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--- |
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license: mit |
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tags: |
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- mlx |
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--- |
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# F5 TTS — MLX |
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[F5 TTS](https://arxiv.org/abs/2410.06885) for the [MLX](https://github.com/ml-explore/mlx) framework. |
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This model is reshaped for MLX from the original weights and is designed for use with [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx) |
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F5 TTS is a non-autoregressive, zero-shot text-to-speech system using a flow-matching mel spectrogram generator with a diffusion transformer (DiT). |
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You can listen to a [sample here](https://s3.amazonaws.com/lucasnewman.datasets/f5tts/sample.wav) that was generated in ~11 seconds on an M3 Max MacBook Pro. |
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See [F5-TTS](https://huggingface.co/SWivid/F5-TTS) for the original checkpoint. |
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## Installation |
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```bash |
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pip install f5-tts-mlx |
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``` |
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## Usage |
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```bash |
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python -m f5_tts_mlx.generate --text "The quick brown fox jumped over the lazy dog." |
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``` |
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If you want to use your own reference audio sample, make sure it's a mono, 24kHz wav file of around 5-10 seconds: |
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```bash |
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python -m f5_tts_mlx.generate \ |
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--text "The quick brown fox jumped over the lazy dog." |
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--ref-audio /path/to/audio.wav |
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--ref-text "This is the caption for the reference audio." |
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``` |
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You can convert an audio file to the correct format with ffmpeg like this: |
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```bash |
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ffmpeg -i /path/to/audio.wav -ac 1 -ar 24000 -sample_fmt s16 -t 10 /path/to/output_audio.wav |
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``` |
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See [here](https://github.com/lucasnewman/f5-tts-mlx/tree/main/f5_tts_mlx) for more options to customize generation. |
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— |
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You can load a pretrained model from Python like this: |
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```python |
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from f5_tts_mlx.generate import generate |
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audio = generate(text = "Hello world.", ...) |
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``` |
