ResembleAI_chatterbox-nano

---

license: mit language:

• en pipeline_tag: text-to-speech tags:
• text-to-speech
• speech
• speech-generation
• voice-cloning

---

Chatterbox TTS

Made with ❤️ by

Chatterbox is a family of four state-of-the-art, open-source text-to-speech models by Resemble AI.

We are excited to introduce Chatterbox-Nano, our most efficient model yet. Built on a streamlined 110M parameter architecture, Nano delivers high-quality speech on CPU - 3x faster than realtime on 8 cores. We have also distilled the speech-token-to-mel decoder, previously a bottleneck, reducing generation from 10 steps to just one, while retaining high-fidelity audio output.

Paralinguistic tags are now native to both Nano and Turbo models, allowing you to use [cough], [laugh], [chuckle], and more to add distinct realism. While Turbo was built primarily for low-latency voice agents, it excels at narration and creative workflows.

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.

⚡ Model Zoo

Choose the right model for your application.

Model	Size	Languages	Key Features	Best For	🤗	Examples
Chatterbox-Nano	110M	English	Paralinguistic Tags ([laugh]), 3x faster than realtime on 8-core CPU	On-device / CPU inference, tight latency & memory budgets	Demo	Listen
Chatterbox-Turbo	350M	English	Paralinguistic Tags ([laugh]), Lower Compute and VRAM	Zero-shot voice agents, Production	Demo	Listen
Chatterbox-Multilingual (Language list)	500M	23+	Zero-shot cloning, Multiple Languages	Global applications, Localization	Demo	Listen
Chatterbox (Tips and Tricks)	500M	English	CFG & Exaggeration tuning	General zero-shot TTS with creative controls	Demo	Listen

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 Debian 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

Chatterbox-Nano

Nano shares Turbo's architecture and is loaded through the same ChatterboxTurboTTS class by passing nano=True:

import torchaudio as ta
import torch
from chatterbox.tts_turbo import ChatterboxTurboTTS

# Load the Nano model (also runs on CPU: device="cpu")
model = ChatterboxTurboTTS.from_pretrained(device="cuda", nano=True)

# Generate with Paralinguistic Tags
text = "Hi there, Sarah here from MochaFone calling you back [chuckle], have you got one minute to chat about the billing issue?"

# Generate audio (requires a reference clip for voice cloning)
wav = model.generate(text, audio_prompt_path="your_10s_ref_clip.wav")

ta.save("test-nano.wav", wav, model.sr)

Chatterbox-Turbo

import torchaudio as ta
import torch
from chatterbox.tts_turbo import ChatterboxTurboTTS

# Load the Turbo model
model = ChatterboxTurboTTS.from_pretrained(device="cuda")

# Generate with Paralinguistic Tags
text = "Hi there, Sarah here from MochaFone calling you back [chuckle], have you got one minute to chat about the billing issue?"

# Generate audio (requires a reference clip for voice cloning)
wav = model.generate(text, audio_prompt_path="your_10s_ref_clip.wav")

ta.save("test-turbo.wav", wav, model.sr)

Chatterbox and Chatterbox-Multilingual

import torchaudio as ta
from chatterbox.tts import ChatterboxTTS
from chatterbox.mtl_tts import ChatterboxMultilingualTTS

device = "cuda"  # or "cpu" / "mps"

# English example
model = ChatterboxTTS.from_pretrained(device=device)

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-english.wav", wav, model.sr)

# Multilingual examples
multilingual_model = ChatterboxMultilingualTTS.from_pretrained(device=device)

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)

# 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, example_tts_turbo.py, example_tts_nano.py, and example_vc.py for more examples.

Supported Languages

Arabic (ar) • Danish (da) • German (de) • Greek (el) • English (en) • Spanish (es) • Finnish (fi) • French (fr) • Hebrew (he) • Hindi (hi) • Italian (it) • Japanese (ja) • Korean (ko) • Malay (ms) • Dutch (nl) • Norwegian (no) • Polish (pl) • Portuguese (pt) • Russian (ru) • Swedish (sv) • Swahili (sw) • Turkish (tr) • Chinese (zh)

Original Chatterbox Tips

• General Use (TTS and Voice Agents):

  • 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 cfg_weight to 0.
  • The default settings (exaggeration=0.5, cfg_weight=0.5) work well for most prompts across all languages.
  • If the reference speaker has a fast speaking style, lowering cfg_weight to around 0.3 can improve pacing.

• Expressive or Dramatic Speech:

  • Try lower cfg_weight values (e.g. ~0.3) and increase exaggeration to around 0.7 or higher.
  • Higher exaggeration tends to speed up speech; reducing cfg_weight helps compensate with slower, more deliberate pacing.

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!

Acknowledgements

• Cosyvoice
• Real-Time-Voice-Cloning
• HiFT-GAN
• Llama 3
• S3Tokenizer

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.