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https://github.com/ml-explore/mlx-examples.git
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bf9926489e
* Add word timestamps and confidence scores * Create a separate forward_with_cross_qk function * Move multiple ops from np to mlx, clean comments * Save alignment_heads * Cast qk to fp32 * Add test for word-level timestamps and confidence scores * format + readme * nit --------- Co-authored-by: Awni Hannun <awni@apple.com>
70 lines
1.7 KiB
Markdown
70 lines
1.7 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
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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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```python
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import whisper
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text = whisper.transcribe(speech_file)["text"]
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```
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The `transcribe` function also supports word-level timestamps. You can generate
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these with:
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```python
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output = whisper.transcribe(speech_file, word_timestamps=True)
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print(output["segments"][0]["words"])
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```
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To see more transcription options use:
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```
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>>> help(whisper.transcribe)
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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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