Instructions to use vanch007/Audio8-TTS-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vanch007/Audio8-TTS-MLX-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Audio8-TTS-MLX-8bit vanch007/Audio8-TTS-MLX-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Audio8-TTS-MLX-8bit
Native MLX 8-bit release of Audio8/Audio8-TTS-Preview-0.6b for Apple Silicon.
Inference code, installation, API documentation, tests, and benchmark evidence: vanch007/mlx-audio8-tts.
Artifact
- Affine 8-bit, group size 64,
sensitive-bf16policy. - 827 MiB language-model weights; 2.08 GiB complete repository download.
- The shared 1.26 GiB neural codec, embeddings, and Fast AR depth decoder are kept at higher precision to protect speech quality.
- 44,100 Hz output, 10 acoustic codebooks.
M3 Max benchmark
Seeded post-warm-up RTF on the release checkpoint: 0.983 English, 0.922 Chinese, and 0.793 Cantonese. Model download, loading, and warm-up are excluded. Lower is better; values below 1.0 are faster than real-time. The reproducible script and report are published with the source project.
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-8bit \
--text "你好,欢迎使用 MLX Audio8 TTS。" \
--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
·
F32 ·
U32 ·
Hardware compatibility
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8-bit
Model tree for vanch007/Audio8-TTS-MLX-8bit
Base model
Edge0/Audio8-TTS-Preview-0.6b