Instructions to use vanch007/Audio8-TTS-MLX-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vanch007/Audio8-TTS-MLX-BF16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Audio8-TTS-MLX-BF16 vanch007/Audio8-TTS-MLX-BF16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Audio8-TTS-MLX-BF16
Native MLX BF16 baseline of Audio8/Audio8-TTS-Preview-0.6b for Apple Silicon.
Inference code, installation, API documentation, tests, and benchmark evidence: vanch007/mlx-audio8-tts.
Artifact
- 1,147 MiB language-model weights; 2.39 GiB complete repository download.
- Bundled 1.26 GiB, 44.1 kHz neural codec with 10 acoustic codebooks.
- Baseline for comparing the recommended 8-bit release.
M3 Max benchmark
Measured RTF across the 11-language real-device matrix: 1.245–1.484. The matrix also covers zero-shot voice cloning and streaming. See the evaluation report.
Usage
git clone https://github.com/vanch007/mlx-audio8-tts.git
cd mlx-audio8-tts
pip install -e '.[server]'
mlx-audio8-tts generate \
--model vanch007/Audio8-TTS-MLX-BF16 \
--text "Hello from the MLX BF16 baseline." \
--output output.wav
This is an independent Apache-2.0 MLX conversion. See the upstream project for the original architecture and checkpoint.
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Model size
0.6B params
Tensor type
BF16
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Hardware compatibility
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Base model
Edge0/Audio8-TTS-Preview-0.6b