chatterbox
Overview
Highlights
- Permissive MIT license for unrestricted commercial use
- Low-latency synthesis optimized for real-time applications
- Simplified API for rapid integration into existing stacks
- Lightweight architecture reducing infrastructure overhead
Usage
# Install Hugging Face transformers
pip install transformers torch
# Load model with transformers
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("ResembleAI/chatterbox")
tokenizer = AutoTokenizer.from_pretrained("ResembleAI/chatterbox")
Hugging Face Download
We recommend downloading the model via the Hugging Face CLI or Hub SDK.
Guidance:Before downloading, install huggingface_hub with:
pip install -U huggingface_hub
CLI Download
Download the full repository
huggingface-cli download ResembleAI/chatterbox
Download a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download ResembleAI/chatterbox config.json --local-dir ./dir
See the official docs for more CLI options
SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('ResembleAI/chatterbox')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://huggingface.co/ResembleAI/chatterbox
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ResembleAI/chatterbox
Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.
PyTorch / Transformers Usage
Install Transformers
pip install -U transformers torch
Load the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('ResembleAI/chatterbox')
tokenizer = AutoTokenizer.from_pretrained('ResembleAI/chatterbox')
Model Download
We recommend downloading the model via the ModelScope CLI or SDK.
Guidance:Before downloading, install ModelScope with:
pip install modelscope
CLI Download
Download the full repository
modelscope download --model ResembleAI/chatterbox
Download a single file to a local folder (e.g. README.md into ./dir)
modelscope download --model ResembleAI/chatterbox README.md --local_dir ./dir
See the docs for more CLI options
SDK Download
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('ResembleAI/chatterbox')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://www.modelscope.cn/ResembleAI/chatterbox.git
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/ResembleAI/chatterbox.git
ModelScope 模型页直接下载模型文件;无需将模型文件放在本站服务器。
Notebook Quickstart
Install the ModelScope library
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
Load the model and run inference
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
p = pipeline('text-generation', 'ResembleAI/chatterbox')
Full Documentation
---
license: mit
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:
- text-to-speech
- speech
- speech-generation
- voice-cloning
- multilingual-tts
library_name: chatterbox
---
<img width="800" alt="cb-big2" src="https://github.com/user-attachments/assets/bd8c5f03-e91d-4ee5-b680-57355da204d1" />
<h1 style="font-size: 32px">Chatterbox TTS</h1>
<div style="display: flex; align-items: center; gap: 12px">
<a href="https://resemble-ai.github.io/chatterbox_demopage/">
<img src="https://img.shields.io/badge/listen-demo_samples-blue" alt="Listen to Demo Samples" />
</a>
<a href="https://huggingface.co/spaces/ResembleAI/Chatterbox">
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg" alt="Open in HF Spaces" />
</a>
<a href="https://podonos.com/resembleai/chatterbox">
<img src="https://static-public.podonos.com/badges/insight-on-pdns-sm-dark.svg" alt="Insight on Podos" />
</a>
</div>
<div style="display: flex; align-items: center; gap: 8px;">
<span style="font-style: italic;white-space: pre-wrap">Made with ❤️ by</span>
<img width="100" alt="resemble-logo-horizontal" src="https://github.com/user-attachments/assets/35cf756b-3506-4943-9c72-c05ddfa4e525" />
</div>
Latest Release: Chatterbox Multilingual V3
Chatterbox Multilingual V3 is the latest general-purpose multilingual TTS model in the Chatterbox family. It keeps the same 0.5B model size while improving speaker similarity, reducing hallucinations, and producing more natural, conversational speech across languages.
V3 is designed for broad language coverage like V2, but with stronger stability and more expressive generation. It is the recommended multilingual model for users who want one voice cloning model that works across many languages. Try it in the Chatterbox Multilingual TTS V3 Space.
Alongside V3, we are releasing the Single Language Pack: dedicated finetunes for priority languages where tighter quality control, stronger language-specific behavior, and more specialized speech generation are valuable.
- Broad Multilingual Coverage: Designed as the main general-purpose multilingual Chatterbox model, supporting wide language coverage similar to V2.
- Single Language Pack: Dedicated single-language models provide stronger specialization and quality control where language- and regional-dialect-specific performance matters most.
- More Consistent Speaker Similarity: Improves voice identity and accent preservation across languages, making cross-language voice cloning more stable and reliable.
- Reduced Hallucination: V3 is optimized to reduce unwanted continuation, repetition, and off-prompt speech, especially in cases where earlier multilingual models were less stable.
Model Zoo
Choose the right Chatterbox model for your application.
| Model | Size | Languages | Key Features | Best For | Demo |
| --- | --- | --- | --- | --- | --- |
| Chatterbox Multilingual V3 | 500M | 23+ | Improved speaker similarity, reduced hallucinations, more natural multilingual speech | Global applications, localization, cross-language voice cloning | Demo |
| Single Language Pack | 500M each | 6 dedicated finetunes | Language- and region-specific quality control | Priority languages and dialect-sensitive applications | Demos |
| Chatterbox | 500M | English | CFG and exaggeration tuning | General zero-shot TTS with creative controls | Demo |
09/04 🔥 Introducing Chatterbox Multilingual in 23 Languages!
We're excited to introduce Chatterbox and Chatterbox Multilingual, Resemble AI's production-grade open source TTS models. Chatterbox Multilingual supports Arabic, Danish, German, Greek, English, Spanish, Finnish, French, Hebrew, Hindi, Italian, Japanese, Korean, Malay, Dutch, Norwegian, Polish, Portuguese, Russian, Swedish, Swahili, Turkish, Chinese out of the box. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations.
Whether you're working on memes, videos, games, or AI agents, Chatterbox brings your content to life. It's also the first open source TTS model to support emotion exaggeration control, a powerful feature that makes your voices stand out. Try it now on our Hugging Face Gradio app.
If you like the model but need to scale or tune it for higher accuracy, check out our competitively priced TTS service (<a href="https://resemble.ai">link</a>). It delivers reliable performance with ultra-low latency of sub 200ms—ideal for production use in agents, applications, or interactive media.
Key Details
- Chatterbox Multilingual V3, a 0.5B general-purpose multilingual TTS model supporting 23 languages
- Dedicated single-language finetunes for Chinese, LatAm Spanish, Brazilian Portuguese, Spain Spanish, Portugal Portuguese, and Hindi
- SoTA zeroshot English TTS
- 0.5B Llama backbone
- Unique exaggeration/intensity control
- Ultra-stable with alignment-informed inference
- Trained on 0.5M hours of cleaned data
- Watermarked outputs
- Easy voice conversion script
Tips
- General Use (TTS and Voice Agents):
exaggeration=0.5, cfg=0.5) work well for most prompts.
- If the reference speaker has a fast speaking style, lowering cfg to around 0.3 can improve pacing.
- Expressive or Dramatic Speech:
cfg values (e.g. ~0.3) and increase exaggeration to around 0.7 or higher.
- Higher exaggeration tends to speed up speech; reducing cfg helps compensate with slower, more deliberate pacing.
*Note:* Ensure that the reference clip matches the specified language tag. Otherwise, language transfer outputs may inherit the accent of the reference clip’s language.
*To mitigate this, set the CFG weight to 0.*
Installation
pip install chatterbox-ttsUsage
import torchaudio as ta
from chatterbox.tts import ChatterboxTTS
model = ChatterboxTTS.from_pretrained(device="cuda")
text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill."
wav = model.generate(text)
ta.save("test-1.wav", wav, model.sr)
If you want to synthesize with a different voice, specify the audio prompt
AUDIO_PROMPT_PATH="YOUR_FILE.wav"
wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH)
ta.save("test-2.wav", wav, model.sr)Multilingual Quickstart
import torchaudio as ta
from chatterbox.mtl_tts import ChatterboxMultilingualTTS
multilingual_model = ChatterboxMultilingualTTS.from_pretrained(
device="cuda",
t3_model="v3",
)
To use the legacy V2 multilingual checkpoint, omit t3_model or pass t3_model="v2".
french_text = "Bonjour, comment ça va? Ceci est le modèle de synthèse vocale multilingue Chatterbox, il prend en charge 23 langues."
wav_french = multilingual_model.generate(french_text, language_id="fr")
ta.save("test-french.wav", wav_french, multilingual_model.sr)
chinese_text = "你好,今天天气真不错,希望你有一个愉快的周末。"
wav_chinese = multilingual_model.generate(chinese_text, language_id="zh")
ta.save("test-chinese.wav", wav_chinese, multilingual_model.sr)
See
example_tts.py for more examples.
Single Language Pack
The Single Language Pack provides dedicated finetunes for priority languages and regional variants. Use these when you want stronger language-specific behavior, tighter quality control, or dialect-aware generation beyond t