Update README.md

README.md

@@ -4,77 +4,6 @@ pipeline_tag: automatic-speech-recognition
 inference: true
 ---
 
-This model is for debugging. It is randomly initialized with the config from [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) but is of smaller size. 
+This model is a smaller, randomly initialized version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3). It's intended use is for debugging, for fast testing or for use in CI/CD pipelines. 
 
-Codes:
-```python
-import os
-
-import torch
-
-from huggingface_hub import create_repo, upload_folder
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    GenerationConfig,
-    AutoConfig,
-    pipeline,
-    set_seed,
-)
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline, AutoConfig
-from datasets import load_dataset
-
-model_id = "openai/whisper-large-v3"
-repo_id = "yujiepan/whisper-v3-tiny-random"
-save_path = f"/tmp/{repo_id}"
-os.system(f'rm -rf {save_path}')
-os.makedirs(save_path, exist_ok=True)
-
-device = "cuda"
-torch_dtype = torch.float16
-model_id = "openai/whisper-large-v3"
-
-config = AutoConfig.from_pretrained(model_id)
-config.num_hidden_layers = 2
-config.d_model = 8
-config.decoder_attention_heads = 2
-config.decoder_ffn_dim = 16
-config.decoder_layers = 2
-config.encoder_ffn_dim = 16
-config.encoder_attention_heads = 2
-config.encoder_layers = 2
-
-model = AutoModelForSpeechSeq2Seq.from_config(config)
-model.to(device).to(torch_dtype)
-model.generation_config = GenerationConfig.from_pretrained(model_id)
-processor = AutoProcessor.from_pretrained(model_id)
-
-set_seed(42)
-num_params = 0
-with torch.no_grad():
-    for name, p in sorted(model.named_parameters()):
-        print(name, p.shape)
-        torch.nn.init.uniform_(p, -0.5, 0.5)
-        num_params += p.numel()
-print("Total number of parameters:", num_params)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-sample = load_dataset(
-    "distil-whisper/librispeech_long", "clean",
-    split="validation",
-)[0]["audio"]
-result = pipe(sample, return_timestamps=True)
-print(result["text"])
-
-create_repo(repo_id, exist_ok=True)
-upload_folder(repo_id=repo_id, folder_path=save_path, repo_type='model')
-```
+This model was taken from [this repository](https://huggingface.co/yujiepan/whisper-v3-tiny-random) and updated / maintained to work with newer transformers versions.