Instructions to use aufklarer/Chatterbox-Multilingual-MLX-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aufklarer/Chatterbox-Multilingual-MLX-fp16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Chatterbox-Multilingual-MLX-fp16 aufklarer/Chatterbox-Multilingual-MLX-fp16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
aufklarer/Chatterbox-Multilingual-MLX-fp16
Multilingual Chatterbox (Resemble AI,
MIT) converted to MLX in genuine fp16 for Apple-silicon inference. Unlike
mlx-community/chatterbox-fp16 (stored as F32, 2.6 GB), this bundle stores all
float tensors as fp16 (1.3 GB) with an identical key layout.
Zero-shot voice cloning from a short reference clip across 23 languages
(incl. Arabic and Hindi). Component prefixes: ve.* (voice encoder), t3.*
(text→speech-token T3), s3gen.* (token→waveform S3Gen).
Note: requires the S3Tokenizer weights from mlx-community/S3TokenizerV2, downloaded automatically at runtime.
Use with mlx-audio
pip install -U mlx-audio
mlx_audio.tts.generate --model aufklarer/Chatterbox-Multilingual-MLX-fp16 --text "[ar] مرحبا" --ref_audio reference.wav
Converted with models/chatterbox/export/convert.py (speech-models).
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Model size
0.6B params
Tensor type
F16
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Hardware compatibility
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Model tree for aufklarer/Chatterbox-Multilingual-MLX-fp16
Base model
ResembleAI/chatterbox