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581a5733a1
* Add option to load customized mlx model * Add quantization * Apply reviews * Separate model conversion and loading * Update test * Fix benchmark * Add notes about conversion * Improve doc
55 lines
1.4 KiB
Markdown
55 lines
1.4 KiB
Markdown
# Whisper
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Speech recognition with Whisper in MLX. Whisper is a set of open source speech
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recognition models from OpenAI, ranging from 39 million to 1.5 billion
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parameters[^1].
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### Setup
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First, install the dependencies:
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```
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pip install -r requirements.txt
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```
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Install [`ffmpeg`](https://ffmpeg.org/):
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```
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# on macOS using Homebrew (https://brew.sh/)
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brew install ffmpeg
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```
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Next, download the Whisper PyTorch checkpoint and convert the weights to the MLX format. For example, to convert the `tiny` model use:
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```
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python convert.py --torch-name-or-path tiny --mlx-path mlx_models/tiny
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```
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Note you can also convert a local PyTorch checkpoint which is in the original OpenAI format.
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To generate a 4-bit quantized model, use `-q`. For a full list of options:
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```
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python convert.py --help
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```
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By default, the conversion script will make the directory `mlx_models/tiny` and save
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the converted `weights.npz` and `config.json` there.
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> [!TIP]
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> Alternatively, you can also download a few converted checkpoints from the
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> [MLX Community](https://huggingface.co/mlx-community) organization on Hugging
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> Face and skip the conversion step.
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### Run
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Transcribe audio with:
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```
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import whisper
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text = whisper.transcribe(speech_file)["text"]
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```
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[^1]: Refer to the [arXiv paper](https://arxiv.org/abs/2212.04356), [blog post](https://openai.com/research/whisper), and [code](https://github.com/openai/whisper) for more details.
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