
resemble-ai/chatterbox
SoTA open-source TTS
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About chatterbox
Chatterbox TTS
We're excited to introduce Chatterbox, Resemble AI's first production-grade open source TTS model. 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 (link). It delivers reliable performance with ultra-low latency of sub 200ms—ideal for production use in agents, applications, or interactive media.
Key Details
- SoTA zeroshot 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
- Outperforms ElevenLabs
Tips
-
General Use (TTS and Voice Agents):
- The default settings (
exaggeration=0.5,cfg_weight=0.5) work well for most prompts. - If the reference speaker has a fast speaking style, lowering
cfg_weightto around0.3can improve pacing.
- The default settings (
-
Expressive or Dramatic Speech:
- Try lower
cfg_weightvalues (e.g.~0.3) and increaseexaggerationto around0.7or higher. - Higher
exaggerationtends to speed up speech; reducingcfg_weighthelps compensate with slower, more deliberate pacing.
- Try lower
Installation
pip install chatterbox-tts
Alternatively, you can install from source:
# conda create -yn chatterbox python=3.11
# conda activate chatterbox
git clone https://github.com/resemble-ai/chatterbox.git
cd chatterbox
pip install -e .
We developed and tested Chatterbox on Python 3.11 on Debain 11 OS; the versions of the dependencies are pinned in pyproject.toml to ensure consistency. You can modify the code or dependencies in this installation mode.
Usage
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)
See example_tts.py and example_vc.py for more examples.
Supported Lanugage
Currenlty only English.
Acknowledgements
Built-in PerTh Watermarking for Responsible AI
Every audio file generated by Chatterbox includes Resemble AI's Perth (Perceptual Threshold) Watermarker - imperceptible neural watermarks that survive MP3 compression, audio editing, and common manipulations while maintaining nearly 100% detection accuracy.
Watermark extraction
You can look for the watermark using the following script.
import perth
import librosa
AUDIO_PATH = "YOUR_FILE.wav"
# Load the watermarked audio
watermarked_audio, sr = librosa.load(AUDIO_PATH, sr=None)
# Initialize watermarker (same as used for embedding)
watermarker = perth.PerthImplicitWatermarker()
# Extract watermark
watermark = watermarker.get_watermark(watermarked_audio, sample_rate=sr)
print(f"Extracted watermark: {watermark}")
# Output: 0.0 (no watermark) or 1.0 (watermarked)
Official Discord
👋 Join us on Discord and let's build something awesome together!
Citation
If you find this model useful, please consider citing.
@misc{chatterboxtts2025,
author = {{Resemble AI}},
title = {{Chatterbox-TTS}},
year = {2025},
howpublished = {\url{https://github.com/resemble-ai/chatterbox}},
note = {GitHub repository}
}
Disclaimer
Don't use this model to do bad things. Prompts are sourced from freely available data on the internet.
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