Model reference · open weights
chatterbox-nano is an open-weight audio or speech model from ResembleAI. chatterbox-nano (BF16) weighs 3.0 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the ResembleAI/chatterbox-nano model card, 2026-10-01
What it is
| Released by | ResembleAI |
|---|---|
| Released | 2026-04-14 |
| VRAM | 3.0 GB for the weights |
What it runs on
| Card | One stream | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
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.
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 |
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.
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)
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)
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(chQuoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works