Instructions to use tinytrashlabs/LuxTTS-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tinytrashlabs/LuxTTS-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LuxTTS-mlx tinytrashlabs/LuxTTS-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
LuxTTS β MLX weights (fp32)
LuxTTS converted to MLX-native safetensors, for Apple silicon. Same model, same precision β a key-remap and a conv-layout transpose, nothing retrained and nothing quantized.
Upstream ships model.pt (torch) and ONNX graphs. Neither loads in MLX, so
every MLX consumer had to run the conversion itself and keep the result
somewhere local. This repo is that result, published once.
Files
| File | Size | |
|---|---|---|
lux_model.safetensors |
468 MB | ZipVoiceDistill β fm_decoder + text_encoder + embeddings |
lux_vocoder.safetensors |
61 MB | Vocos vocoder, MLX conv layout |
config.json |
β | model dimensions, 24 kHz feature config |
tokens.txt |
β | phoneme vocabulary |
fp32 on purpose. fp16 halves the download and is indistinguishable on a long, clean reference β and audibly worse on a short one. Blind A/B on a clone made from an 8-second phone recording: 4 of 8 fp16 renders had artefacts a listener rejected, against 0 of 8 at fp32. If you are cloning from short user recordings, use these.
Use
// EngineKit (Gloam Voice Studio)
let model = try await LuxSpeechModel.load(from: weightsDirectory)
The directory needs all four files side by side.
Provenance and license
Converted from YatharthS/LuxTTS, Apache-2.0, which this repo inherits. The vocoder is Vocos. Conversion and publication by Tiny Trash Labs for Gloam Voice Studio.
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Quantized
Model tree for tinytrashlabs/LuxTTS-mlx
Base model
YatharthS/LuxTTS