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Add notes about conversion

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bofenghuang
2023-12-29 17:20:41 +01:00
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whisper/README.md
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@@ -6,7 +6,7 @@ parameters[^1].
### Setup ### Setup
First, install the dependencies. First, install the dependencies:
``` ```
pip install -r requirements.txt pip install -r requirements.txt
@@ -19,6 +19,27 @@ Install [`ffmpeg`](https://ffmpeg.org/):
brew install ffmpeg brew install ffmpeg
``` ```
Next, download the Whisper PyTorch checkpoint and convert the weights to MLX format:
```
# Take the "tiny" model as an example. Note that you can also convert a local PyTorch checkpoint in OpenAI's format.
python convert.py --torch-name-or-path tiny --mlx-path mlx_models/tiny
```
To generate a 4-bit quantized model, use ``-q`` for a full list of options:
```
python convert.py --help
```
By default, the conversion script will make the directory `mlx_models/tiny` and save
the converted `weights.npz` and `config.json` there.
> [!TIP]
> Alternatively, you can also download a few converted checkpoints from the
> [MLX Community](https://huggingface.co/mlx-community) organization on Hugging
> Face and skip the conversion step.
### Run ### Run
Transcribe audio with: Transcribe audio with: