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bofenghuang
2023-12-29 19:09:54 +01:00
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7
whisper/README.md
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@@ -19,14 +19,15 @@ Install [`ffmpeg`](https://ffmpeg.org/):
brew install ffmpeg brew install ffmpeg
``` ```
Next, download the Whisper PyTorch checkpoint and convert the weights to MLX format: Next, download the Whisper PyTorch checkpoint and convert the weights to the MLX format. For example, to convert the `tiny` model use:
``` ```
# 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 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: Note you can also convert a local PyTorch checkpoint which is in the original OpenAI format.
To generate a 4-bit quantized model, use `-q`. For a full list of options:
``` ```
python convert.py --help python convert.py --help

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whisper/convert.py
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@@ -113,7 +113,7 @@ def load_torch_model(
Parameters Parameters
---------- ----------
name_or_path : str name_or_path : str
one of the official model names listed by `whisper.available_models()` or a local Pytorch checkpoint in OpenAI's format one of the official model names listed by `whisper.available_models()` or a local Pytorch checkpoint which is in the original OpenAI format
download_root: str download_root: str
path to download the model files; by default, it uses "~/.cache/whisper" path to download the model files; by default, it uses "~/.cache/whisper"