updated
Browse files- run.sh +6 -6
- run_npsc.sh +37 -0
- run_nst.sh +38 -0
- run_whisper_finetuning.py +78 -31
run.sh
CHANGED
@@ -2,18 +2,17 @@
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python run_whisper_finetuning.py \
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--model_name_or_path="openai/whisper-small" \
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--output_dir="../whisper-testrun1" \
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-
--repo_id="NbAiLab/whisper-testrun1" \
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--overwrite_output_dir=True \
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--language="Norwegian" \
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--task="transcribe" \
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--dataset_name="
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--dataset_config="
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--do_train=True \
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--do_eval=True \
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--audio_column_name="audio" \
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-
--text_column_name="
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--per_device_train_batch_size=
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-
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--learning_rate=2e-5 \
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--warmup_steps=500 \
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--max_steps=10000 \
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@@ -23,6 +22,7 @@ python run_whisper_finetuning.py \
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--evaluation_strategy="steps" \
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--save_steps=1000 \
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--eval_steps=1000 \
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--logging_steps=250 \
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--fp16=True \
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--load_best_model_at_end=True \
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python run_whisper_finetuning.py \
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--model_name_or_path="openai/whisper-small" \
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--output_dir="../whisper-testrun1" \
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--overwrite_output_dir=True \
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--language="Norwegian" \
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--task="transcribe" \
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+
--dataset_name="mozilla-foundation/common_voice_11_0" \
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--dataset_config="nn-NO" \
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--do_train=True \
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--do_eval=True \
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--audio_column_name="audio" \
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--text_column_name="sentence" \
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--per_device_train_batch_size=32 \
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--per_device_train_batch_size=32 \
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--learning_rate=2e-5 \
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--warmup_steps=500 \
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--max_steps=10000 \
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--evaluation_strategy="steps" \
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--save_steps=1000 \
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--eval_steps=1000 \
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+
--max_eval_samples=10 \
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--logging_steps=250 \
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--fp16=True \
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--load_best_model_at_end=True \
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run_npsc.sh
ADDED
@@ -0,0 +1,37 @@
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python run_whisper_finetuning.py \
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--model_name_or_path="openai/whisper-small" \
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--output_dir="../whisper-testrun1" \
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--overwrite_output_dir=True \
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--language="Norwegian" \
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--task="transcribe" \
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--dataset_name="NbAiLab/NPSC" \
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--dataset_config="16K_mp3_bokmaal" \
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--do_train=True \
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--do_eval=True \
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--audio_column_name="audio" \
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--text_column_name="text" \
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--per_device_train_batch_size=16 \
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--per_device_train_batch_size=16 \
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--learning_rate=2e-5 \
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--warmup_steps=500 \
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--max_steps=10000 \
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--gradient_checkpointing=True \
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--gradient_accumulation_steps=1 \
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--group_by_length=False \
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--evaluation_strategy="steps" \
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--save_steps=1000 \
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--eval_steps=1000 \
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--logging_steps=250 \
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--fp16=True \
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--load_best_model_at_end=True \
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--metric_for_best_model="wer" \
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--greater_is_better=False \
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--report_to="tensorboard" \
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--predict_with_generate=True \
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--generation_max_length=225 \
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--print_training_arguments=True \
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--push_to_hub=True
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run_nst.sh
ADDED
@@ -0,0 +1,38 @@
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python run_whisper_finetuning.py \
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--model_name_or_path="openai/whisper-small" \
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--output_dir="../whisper-testrun1" \
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--overwrite_output_dir=True \
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--language="Norwegian" \
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--task="transcribe" \
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--dataset_name="NbAiLab/NST" \
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--dataset_config="no-close" \
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--do_train=True \
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--do_eval=True \
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--audio_column_name="audio" \
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--text_column_name="text" \
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--per_device_train_batch_size=16 \
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--per_device_train_batch_size=16 \
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--learning_rate=2e-5 \
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--warmup_steps=500 \
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--max_steps=10000 \
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--gradient_checkpointing=True \
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--gradient_accumulation_steps=1 \
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--group_by_length=False \
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--evaluation_strategy="steps" \
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--save_steps=1000 \
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--eval_steps=10 \
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--max_eval_samples=100 \
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--logging_steps=250 \
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--fp16=True \
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--load_best_model_at_end=True \
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--metric_for_best_model="wer" \
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--greater_is_better=False \
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--report_to="tensorboard" \
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--predict_with_generate=True \
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--generation_max_length=225 \
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--print_training_arguments=True \
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--push_to_hub=True
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run_whisper_finetuning.py
CHANGED
@@ -51,6 +51,48 @@ from transformers.utils.versions import require_version
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def list_field(default=None, metadata=None):
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return field(default_factory=lambda: default, metadata=metadata)
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@dataclass
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class ModelArguments:
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@@ -134,6 +176,7 @@ class ModelArguments:
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)
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@dataclass
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class DataTrainingArguments:
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"""
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@@ -191,7 +234,7 @@ class DataTrainingArguments:
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
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-
"value if set."
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},
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)
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chars_to_ignore: Optional[List[str]] = list_field(
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@@ -240,19 +283,11 @@ class DataTrainingArguments:
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default="|",
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metadata={"help": "The word delimiter token for the tokenizer"},
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)
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-
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default=True,
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metadata={
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"help": "Output tokens in addition to loss and digits for calculating metrics"},
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)
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generation_max_length: int = field(
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default=225,
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metadata={"help": "Maximum number of tokens generated"},
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)
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phoneme_language: Optional[str] = field(
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default=None,
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metadata={
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-
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" passed to the tokenizer for tokenization. Note that"
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" this is only relevant if the model classifies the"
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" input audio to a sequence of phoneme sequences."
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@@ -303,7 +338,7 @@ def main():
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser(
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-
(ModelArguments, DataTrainingArguments,
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Metrics
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@@ -351,7 +386,7 @@ def main():
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# Load dataset
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train_dataset = load_dataset(data_args.dataset_name, data_args.dataset_config_name, split="train", streaming=True, use_auth_token=True)
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-
eval_dataset = load_dataset(data_args.dataset_name, data_args.dataset_config_name, split="
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# Rename columns
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@@ -373,15 +408,17 @@ def main():
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model_args.model_name_or_path, language=model_args.language, task=model_args.task)
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data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)
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-
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# Prepare data
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-
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-
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# TODO Not able to implement in Streaming mode. Can not find a way to list columns. But is is necessary?
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# train_data = train_data.map(prepare_dataset, remove_columns=train_data.column_names, num_proc=1)
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train_dataset = train_dataset.map(prepare_dataset)
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# Metrics
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metric = evaluate.load("wer")
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@@ -407,8 +444,10 @@ def main():
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# use last checkpoint if exist
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if last_checkpoint is not None:
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checkpoint = last_checkpoint
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elif os.path.isdir(model_args.model_name_or_path):
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checkpoint = model_args.model_name_or_path
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else:
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checkpoint = None
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@@ -423,7 +462,13 @@ def main():
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# Set seed before initializing model.
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set_seed(training_args.seed)
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-
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trainer = Seq2SeqTrainer(
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args=training_args,
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model=model,
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@@ -433,6 +478,7 @@ def main():
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compute_metrics=compute_metrics,
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tokenizer=processor.feature_extractor,
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)
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train_result = trainer.train(resume_from_checkpoint=checkpoint)
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trainer.save_model()
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@@ -448,21 +494,22 @@ def main():
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trainer.create_model_card(**kwargs)
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# TODO - Look closer into the evaluation and the model card writing.
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-
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# Evaluation
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-
results = {}
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if training_args.do_eval:
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-
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-
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# Write model card and (optionally) push to hub
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config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
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def list_field(default=None, metadata=None):
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return field(default_factory=lambda: default, metadata=metadata)
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+
@dataclass
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+
class Seq2SeqTrainingArguments(TrainingArguments):
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"""
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+
Args:
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+
sortish_sampler (`bool`, *optional*, defaults to `False`):
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+
Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset*
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+
for now but will become generally available in the near future.
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+
It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness
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+
for the training set.
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+
predict_with_generate (`bool`, *optional*, defaults to `False`):
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Whether to use generate to calculate generative metrics (ROUGE, BLEU).
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+
generation_max_length (`int`, *optional*):
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+
The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
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+
`max_length` value of the model configuration.
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+
generation_num_beams (`int`, *optional*):
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+
The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
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+
`num_beams` value of the model configuration.
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+
"""
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+
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+
sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
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+
predict_with_generate: bool = field(
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+
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
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+
)
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+
generation_max_length: Optional[int] = field(
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default=None,
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+
metadata={
|
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+
"help": (
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"The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
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"to the `max_length` value of the model configuration."
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+
)
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},
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)
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+
generation_num_beams: Optional[int] = field(
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default=None,
|
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+
metadata={
|
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+
"help": (
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"The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
|
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+
"to the `num_beams` value of the model configuration."
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+
)
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+
},
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)
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+
|
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|
97 |
@dataclass
|
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class ModelArguments:
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)
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|
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+
|
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@dataclass
|
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class DataTrainingArguments:
|
182 |
"""
|
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|
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default=None,
|
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metadata={
|
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"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
|
237 |
+
"value if set. Should also be set when streaming."
|
238 |
},
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)
|
240 |
chars_to_ignore: Optional[List[str]] = list_field(
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|
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default="|",
|
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metadata={"help": "The word delimiter token for the tokenizer"},
|
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)
|
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+
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phoneme_language: Optional[str] = field(
|
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default=None,
|
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metadata={
|
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+
"help": "The target language that should be used be"
|
291 |
" passed to the tokenizer for tokenization. Note that"
|
292 |
" this is only relevant if the model classifies the"
|
293 |
" input audio to a sequence of phoneme sequences."
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|
338 |
# or by passing the --help flag to this script.
|
339 |
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
340 |
parser = HfArgumentParser(
|
341 |
+
(ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
|
342 |
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
343 |
|
344 |
# Metrics
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|
386 |
|
387 |
# Load dataset
|
388 |
train_dataset = load_dataset(data_args.dataset_name, data_args.dataset_config_name, split="train", streaming=True, use_auth_token=True)
|
389 |
+
eval_dataset = load_dataset(data_args.dataset_name, data_args.dataset_config_name, split="test", streaming=True, use_auth_token=True)
|
390 |
|
391 |
|
392 |
# Rename columns
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|
408 |
model_args.model_name_or_path, language=model_args.language, task=model_args.task)
|
409 |
data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)
|
410 |
|
411 |
+
|
412 |
# Prepare data
|
413 |
+
# Is not working.... but since it is already 16000 maybe I dont need it?
|
414 |
+
# train_dataset = train_dataset.cast_column("audio", Audio(sampling_rate=16000))
|
415 |
+
# eval_dataset = eval_dataset.cast_column("audio", Audio(sampling_rate=16000))
|
416 |
|
417 |
# TODO Not able to implement in Streaming mode. Can not find a way to list columns. But is is necessary?
|
418 |
# train_data = train_data.map(prepare_dataset, remove_columns=train_data.column_names, num_proc=1)
|
419 |
|
420 |
train_dataset = train_dataset.map(prepare_dataset)
|
421 |
+
eval_dataset = eval_dataset.map(prepare_dataset)
|
422 |
|
423 |
# Metrics
|
424 |
metric = evaluate.load("wer")
|
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|
444 |
|
445 |
# use last checkpoint if exist
|
446 |
if last_checkpoint is not None:
|
447 |
+
print("*** Found a checkpoint!")
|
448 |
checkpoint = last_checkpoint
|
449 |
elif os.path.isdir(model_args.model_name_or_path):
|
450 |
+
print("*** Loading checkpoint from parameters")
|
451 |
checkpoint = model_args.model_name_or_path
|
452 |
else:
|
453 |
checkpoint = None
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|
462 |
|
463 |
# Set seed before initializing model.
|
464 |
set_seed(training_args.seed)
|
465 |
+
|
466 |
+
# TODO - I think the number of epochs needs to be set manually? Now it seems to be calculated based on the save steps. How do I do this?
|
467 |
+
# Code here
|
468 |
+
|
469 |
+
# Save the processor as well, since we need it later
|
470 |
+
processor.save_pretrained(training_args.output_dir)
|
471 |
+
|
472 |
trainer = Seq2SeqTrainer(
|
473 |
args=training_args,
|
474 |
model=model,
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|
478 |
compute_metrics=compute_metrics,
|
479 |
tokenizer=processor.feature_extractor,
|
480 |
)
|
481 |
+
|
482 |
|
483 |
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
484 |
trainer.save_model()
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|
494 |
trainer.create_model_card(**kwargs)
|
495 |
|
496 |
# TODO - Look closer into the evaluation and the model card writing.
|
497 |
+
|
498 |
+
# breakpoint()
|
499 |
# Evaluation
|
500 |
+
# results = {}
|
501 |
+
# if training_args.do_eval:
|
502 |
+
# logger.info("*** Evaluate ***")
|
503 |
+
# metrics = trainer.evaluate()
|
504 |
+
# max_eval_samples = (
|
505 |
+
# data_args.max_eval_samples if data_args.max_eval_samples is not None else len(
|
506 |
+
# vectorized_datasets["eval"])
|
507 |
+
# )
|
508 |
+
# metrics["eval_samples"] = min(
|
509 |
+
# max_eval_samples, len(vectorized_datasets["eval"]))
|
510 |
+
|
511 |
+
# trainer.log_metrics("eval", metrics)
|
512 |
+
# trainer.save_metrics("eval", metrics)
|
513 |
|
514 |
# Write model card and (optionally) push to hub
|
515 |
config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
|