Instructions to use mlx-community/chatterbox-multilingual-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/chatterbox-multilingual-v3 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir chatterbox-multilingual-v3 mlx-community/chatterbox-multilingual-v3
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
metadata
library_name: mlx-audio
license: mit
base_model:
- ResembleAI/chatterbox
language:
- ar
- da
- de
- el
- en
- es
- fi
- fr
- he
- hi
- it
- ja
- ko
- ms
- nl
- 'no'
- pl
- pt
- ru
- sv
- sw
- tr
- zh
pipeline_tag: text-to-speech
tags:
- mlx
- text-to-speech
- voice-cloning
mlx-community/chatterbox-multilingual-v3
This is Chatterbox Multilingual V3, converted to MLX format from ResembleAI/chatterbox using mlx-audio version 0.4.7.
Note: This model requires the S3Tokenizer weights from mlx-community/S3TokenizerV2, which will be downloaded automatically.
Use with mlx-audio
pip install -U mlx-audio
Command line
mlx_audio.tts.generate --model mlx-community/chatterbox-multilingual-v3 --text "Bonjour, voici Chatterbox sur MLX !" --ref_audio reference.wav --lang_code fr
Python
from mlx_audio.tts.generate import generate_audio
generate_audio(
text="Bonjour, voici Chatterbox sur MLX !",
model="mlx-community/chatterbox-multilingual-v3",
ref_audio="reference.wav",
lang_code="fr",
file_prefix="output",
)