Create README.md

README.md

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+---
+language:
+  - en
+tags:
+  - audio
+  - automatic-speech-recognition
+license: mit
+---
+
+# Distil-Whisper: distil-large-v3 for OpenAI Whisper
+
+This repository contains the model weights for [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
+converted to [OpenAI Whisper](https://github.com/openai/whisper) format.
+
+Compared to previous Distil-Whisper releases, distil-large-v3 is specifically designed to give one-to-one equivalence
+with the OpenAI Whisper long-form transcription algorithm. In our benchmark over 4 out-of-distribution datasets, distil-large-v3
+outperformed distil-large-v2 by 5% WER average. Thus, you can expect significant performance gains by switching to this 
+latest checkpoint.
+
+## Usage
+
+To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed.
+For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub:
+
+```bash
+pip install --upgrade pip
+pip install --upgrade openai-whisper datasets[audio]
+```
+
+The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using 
+🤗 Datasets:
+
+```python
+from huggingface_hub import hf_hub_download
+from datasets import load_dataset
+from whisper import load_model, transcribe
+
+model_path = hf_hub_download(repo_id="distil-whisper/distil-large-v3-openai", filename="model.bin")
+model = load_model(model_path)
+
+dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+sample = dataset[0]["audio"]["path"]
+
+pred_out = transcribe(model, audio=sample)
+print(pred_out["text"])
+```
+
+Note that the model weights will be downloaded and saved to your cache the first time you run the example. Subsequently,
+you can re-use the same example, and the weights will be loaded directly from your cache without having to download them
+again.
+
+To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe:
+
+```python
+pred_out = transcribe(model, audio="audio.mp3")
+```
+
+## Model Details
+
+For more information about the distil-large-v3 model, refer to the original [model card](https://huggingface.co/distil-whisper/distil-large-v3).
+
+## License
+
+Distil-Whisper inherits the [MIT license](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) from OpenAI's Whisper model.
+
+## Citation
+
+If you use this model, please consider citing the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430):
+```
+@misc{gandhi2023distilwhisper,
+      title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling}, 
+      author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
+      year={2023},
+      eprint={2311.00430},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```