file file. + errors (`str`, *optional*, defaults to `"replace"`): + Paradigm to follow when decoding bytes to UTF-8. See + [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. + unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): + The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as + `"<|startoftranscript|>"` when generating. + eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): + The end of sequence token. + pad_token (`str`, *optional*): + The token used for padding, for example when batching sequences of different lengths. + add_prefix_space (`bool`, *optional*, defaults to `False`): + Whether or not to add an initial space to the input. This allows to treat the leading word just as any + other word. + language (`str`, *optional*): + The language of the transcription text. The corresponding language id token is appended to the start of the + sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token + `"<|es|>"` is appended to the start of sequence. This should be used for multilingual fine-tuning only. + task (`str`, *optional*): + Task identifier to append at the start of sequence (if any). This should be used for mulitlingual + fine-tuning, with `"transcribe"` for speech recognition and `"translate"` for speech translation. + predict_timestamps (`bool`, *optional*, defaults to `False`): + Whether to omit the `<|notimestamps|>` token at the start of the sequence. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + merges_file, + normalizer_file=None, + errors="replace", + unk_token="<|endoftext|>", + bos_token="<|endoftext|>", + eos_token="<|endoftext|>", + pad_token=None, + add_prefix_space=False, + language=None, + task=None, + predict_timestamps=False, + **kwargs, + ): + bos_token = ( + AddedToken(bos_token, lstrip=False, rstrip=False, normalized=False, special=True) + if isinstance(bos_token, str) + else bos_token + ) + eos_token = ( + AddedToken(eos_token, lstrip=False, rstrip=False, normalized=False, special=True) + if isinstance(eos_token, str) + else eos_token + ) + unk_token = ( + AddedToken(unk_token, lstrip=False, rstrip=False, normalized=False, special=True) + if isinstance(unk_token, str) + else unk_token + ) + pad_token = ( + AddedToken(pad_token, lstrip=False, rstrip=False, normalized=False, special=True) + if isinstance(pad_token, str) + else pad_token + ) + + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.encoder = json.load(vocab_handle) + self.decoder = {v: k for k, v in self.encoder.items()} + self.errors = errors # how to handle errors in decoding + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + with open(merges_file, encoding="utf-8") as merges_handle: + bpe_merges = merges_handle.read().split("\n")[1:-1] + bpe_merges = [tuple(merge.split()) for merge in bpe_merges] + self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) + self.cache = {} + self.add_prefix_space = add_prefix_space + + if normalizer_file is not None: + with open(normalizer_file, encoding="utf-8") as vocab_handle: + self.english_spelling_normalizer = json.load(vocab_handle) + else: + self.english_spelling_normalizer = None + + # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions + self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") + self.timestamp_pat = re.compile(r"<\|(\d+\.\d+)\|>") + + self.language = language + super().__init__( + errors=errors, + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + add_prefix_space=add_prefix_space, + **kwargs, + ) + + self.task = task + self.predict_timestamps = predict_timestamps + + @property + def vocab_size(self) -> int: + return len(self.encoder) + + def get_vocab(self): + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe with GPT2 -> Whisper + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token) + pairs = get_pairs(word) + + if not pairs: + return token + + while True: + bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + except ValueError: + new_word.extend(word[i:]) + break + else: + new_word.extend(word[i:j]) + i = j + + if word[i] == first and i < len(word) - 1 and word[i + 1] == second: + new_word.append(first + second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = " ".join(word) + self.cache[token] = word + return word + + def set_prefix_tokens(self, language: str = None, task: str = None, predict_timestamps: bool = None): + """ + Override the prefix tokens appended to the start of the label sequence. This method can be used standalone to + update the prefix tokens as required when fine-tuning. Example: + + ```python + >>> # instantiate the tokenizer and set the prefix token to Spanish + >>> tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="spanish") + >>> # now switch the prefix token from Spanish to French + >>> tokenizer.set_prefix_tokens(language="french") + ``` + + Args: + language (`str`, *optional*, defaults to `None`): + The language of the transcription text. + task (`str`, *optional*, defaults to `None`): + Task identifier to append at the start of sequence (if any). + predict_timestamps (`bool`, *optional*, defaults to `None`): + Whether to omit the `<|notimestamps|>` token at the start of the sequence. + """ + self.language = language if language is not None else self.language + self.task = task if task is not None else self.task + self.predict_timestamps = predict_timestamps if predict_timestamps is not None else self.predict_timestamps + + @property + def prefix_tokens(self) -> List[int]: + bos_token_id = self.convert_tokens_to_ids("<|startoftranscript|>") + translate_token_id = self.convert_tokens_to_ids("<|translate|>") + transcribe_token_id = self.convert_tokens_to_ids("<|transcribe|>") + notimestamps_token_id = self.convert_tokens_to_ids("<|notimestamps|>") + langs = tuple(LANGUAGES.keys()) + + if self.language is not None: + self.language = self.language.lower() + if self.language in TO_LANGUAGE_CODE: + language_id = TO_LANGUAGE_CODE[self.language] + elif self.language in TO_LANGUAGE_CODE.values(): + language_id = self.language + else: + is_language_code = len(self.language) == 2 + raise ValueError( + f"Unsupported language: {self.language}. Language should be one of:" + f" {list(TO_LANGUAGE_CODE.values()) if is_language_code else list(TO_LANGUAGE_CODE.keys())}." + ) + + if self.task is not None: + if self.task not in TASK_IDS: + raise ValueError(f"Unsupported task: {self.task}. Task should be in: {TASK_IDS}") + + bos_sequence = [bos_token_id] + if self.language is not None: + bos_sequence.append(bos_token_id + 1 + langs.index(language_id)) + if self.task is not None: + bos_sequence.append(transcribe_token_id if self.task == "transcribe" else translate_token_id) + if not self.predict_timestamps: + bos_sequence.append(notimestamps_token_id) + return bos_sequence + + # Copied from transformers.models.speech_to_text.tokenization_speech_to_text.Speech2TextTokenizer.build_inputs_with_special_tokens + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]: + """Build model inputs from a sequence by appending eos_token_id.""" + if token_ids_1 is None: + return self.prefix_tokens + token_ids_0 + [self.eos_token_id] + # We don't expect to process pairs, but leave the pair logic for API consistency + return self.prefix_tokens + token_ids_0 + token_ids_1 + [self.eos_token_id] + + # Copied from transformers.models.speech_to_text.tokenization_speech_to_text.Speech2TextTokenizer.get_special_tokens_mask + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + prefix_ones = [1] * len(self.prefix_tokens) + suffix_ones = [1] + if token_ids_1 is None: + return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones + return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones + + # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize with GPT2 -> Whisper + def _tokenize(self, text): + """Tokenize a string.""" + bpe_tokens = [] + for token in re.findall(self.pat, text): + token = "".join( + self.byte_encoder[b] for b in token.encode("utf-8") + ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) + bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) + return bpe_tokens + + # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_token_to_id with GPT2 -> Whisper + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.encoder.get(token, self.encoder.get(self.unk_token)) + + def _convert_id_to_token(self, index): + """ + Converts an index (integer) in a token (str) using the vocab. Whisper's base tokenizer always decodes OOV + tokens as "", thus we do not use the `unk_token` here. + """ + return self.decoder.get(index, "") + + def _normalize(self, text): + warnings.warn( + "The private method `_normalize` is deprecated and will be removed in v5 of Transformers." + "You can normalize an input string using the Whisper English normalizer using the `normalize` method." + ) + return self.normalize(text) + + def _basic_normalize(self, text, remove_diacritics=False): + warnings.warn( + "The private method `_basic_normalize` is deprecated and will be removed in v5 of Transformers." + "You can normalize an input string using the Whisper basic normalizer using the `basic_normalize` method." + ) + return self.basic_normalize(text, remove_diacritics=remove_diacritics) + + def normalize(self, text): + """ + Normalize a given string using the `EnglishTextNormalizer` class, which preforms commons transformation on + english text. + """ + normalizer = EnglishTextNormalizer(self.english_spelling_normalizer) + return normalizer(text) + + @staticmethod + def basic_normalize(text, remove_diacritics=False): + """ + Normalize a given string using the `BasicTextNormalizer` class, which preforms commons transformation on + multilingual text. + """ + normalizer = BasicTextNormalizer(remove_diacritics=remove_diacritics) + return normalizer(text) + + def _decode_with_timestamps(self, token_ids, skip_special_tokens=False, time_precision=0.02) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. This method decodes + given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + timestamp_begin = self.all_special_ids[-1] + 1 + outputs = [[]] + + cur_max_timestamp = 0.0 + prev_segments_len = 0.0 + + for token in token_ids: + if token >= timestamp_begin: + timestamp = float((token - timestamp_begin) * time_precision) + + if timestamp < cur_max_timestamp: + # next segment has started + prev_segments_len += cur_max_timestamp + + cur_max_timestamp = timestamp + + outputs.append(f"<|{(timestamp + prev_segments_len):.2f}|>") + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [ + s if isinstance(s, str) else self.decode(s, skip_special_tokens=skip_special_tokens) for s in outputs + ] + return "".join(outputs) + + def _compute_offsets(self, token_ids, time_precision=0.02): + """ + Compute offsets for a given tokenized input + + Args: + token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Can be obtained using the `__call__` method. + time_precision (`float`, `optional`, defaults to 0.02): + The time ratio to convert from token to time. + """ + offsets = [] + # ensure torch tensor of token ids is placed on cpu + if "torch" in str(type(token_ids)) and (hasattr(token_ids, "cpu") and callable(token_ids.cpu)): + token_ids = token_ids.cpu() + token_ids = np.array(token_ids) + if token_ids.shape[0] > 1 and len(token_ids.shape) > 1: + raise ValueError("Can only process a single input at a time") + timestamp_begin = self.all_special_ids[-1] + 1 + timestamp_tokens = token_ids >= timestamp_begin + + consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1 + if consecutive.shape[0] == 0 and timestamp_tokens.sum() <= 1: + # either there are no timestamps or there are no consecutive ones + return [] + elif np.where(timestamp_tokens)[0][-1] + 1 not in consecutive: + # we add the final timestamp if it is not already in the list + consecutive = np.append(consecutive, np.where(timestamp_tokens)[0][-1] + 1) + + last_slice = np.where(timestamp_tokens)[0][0] + for current_slice in consecutive: + sliced_tokens = token_ids[last_slice:current_slice] + if len(sliced_tokens) > 1: + start_timestamp_position = sliced_tokens[0].item() - timestamp_begin + end_timestamp_position = sliced_tokens[-1].item() - timestamp_begin + # strip timestamp tokens from the text output + sliced_tokens = self._preprocess_token_ids(sliced_tokens) + text = self._decode(sliced_tokens) + text = self._filter_timestamp_ids(text) + offsets.append( + { + "text": text, + "timestamp": ( + start_timestamp_position * time_precision, + end_timestamp_position * time_precision, + ), + } + ) + last_slice = current_slice + + return offsets + + @lru_cache + def timestamp_ids(self, time_precision=0.02): + """ + Compute the timestamp token ids for a given precision and save to least-recently used (LRU) cache. + + Args: + time_precision (`float`, `optional`, defaults to 0.02): + The time ratio to convert from token to time. + """ + return self.convert_tokens_to_ids([("<|%.2f|>" % (i * time_precision)) for i in range(1500 + 1)]) + + def _preprocess_token_ids(self, token_ids, skip_special_tokens: bool = False): + """ + Pre-process the token ids for decoding by removing the prompt tokens ids and timestamp token ids. + + Args: + token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Typically, obtained using the `__call__` method of the tokenizer. + skip_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to remove special tokens from the token ids. If `True`, the prompt token ids will be + removed. + """ + if skip_special_tokens: + prompt_token_id = self.convert_tokens_to_ids("<|startofprev|>") + decoder_start_token_id = self.convert_tokens_to_ids("<|startoftranscript|>") + token_ids = self._strip_prompt(token_ids, prompt_token_id, decoder_start_token_id) + + return token_ids + + def _filter_timestamp_ids(self, token_ids): + return re.sub(self.timestamp_pat, "", token_ids) + + def decode( + self, + token_ids, + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: bool = None, + output_offsets: bool = False, + time_precision: float = 0.02, + decode_with_timestamps: bool = False, + normalize: bool = False, + basic_normalize: bool = False, + remove_diacritics: bool = False, + **kwargs, + ) -> str: + """ + Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special + tokens and clean up tokenization spaces. + + Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. + + Args: + token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Can be obtained using the `__call__` method. + skip_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to remove special tokens in the decoding. + clean_up_tokenization_spaces (`bool`, *optional*): + Whether or not to clean up the tokenization spaces. If `None`, will default to + `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). + output_offsets (`bool`, *optional*, defaults to `False`): + Whether or not to output the offsets of the tokens. This should only be set if the model predicted + timestamps. + time_precision (`float`, `optional`, defaults to 0.02): + The time ratio to convert from token to time. + decode_with_timestamps (`bool`, *optional*, defaults to `False`): + Whether or not to decode with timestamps included in the raw text. + normalize (`bool`, *optional*, defaults to `False`): + Whether or not to apply the English text normalizer to the decoded text. Only applicable when the + target text is in English. Otherwise, the basic text normalizer should be applied. + basic_normalize (`bool`, *optional*, defaults to `False`): + Whether or not to apply the Basic text normalizer to the decoded text. Applicable to multilingual + target text. + remove_diacritics (`bool`, *optional*, defaults to `False`): + Whether or not to remove diacritics when applying the Basic text normalizer. Removing diacritics may + destroy information in the decoded text, hence it should be used with caution. + kwargs (additional keyword arguments, *optional*): + Will be passed to the underlying model specific decode method. + Returns: + `str`: The decoded sentence. + """ + filtered_ids = self._preprocess_token_ids( + token_ids, + skip_special_tokens=skip_special_tokens, + ) + + text = super().decode( + filtered_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + normalize=normalize, + basic_normalize=basic_normalize, + remove_diacritics=remove_diacritics, + **kwargs, + ) + if decode_with_timestamps: + # legacy method to decode timestamps when not included in the tokenizer vocabulary + text = self._decode_with_timestamps( + filtered_ids, time_precision=time_precision, skip_special_tokens=skip_special_tokens + ) + else: + text = self._filter_timestamp_ids(text) + + # retrieve offsets + if output_offsets: + offsets = self._compute_offsets(token_ids, time_precision=time_precision) + return {"text": text, "offsets": offsets} + return text + + def _decode( + self, + token_ids: Union[int, List[int]], + skip_special_tokens: bool = False, + normalize: bool = False, + basic_normalize: bool = False, + remove_diacritics: bool = False, + **kwargs, + ) -> str: + self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False) + filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) + + # To avoid mixing byte-level and unicode for byte-level BPT + # we need to build string separately for added tokens and byte-level tokens + # cf. https://github.com/huggingface/transformers/issues/1133 + sub_texts = [] + current_sub_text = [] + for token in filtered_tokens: + if skip_special_tokens and token in self.all_special_ids: + continue + if token in self.added_tokens_encoder: + if current_sub_text: + sub_texts.append(self.convert_tokens_to_string(current_sub_text)) + current_sub_text = [] + sub_texts.append(token) + else: + current_sub_text.append(token) + if current_sub_text: + sub_texts.append(self.convert_tokens_to_string(current_sub_text)) + + text = "".join(sub_texts) + + if normalize: + clean_text = self.normalize(text) + return clean_text + elif basic_normalize: + clean_text = self.basic_normalize(text, remove_diacritics=remove_diacritics) + return clean_text + else: + return text + + # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.convert_tokens_to_string with GPT2 -> Whisper + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + text = "".join(tokens) + text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) + return text + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + merge_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] + ) + normalizer_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["normalizer_file"] + ) + + with open(vocab_file, "w", encoding="utf-8") as f: + f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") + + index = 0 + with open(merge_file, "w", encoding="utf-8") as writer: + writer.write("#version: 0.2\n") + for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." + " Please check that the tokenizer is not corrupted!" + ) + index = token_index + writer.write(" ".join(bpe_tokens) + "\n") + index += 1 + + if self.english_spelling_normalizer is not None: + with open(normalizer_file, "w", encoding="utf-8") as f: + f.write( + json.dumps(self.english_spelling_normalizer, indent=2, sort_keys=True, ensure_ascii=False) + "\n" + ) + + return vocab_file, merge_file, normalizer_file + + # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.prepare_for_tokenization with GPT2 -> Whisper + def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): + add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) + if is_split_into_words or add_prefix_space: + text = " " + text + return (text, kwargs) + + @property + # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.default_chat_template + def default_chat_template(self): + """ + A simple chat template that ignores role information and just concatenates messages with EOS tokens. + """ + logger.warning_once( + "\nNo chat template is defined for this tokenizer - using the default template " + f"for the {self.__class__.__name__} class. If the default is not appropriate for " + "your model, please set `tokenizer.chat_template` to an appropriate template. " + "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n" + ) + return "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}" + + def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True): + self.set_prefix_tokens(task=task, language=language, predict_timestamps=not no_timestamps) + # prefix tokens are of the form: <|startoftranscript|> <|lang_id|> <|task|> <|notimestamps|> + # we don't want to force the bos token at position 1, as this is the starting token + # when we generate, so we slice the prefix tokens to: <|lang_id|> <|task|> <|notimestamps|> + # to get the forced tokens + forced_tokens = self.prefix_tokens[1:] + forced_decoder_ids = [(rank + 1, token) for rank, token in enumerate(forced_tokens)] + return forced_decoder_ids + + def _decode_asr(self, model_outputs, *, return_timestamps, return_language, time_precision): + return _decode_asr( + self, + model_outputs, + return_timestamps=return_timestamps, + return_language=return_language, + time_precision=time_precision, + ) + + def get_prompt_ids(self, text: str, return_tensors="np"): + """Converts prompt text to IDs that can be passed to [`~WhisperForConditionalGeneration.generate`].""" + batch_encoding = self("<|startofprev|>", " " + text.strip(), add_special_tokens=False) + + # Check for special tokens + prompt_text_ids = batch_encoding["input_ids"][1:] + special_token_id = next((x for x in prompt_text_ids if x >= self.all_special_ids[0]), None) + if special_token_id is not None: + token = self.convert_ids_to_tokens(special_token_id) + raise ValueError(f"Encountered text in the prompt corresponding to disallowed special token: {token}.") + + batch_encoding.convert_to_tensors(tensor_type=return_tensors) + return batch_encoding["input_ids"] + + @staticmethod + def _strip_prompt(token_ids: List[int], prompt_token_id: int, decoder_start_token_id: int): + has_prompt = isinstance(token_ids, list) and token_ids and token_ids[0] == prompt_token_id + if has_prompt: + if decoder_start_token_id in token_ids: + return token_ids[token_ids.index(decoder_start_token_id) :] + else: + return [] + + return token_ids + + +def _decode_asr(tokenizer, model_outputs, *, return_timestamps, return_language, time_precision): + """ + Internal method meant to only be used by asr pipeline. Handles all the little quirks specific to whisper to handle + the various options not allowed in other seq2seq models + """ + + # =========== Overview ============ + # - iterate over all outputs + # - all tokens within output + # - Each token can be + # - language token + # - special token + # - timestamp token + # - text token + # - We accumulate the text tokens. + # - We split on end timestamps + # - Lots of complexity comes from stride and timestamps + + last_language = None + + def new_chunk(): + return {"language": last_language, "timestamp": [None, None], "text": ""} + + # Welcome to the state machine ! + chunks = [] + chunk = new_chunk() + time_offset = 0.0 + timestamp_begin = tokenizer.convert_tokens_to_ids("<|notimestamps|>") + 1 + previous_tokens = [] + previous_token_timestamps = [] + skip = False + right_stride_start = None + + all_special_ids = set(tokenizer.all_special_ids) + # - iterate over all outputs + for chunk_id, output in enumerate(model_outputs): + # We can drop everything to Python list, it's going to make + # our lives easier + token_ids = output["tokens"][0].tolist() + if return_timestamps == "word": + token_timestamps = output["token_timestamps"][0].tolist() + + # Those keep track of timestamps within strides + # Which need to be skipped and resolve all tokens in a single + # chunk. + last_timestamp = None + first_timestamp = timestamp_begin + + if "stride" in output: + chunk_len, stride_left, stride_right = output["stride"] + # Offset the timings to account for the other `model_outputs`. + time_offset -= stride_left + right_stride_start = chunk_len - stride_right + + # Keeping track of timestamps within strides + # We're going to NOT split on those, and delay until we're + # out of BOTH stride. Otherwise lots of issues occur and + # corner cases + if stride_left: + first_timestamp = stride_left / time_precision + timestamp_begin + if stride_right: + for token in reversed(token_ids): + if token >= timestamp_begin: + # There can be several token in the right stride + # But the last one is ALWAYS going to be skipped + if ( + last_timestamp is not None + and (token - timestamp_begin) * time_precision < right_stride_start + ): + break + last_timestamp = token + + current_tokens = [] + current_token_timestamps = [] + + # - all tokens within output + for i, token in enumerate(token_ids): + # 4 possible states for each token + # - 1/ Language code + # - 2/ all other special tokens (which we ignore) + # - 3/ Timestamp + # - 4/ Regular text + if token in all_special_ids: + # Either language code or other + text = tokenizer.decode([token]) + # Removing outer shell <|XX|> + text = text[2:-2] + language = LANGUAGES.get(text, None) + if language is not None: + # 1/ Indeed some language + # TODO Handle when language is different from the previous + # one, and we cannot use timestamped tokens to create chunks + if last_language and language != last_language and not return_timestamps: + previous_tokens.append(current_tokens) + resolved_tokens = _find_longest_common_sequence(previous_tokens) + resolved_text = tokenizer.decode(resolved_tokens) + chunk["text"] = resolved_text + chunks.append(chunk) + + # Flush all our temporary context + previous_tokens = [] + current_tokens = [] + chunk = new_chunk() + chunk["language"] = language + last_language = language + else: + # 2/ This is a regular special token, ignoring it + pass + elif token >= timestamp_begin: + # 3/ Timestamp token + time = (token - timestamp_begin) * time_precision + time_offset + time = round(time, 2) + if last_timestamp and token >= last_timestamp: + # Whisper outputted a timestamp token, but it falls within + # our stride, so we're going to skip it for the time being + # and resolve this later + # Skip is necessary because timestamp tokens always come + # by pair, so we need to skip the next one too (which would mark the start of another chunk). + skip = True + elif skip or (previous_tokens and token < first_timestamp): + skip = False + elif chunk["timestamp"][0] is None: + chunk["timestamp"][0] = time + else: + # This is the end of the timestamp chunk + if time == chunk["timestamp"][0]: + # This is a bug in timestamp token output + # where we're taking the duplicate token + # as a stop where it should be a start. + # This is an issue in the underlying model output + # Let's just skip it so it becomes de-factor + # a start agin + pass + else: + chunk["timestamp"][1] = time + # Handling merges. + previous_tokens.append(current_tokens) + if return_timestamps == "word": + previous_token_timestamps.append(current_token_timestamps) + resolved_tokens, resolved_token_timestamps = _find_longest_common_sequence( + previous_tokens, previous_token_timestamps + ) + resolved_text = tokenizer.decode(resolved_tokens) + chunk["text"] = resolved_text + if return_timestamps == "word": + chunk["words"] = _collate_word_timestamps( + tokenizer, resolved_tokens, resolved_token_timestamps, last_language + ) + chunks.append(chunk) + + # Flush all our temporary context + previous_tokens = [] + current_tokens = [] + previous_token_timestamps = [] + current_token_timestamps = [] + chunk = new_chunk() + else: + # 4/ Regular token + # We just append to the list of all tokens so we can handle + # merges later and decode into text. + current_tokens.append(token) + if return_timestamps == "word": + start_time = round(token_timestamps[i] + time_offset, 2) + if i + 1 < len(token_timestamps): + end_time = round(token_timestamps[i + 1] + time_offset, 2) + else: + end_time = None # should never happen + current_token_timestamps.append((start_time, end_time)) + + if "stride" in output: + time_offset += chunk_len - stride_right + + # Leftover tokens + if current_tokens: + previous_tokens.append(current_tokens) + if return_timestamps == "word": + previous_token_timestamps.append(current_token_timestamps) + elif not (any(p for p in previous_tokens)): + chunk = new_chunk() + previous_tokens = [] + current_tokens = [] + previous_token_timestamps = [] + current_token_timestamps = [] + + if previous_tokens: + if return_timestamps: + logger.warning( + "Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. " + "Also make sure WhisperTimeStampLogitsProcessor was used during generation." + ) + # Happens when we don't use timestamps + resolved_tokens, resolved_token_timestamps = _find_longest_common_sequence( + previous_tokens, previous_token_timestamps + ) + resolved_text = tokenizer.decode(resolved_tokens) + chunk["text"] = resolved_text + if return_timestamps == "word": + chunk["words"] = _collate_word_timestamps( + tokenizer, resolved_tokens, resolved_token_timestamps, last_language + ) + chunks.append(chunk) + + # Preparing and cleaning up the pipeline output + full_text = "".join(chunk["text"] for chunk in chunks) + if return_timestamps or return_language: + for chunk in chunks: + if not return_timestamps: + chunk.pop("timestamp") + else: + chunk["timestamp"] = tuple(chunk["timestamp"]) + if not return_language: + chunk.pop("language") + + if return_timestamps == "word": + new_chunks = [] + for chunk in chunks: + new_chunks.extend(chunk["words"]) + optional = {"chunks": new_chunks} + else: + optional = {"chunks": chunks} + else: + optional = {} + return full_text, optional + + +def _find_longest_common_sequence(sequences, token_timestamp_sequences=None): + # It would be much harder to do O(n) because of fault tolerance. + # We actually have a really good property which is that the total sequence + # MUST be those subsequences in order. + # If token_timestamp_sequences is provided, will split those sequences in + # exactly the same way. + + left_sequence = sequences[0] + left_length = len(left_sequence) + total_sequence = [] + + if token_timestamp_sequences: + left_token_timestamp_sequence = token_timestamp_sequences[0] + total_token_timestamp_sequence = [] + + for seq_idx, right_sequence in enumerate(sequences[1:]): + # index = 0 + max_ = 0.0 + max_indices = (left_length, left_length, 0, 0) + # Here we're sliding matches + # [a, b, c, d] + # [c, d, f] + # = [c] == [d] + # + # [a, b, c, d] + # [c, d, f] + # = [c, d] == [c, d] + # + # + # [a, b, c, d] + # [c, d, f] + # + # = [b, c, d] == [c, d, f] + # + # [a, b, c, d] + # [c, d, f] + # + # [a, b, c] == [c, d, f] + # + # [a, b, c, d] + # [d, f] + # + # [a, b] == [d, f] + # + # [a, b, c, d] + # [f] + # + # [a] == [f] + right_length = len(right_sequence) + for i in range(1, left_length + right_length): + # epsilon to favor long perfect matches + eps = i / 10000.0 + + # Slightly convoluted because we don't want out of bound indices + # This will be necessary for a small conflict resolution optimization + # later + left_start = max(0, left_length - i) + left_stop = min(left_length, left_length + right_length - i) + left = np.array(left_sequence[left_start:left_stop]) + + right_start = max(0, i - left_length) + right_stop = min(right_length, i) + right = np.array(right_sequence[right_start:right_stop]) + + # We can only match subsequences of the same size. + if len(left) != len(right): + raise RuntimeError( + "There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference." + ) + + matches = np.sum(left == right) + matching = matches / i + eps + if matches > 1 and matching > max_: + max_ = matching + max_indices = (left_start, left_stop, right_start, right_stop) + + (left_start, left_stop, right_start, right_stop) = max_indices + + # This is a small conflict optimization since those sequences overlap + # in audio. + # We're going to give more confidence to the left sequence + # for the left of the overlap, + # and to the right of the sequence, for the right of the overlap + left_mid = (left_stop + left_start) // 2 + right_mid = (right_stop + right_start) // 2 + total_sequence.extend(left_sequence[:left_mid]) + left_sequence = right_sequence[right_mid:] + left_length = len(left_sequence) + + if token_timestamp_sequences: + total_token_timestamp_sequence.extend(left_token_timestamp_sequence[:left_mid]) + left_token_timestamp_sequence = token_timestamp_sequences[seq_idx + 1][right_mid:] + + total_sequence.extend(left_sequence) + + if token_timestamp_sequences is None: + return total_sequence + + if len(token_timestamp_sequences) > 0: + total_token_timestamp_sequence.extend(left_token_timestamp_sequence) + return total_sequence, total_token_timestamp_sequence + else: + return total_sequence, [] + + +def _collate_word_timestamps(tokenizer, tokens, token_timestamps, language): + words, _, token_indices = _combine_tokens_into_words(tokenizer, tokens, language) + timings = [ + { + "text": word, + "timestamp": (token_timestamps[indices[0]][0], token_timestamps[indices[-1]][1]), + } + for word, indices in zip(words, token_indices) + ] + return timings + + +def _combine_tokens_into_words( + tokenizer, + tokens: List[int], + language: str = None, + prepend_punctuations: str = "\"'“¡¿([{-", + append_punctuations: str = "\"'.。,，!！?？:：”)]}、", +): + """ + Groups tokens by word. Returns a tuple containing a list of strings with the words, and a list of `token_id` + sequences with the tokens making up each word. + """ + if language is None: + language = tokenizer.language + if language is None: + language = "english" + + if language in {"chinese", "japanese", "thai", "lao", "myanmar", "cantonese"}: + # These languages don't typically use spaces. + words, word_tokens, token_indices = _split_tokens_on_unicode(tokenizer, tokens) + else: + words, word_tokens, token_indices = _split_tokens_on_spaces(tokenizer, tokens) + + _merge_punctuations(words, word_tokens, token_indices, prepend_punctuations, append_punctuations) + return words, word_tokens, token_indices + + +def _split_tokens_on_unicode(tokenizer, tokens: List[int]): + """Combine tokens into words by splitting at any position where the tokens are decoded as valid unicode points.""" + decoded_full = tokenizer.decode(tokens, decode_with_timestamps=True) + replacement_char = "\ufffd" + + words = [] + word_tokens = [] + token_indices = [] + current_tokens = [] + current_indices = [] + unicode_offset = 0 + + for token_idx, token in enumerate(tokens): + current_tokens.append(token) + current_indices.append(token_idx) + decoded = tokenizer.decode(current_tokens, decode_with_timestamps=True) + + if ( + replacement_char not in decoded + or decoded_full[unicode_offset + decoded.index(replacement_char)] == replacement_char + ): + words.append(decoded) + word_tokens.append(current_tokens) + token_indices.append(current_indices) + current_tokens = [] + current_indices = [] + unicode_offset += len(decoded) + + return words, word_tokens, token_indices + + +def _split_tokens_on_spaces(tokenizer, tokens: List[int]): + """Combine tokens into words by splitting at whitespace and punctuation tokens.""" + subwords, subword_tokens_list, subword_indices_list = _split_tokens_on_unicode(tokenizer, tokens) + words = [] + word_tokens = [] + token_indices = [] + + for subword, subword_tokens, subword_indices in zip(subwords, subword_tokens_list, subword_indices_list): + special = subword_tokens[0] >= tokenizer.eos_token_id + with_space = subword.startswith(" ") + punctuation = subword.strip() in "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~" + + if special or with_space or punctuation or len(words) == 0: + words.append(subword) + word_tokens.append(subword_tokens) + token_indices.append(subword_indices) + else: + words[-1] = words[-1] + subword + word_tokens[-1].extend(subword_tokens) + token_indices[-1].extend(subword_indices) + + return words, word_tokens, token_indices + + +def _merge_punctuations(words, tokens, indices, prepended, appended): + """Merges punctuation tokens with neighboring words.""" + # prepend punctuations + i = len(words) - 2 + j = len(words) - 1 + while i >= 0: + if words[i].startswith(" ") and words[i].strip() in prepended: + words[j] = words[i] + words[j] + tokens[j] = tokens[i] + tokens[j] + indices[j] = indices[i] + indices[j] + words[i] = "" + tokens[i] = [] + indices[i] = [] + else: + j = i + i -= 1 + + # append punctuations + i = 0 + j = 1 + while j < len(words): + if not words[i].endswith(" ") and words[j] in appended: + words[i] += words[j] + tokens[i] += tokens[j] + indices[i] += indices[j] + words[j] = "" + tokens[j] = [] + indices[j] = [] + else: + i = j + j += 1 + + # remove elements that are now empty + words[:] = [word for word in words if word] + tokens[:] = [token for token in tokens if token] + indices[:] = [idx for idx in indices if idx] diff --git a/venv/lib/python3.10/site-packages/transformers/models/whisper/tokenization_whisper_fast.py b/venv/lib/python3.10/site-packages/transformers/models/whisper/tokenization_whisper_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..0463d521d5839c581ccb62ac6b43eb97b2f2d62e --- /dev/null +++ b/venv/lib/python3.10/site-packages/transformers/models/whisper/tokenization_whisper_fast.py @@ -0,0 +1,600 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for Whisper.""" +import json +import os +import re +import warnings +from functools import lru_cache +from typing import List, Optional, Tuple + +import numpy as np +from tokenizers import AddedToken, pre_tokenizers, processors + +from ...tokenization_utils_base import BatchEncoding +from ...tokenization_utils_fast import PreTrainedTokenizerFast +from ...utils import logging +from .english_normalizer import BasicTextNormalizer, EnglishTextNormalizer +from .tokenization_whisper import LANGUAGES, TASK_IDS, TO_LANGUAGE_CODE, WhisperTokenizer, _decode_asr + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = { + "vocab_file": "vocab.json", + "tokenizer_file": "tokenizer.json", + "merges_file": "merges.txt", + "normalizer_file": "normalizer.json", +} + + +class WhisperTokenizerFast(PreTrainedTokenizerFast): + """ + Construct a "fast" Whisper tokenizer (backed by HuggingFace's *tokenizers* library). + + This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Args: + vocab_file (`str`, *optional*): + Path to the vocabulary file. + merges_file (`str`, *optional*): + Path to the merges file. + normalizer_file (`str`, *optional*): + Path to the normalizer_file file. + tokenizer_file (`str`, *optional*): + Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that + contains everything needed to load the tokenizer. + unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): + The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as + `"<|startoftranscript|>"` when generating. + eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): + The end of sequence token. + add_prefix_space (`bool`, *optional*, defaults to `False`): + Whether or not to add an initial space to the input. This allows to treat the leading word just as any + other word. (Whisper tokenizer detect beginning of words by the preceding space). + language (`str`, *optional*): + The language of the transcription text. The corresponding language id token is appended to the start of the + sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token + `"<|es|>"` is appended to the start of sequence. This should be used for multilingual fine-tuning only. + task (`str`, *optional*): + Task identifier to append at the start of sequence (if any). This should be used for mulitlingual + fine-tuning, with `"transcribe"` for speech recognition and `"translate"` for speech translation. + predict_timestamps (`bool`, *optional*, defaults to `False`): + Whether to omit the `<|notimestamps|>` token at the start of the sequence. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + slow_tokenizer_class = WhisperTokenizer + + def __init__( + self, + vocab_file=None, + merges_file=None, + normalizer_file=None, + tokenizer_file=None, + unk_token="<|endoftext|>", + bos_token="<|endoftext|>", + eos_token="<|endoftext|>", + add_prefix_space=False, + language=None, + task=None, + predict_timestamps=False, + **kwargs, + ): + bos_token = ( + AddedToken(bos_token, lstrip=False, rstrip=False, normalized=False, special=True) + if isinstance(bos_token, str) + else bos_token + ) + eos_token = ( + AddedToken(eos_token, lstrip=False, rstrip=False, normalized=False, special=True) + if isinstance(eos_token, str) + else eos_token + ) + unk_token = ( + AddedToken(unk_token, lstrip=False, rstrip=False, normalized=False, special=True) + if isinstance(unk_token, str) + else unk_token + ) + + super().__init__( + vocab_file, + merges_file, + tokenizer_file=tokenizer_file, + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + add_prefix_space=add_prefix_space, + **kwargs, + ) + + self.add_bos_token = kwargs.pop("add_bos_token", False) + + pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) + if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: + pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) + pre_tok_state["add_prefix_space"] = add_prefix_space + self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) + + if normalizer_file is not None: + with open(normalizer_file, encoding="utf-8") as vocab_handle: + self.english_spelling_normalizer = json.load(vocab_handle) + else: + self.english_spelling_normalizer = None + + self.add_prefix_space = add_prefix_space + self.timestamp_pat = re.compile(r"<\|(\d+\.\d+)\|>") + + self.language = language + self.task = task + self.predict_timestamps = predict_timestamps + + # Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._batch_encode_plus + def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: + is_split_into_words = kwargs.get("is_split_into_words", False) + assert self.add_prefix_space or not is_split_into_words, ( + f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " + "to use it with pretokenized inputs." + ) + + return super()._batch_encode_plus(*args, **kwargs) + + # Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._encode_plus + def _encode_plus(self, *args, **kwargs) -> BatchEncoding: + is_split_into_words = kwargs.get("is_split_into_words", False) + + assert self.add_prefix_space or not is_split_into_words, ( + f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " + "to use it with pretokenized inputs." + ) + + return super()._encode_plus(*args, **kwargs) + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._decode_with_timestamps + def _decode_with_timestamps(self, token_ids, skip_special_tokens=False, time_precision=0.02) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. This method decodes + given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + timestamp_begin = self.all_special_ids[-1] + 1 + outputs = [[]] + + cur_max_timestamp = 0.0 + prev_segments_len = 0.0 + + for token in token_ids: + if token >= timestamp_begin: + timestamp = float((token - timestamp_begin) * time_precision) + + if timestamp < cur_max_timestamp: + # next segment has started + prev_segments_len += cur_max_timestamp + + cur_max_timestamp = timestamp + + outputs.append(f"<|{(timestamp + prev_segments_len):.2f}|>") + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [ + s if isinstance(s, str) else self.decode(s, skip_special_tokens=skip_special_tokens) for s in outputs + ] + return "".join(outputs) + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._compute_offsets + def _compute_offsets(self, token_ids, time_precision=0.02): + """ + Compute offsets for a given tokenized input + + Args: + token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Can be obtained using the `__call__` method. + time_precision (`float`, `optional`, defaults to 0.02): + The time ratio to convert from token to time. + """ + offsets = [] + # ensure torch tensor of token ids is placed on cpu + if "torch" in str(type(token_ids)) and (hasattr(token_ids, "cpu") and callable(token_ids.cpu)): + token_ids = token_ids.cpu() + token_ids = np.array(token_ids) + if token_ids.shape[0] > 1 and len(token_ids.shape) > 1: + raise ValueError("Can only process a single input at a time") + timestamp_begin = self.all_special_ids[-1] + 1 + timestamp_tokens = token_ids >= timestamp_begin + + consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1 + if consecutive.shape[0] == 0 and timestamp_tokens.sum() <= 1: + # either there are no timestamps or there are no consecutive ones + return [] + elif np.where(timestamp_tokens)[0][-1] + 1 not in consecutive: + # we add the final timestamp if it is not already in the list + consecutive = np.append(consecutive, np.where(timestamp_tokens)[0][-1] + 1) + + last_slice = np.where(timestamp_tokens)[0][0] + for current_slice in consecutive: + sliced_tokens = token_ids[last_slice:current_slice] + if len(sliced_tokens) > 1: + start_timestamp_position = sliced_tokens[0].item() - timestamp_begin + end_timestamp_position = sliced_tokens[-1].item() - timestamp_begin + # strip timestamp tokens from the text output + sliced_tokens = self._preprocess_token_ids(sliced_tokens) + text = self._decode(sliced_tokens) + text = self._filter_timestamp_ids(text) + offsets.append( + { + "text": text, + "timestamp": ( + start_timestamp_position * time_precision, + end_timestamp_position * time_precision, + ), + } + ) + last_slice = current_slice + + return offsets + + @lru_cache + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.timestamp_ids + def timestamp_ids(self, time_precision=0.02): + """ + Compute the timestamp token ids for a given precision and save to least-recently used (LRU) cache. + + Args: + time_precision (`float`, `optional`, defaults to 0.02): + The time ratio to convert from token to time. + """ + return self.convert_tokens_to_ids([("<|%.2f|>" % (i * time_precision)) for i in range(1500 + 1)]) + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._preprocess_token_ids + def _preprocess_token_ids(self, token_ids, skip_special_tokens: bool = False): + """ + Pre-process the token ids for decoding by removing the prompt tokens ids and timestamp token ids. + + Args: + token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Typically, obtained using the `__call__` method of the tokenizer. + skip_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to remove special tokens from the token ids. If `True`, the prompt token ids will be + removed. + """ + if skip_special_tokens: + prompt_token_id = self.convert_tokens_to_ids("<|startofprev|>") + decoder_start_token_id = self.convert_tokens_to_ids("<|startoftranscript|>") + token_ids = self._strip_prompt(token_ids, prompt_token_id, decoder_start_token_id) + + return token_ids + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._filter_timestamp_ids + def _filter_timestamp_ids(self, token_ids): + return re.sub(self.timestamp_pat, "", token_ids) + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.decode + def decode( + self, + token_ids, + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: bool = None, + output_offsets: bool = False, + time_precision: float = 0.02, + decode_with_timestamps: bool = False, + normalize: bool = False, + basic_normalize: bool = False, + remove_diacritics: bool = False, + **kwargs, + ) -> str: + """ + Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special + tokens and clean up tokenization spaces. + + Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. + + Args: + token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Can be obtained using the `__call__` method. + skip_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to remove special tokens in the decoding. + clean_up_tokenization_spaces (`bool`, *optional*): + Whether or not to clean up the tokenization spaces. If `None`, will default to + `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). + output_offsets (`bool`, *optional*, defaults to `False`): + Whether or not to output the offsets of the tokens. This should only be set if the model predicted + timestamps. + time_precision (`float`, `optional`, defaults to 0.02): + The time ratio to convert from token to time. + decode_with_timestamps (`bool`, *optional*, defaults to `False`): + Whether or not to decode with timestamps included in the raw text. + normalize (`bool`, *optional*, defaults to `False`): + Whether or not to apply the English text normalizer to the decoded text. Only applicable when the + target text is in English. Otherwise, the basic text normalizer should be applied. + basic_normalize (`bool`, *optional*, defaults to `False`): + Whether or not to apply the Basic text normalizer to the decoded text. Applicable to multilingual + target text. + remove_diacritics (`bool`, *optional*, defaults to `False`): + Whether or not to remove diacritics when applying the Basic text normalizer. Removing diacritics may + destroy information in the decoded text, hence it should be used with caution. + kwargs (additional keyword arguments, *optional*): + Will be passed to the underlying model specific decode method. + Returns: + `str`: The decoded sentence. + """ + filtered_ids = self._preprocess_token_ids( + token_ids, + skip_special_tokens=skip_special_tokens, + ) + + text = super().decode( + filtered_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + normalize=normalize, + basic_normalize=basic_normalize, + remove_diacritics=remove_diacritics, + **kwargs, + ) + if decode_with_timestamps: + # legacy method to decode timestamps when not included in the tokenizer vocabulary + text = self._decode_with_timestamps( + filtered_ids, time_precision=time_precision, skip_special_tokens=skip_special_tokens + ) + else: + text = self._filter_timestamp_ids(text) + + # retrieve offsets + if output_offsets: + offsets = self._compute_offsets(token_ids, time_precision=time_precision) + return {"text": text, "offsets": offsets} + return text + + def _decode( + self, *args, normalize: bool = False, basic_normalize: bool = False, remove_diacritics: bool = False, **kwargs + ) -> str: + text = super()._decode(*args, **kwargs) + + if normalize: + clean_text = self._normalize(text) + return clean_text + elif basic_normalize: + clean_text = self._basic_normalize(text, remove_diacritics=remove_diacritics) + return clean_text + else: + return text + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._normalize + def _normalize(self, text): + warnings.warn( + "The private method `_normalize` is deprecated and will be removed in v5 of Transformers." + "You can normalize an input string using the Whisper English normalizer using the `normalize` method." + ) + return self.normalize(text) + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._basic_normalize + def _basic_normalize(self, text, remove_diacritics=False): + warnings.warn( + "The private method `_basic_normalize` is deprecated and will be removed in v5 of Transformers." + "You can normalize an input string using the Whisper basic normalizer using the `basic_normalize` method." + ) + return self.basic_normalize(text, remove_diacritics=remove_diacritics) + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.normalize + def normalize(self, text): + """ + Normalize a given string using the `EnglishTextNormalizer` class, which preforms commons transformation on + english text. + """ + normalizer = EnglishTextNormalizer(self.english_spelling_normalizer) + return normalizer(text) + + @staticmethod + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.basic_normalize + def basic_normalize(text, remove_diacritics=False): + """ + Normalize a given string using the `BasicTextNormalizer` class, which preforms commons transformation on + multilingual text. + """ + normalizer = BasicTextNormalizer(remove_diacritics=remove_diacritics) + return normalizer(text) + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + files = self._tokenizer.model.save(save_directory, name=filename_prefix) + + normalizer_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["normalizer_file"] + ) + + if self.english_spelling_normalizer is not None: + with open(normalizer_file, "w", encoding="utf-8") as f: + f.write( + json.dumps(self.english_spelling_normalizer, indent=2, sort_keys=True, ensure_ascii=False) + "\n" + ) + + return tuple(files) + (normalizer_file,) + + def set_prefix_tokens(self, language: str = None, task: str = None, predict_timestamps: bool = None): + """ + Override the prefix tokens appended to the start of the label sequence. This method can be used standalone to + update the prefix tokens as required when fine-tuning. Example: + + ```python + >>> # instantiate the tokenizer and set the prefix token to Spanish + >>> tokenizer = WhisperTokenizerFast.from_pretrained("openai/whisper-tiny", language="spanish") + >>> # now switch the prefix token from Spanish to French + >>> tokenizer.set_prefix_tokens(language="french") + ``` + + Args: + language (`str`, *optional*, defaults to `None`): + The language of the transcription text. + task (`str`, *optional*, defaults to `None`): + Task identifier to append at the start of sequence (if any). + predict_timestamps (`bool`, *optional*, defaults to `None`): + Whether to omit the `<|notimestamps|>` token at the start of the sequence. + """ + self.language = language if language is not None else self.language + self.task = task if task is not None else self.task + self.predict_timestamps = predict_timestamps if predict_timestamps is not None else self.predict_timestamps + + prefix_token_ids = self.prefix_tokens + prefixes = self.convert_ids_to_tokens(prefix_token_ids) + eos = self.eos_token + eos_token_id = self.eos_token_id + prefix_template = " ".join([f"{token}:0" for token in prefixes]) + self.backend_tokenizer.post_processor = processors.TemplateProcessing( + single=f"{prefix_template} $A:0 {eos}:0", + pair=f"{prefix_template} $A:0 $B:1 {eos}:1", + special_tokens=[ + (eos, eos_token_id), + *zip(prefixes, prefix_token_ids), + ], + ) + + @property + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.prefix_tokens + def prefix_tokens(self) -> List[int]: + bos_token_id = self.convert_tokens_to_ids("<|startoftranscript|>") + translate_token_id = self.convert_tokens_to_ids("<|translate|>") + transcribe_token_id = self.convert_tokens_to_ids("<|transcribe|>") + notimestamps_token_id = self.convert_tokens_to_ids("<|notimestamps|>") + langs = tuple(LANGUAGES.keys()) + + if self.language is not None: + self.language = self.language.lower() + if self.language in TO_LANGUAGE_CODE: + language_id = TO_LANGUAGE_CODE[self.language] + elif self.language in TO_LANGUAGE_CODE.values(): + language_id = self.language + else: + is_language_code = len(self.language) == 2 + raise ValueError( + f"Unsupported language: {self.language}. Language should be one of:" + f" {list(TO_LANGUAGE_CODE.values()) if is_language_code else list(TO_LANGUAGE_CODE.keys())}." + ) + + if self.task is not None: + if self.task not in TASK_IDS: + raise ValueError(f"Unsupported task: {self.task}. Task should be in: {TASK_IDS}") + + bos_sequence = [bos_token_id] + if self.language is not None: + bos_sequence.append(bos_token_id + 1 + langs.index(language_id)) + if self.task is not None: + bos_sequence.append(transcribe_token_id if self.task == "transcribe" else translate_token_id) + if not self.predict_timestamps: + bos_sequence.append(notimestamps_token_id) + return bos_sequence + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.build_inputs_with_special_tokens + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]: + """Build model inputs from a sequence by appending eos_token_id.""" + if token_ids_1 is None: + return self.prefix_tokens + token_ids_0 + [self.eos_token_id] + # We don't expect to process pairs, but leave the pair logic for API consistency + return self.prefix_tokens + token_ids_0 + token_ids_1 + [self.eos_token_id] + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.get_special_tokens_mask + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + prefix_ones = [1] * len(self.prefix_tokens) + suffix_ones = [1] + if token_ids_1 is None: + return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones + return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones + + @property + # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.default_chat_template + def default_chat_template(self): + """ + A simple chat template that ignores role information and just concatenates messages with EOS tokens. + """ + logger.warning_once( + "\nNo chat template is defined for this tokenizer - using the default template " + f"for the {self.__class__.__name__} class. If the default is not appropriate for " + "your model, please set `tokenizer.chat_template` to an appropriate template. " + "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n" + ) + return "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}" + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.get_decoder_prompt_ids + def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True): + self.set_prefix_tokens(task=task, language=language, predict_timestamps=not no_timestamps) + # prefix tokens are of the form: <|startoftranscript|> <|lang_id|> <|task|> <|notimestamps|> + # we don't want to force the bos token at position 1, as this is the starting token + # when we generate, so we slice the prefix tokens to: <|lang_id|> <|task|> <|notimestamps|> + # to get the forced tokens + forced_tokens = self.prefix_tokens[1:] + forced_decoder_ids = [(rank + 1, token) for rank, token in enumerate(forced_tokens)] + return forced_decoder_ids + + def _decode_asr(self, model_outputs, *, return_timestamps, return_language, time_precision): + return _decode_asr( + self, + model_outputs, + return_timestamps=return_timestamps, + return_language=return_language, + time_precision=time_precision, + ) + + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.get_prompt_ids + def get_prompt_ids(self, text: str, return_tensors="np"): + """Converts prompt text to IDs that can be passed to [`~WhisperForConditionalGeneration.generate`].""" + batch_encoding = self("<|startofprev|>", " " + text.strip(), add_special_tokens=False) + + # Check for special tokens + prompt_text_ids = batch_encoding["input_ids"][1:] + special_token_id = next((x for x in prompt_text_ids if x >= self.all_special_ids[0]), None) + if special_token_id is not None: + token = self.convert_ids_to_tokens(special_token_id) + raise ValueError(f"Encountered text in the prompt corresponding to disallowed special token: {token}.") + + batch_encoding.convert_to_tensors(tensor_type=return_tensors) + return batch_encoding["input_ids"] + + @staticmethod + # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._strip_prompt + def _strip_prompt(token_ids: List[int], prompt_token_id: int, decoder_start_token_id: int): + has_prompt = isinstance(token_ids, list) and token_ids and token_ids[0] == prompt_token_id + if has_prompt: + if decoder_start_token_id in token_ids: + return token_ids[token_ids.index(decoder_start_token_id) :] + else: + return [] + + return token_ids