Chatterbox Voice Cloning

Run Chatterbox TTS by Resemble AI for zero-shot voice cloning and multilingual speech synthesis on Clore.ai GPUs.

Chatterbox is a family of state-of-the-art open-source text-to-speech models by Resemble AI. It performs zero-shot voice cloning from a short reference clip (~10 seconds), supports paralinguistic tags like [laugh] and [cough], and offers a multilingual variant covering 23+ languages. Three model variants are available: Turbo (350M, low-latency), Original (500M, creative controls), and Multilingual (500M, 23+ languages).

GitHub: resemble-ai/chatterbox PyPI: chatterbox-tts License: MIT

Key Features

• Zero-shot voice cloning — clone any voice from ~10 seconds of reference audio

• Paralinguistic tags (Turbo) — [laugh], [cough], [chuckle], [sigh] for realistic speech

• 23+ languages (Multilingual) — Arabic, Chinese, French, German, Japanese, Korean, Russian, Spanish, and more

• CFG & Exaggeration tuning (Original) — creative control over expressiveness

• Three model sizes — Turbo (350M), Original (500M), Multilingual (500M)

• MIT license — fully open for commercial use

Requirements

Component	Minimum	Recommended
GPU	RTX 3060 12 GB	RTX 3090 / RTX 4090
VRAM	6 GB	10 GB+
RAM	8 GB	16 GB
Disk	5 GB	15 GB
Python	3.10+	3.11
CUDA	11.8+	12.1+

Clore.ai recommendation: RTX 3090 ($0.30–1.00/day) for comfortable VRAM headroom. RTX 3060 works for Turbo model. For the Multilingual model with long texts, consider an RTX 4090 ($0.50–2.00/day).

Installation

# Install from PyPI
pip install chatterbox-tts

# Or install from source
git clone https://github.com/resemble-ai/chatterbox.git
cd chatterbox
pip install -e .

# Verify
python -c "from chatterbox.tts import ChatterboxTTS; print('Chatterbox ready')"

Quick Start

Turbo Model (Lowest Latency)

import torchaudio as ta
from chatterbox.tts_turbo import ChatterboxTurboTTS

model = ChatterboxTurboTTS.from_pretrained(device="cuda")

# Basic TTS with paralinguistic tags
text = "Hey, welcome back! [chuckle] I've got some great news for you today."

# Voice cloning — provide a 10+ second reference clip
wav = model.generate(text, audio_prompt_path="reference_voice.wav")

ta.save("output_turbo.wav", wav, model.sr)
print(f"Saved at {model.sr} Hz")

Original Model (English, Creative Controls)

import torchaudio as ta
from chatterbox.tts import ChatterboxTTS

model = ChatterboxTTS.from_pretrained(device="cuda")

text = "The quick brown fox jumps over the lazy dog. It was a beautiful morning."

# Generate without voice cloning (uses default voice)
wav = model.generate(text)
ta.save("output_default.wav", wav, model.sr)

# Generate with voice cloning
wav = model.generate(text, audio_prompt_path="my_voice_sample.wav")
ta.save("output_cloned.wav", wav, model.sr)

Usage Examples

Multilingual Voice Cloning

import torchaudio as ta
from chatterbox.mtl_tts import ChatterboxMultilingualTTS

model = ChatterboxMultilingualTTS.from_pretrained(device="cuda")

# French
french_text = "Bonjour, comment allez-vous? Bienvenue dans notre démonstration."
wav_fr = model.generate(french_text, language_id="fr")
ta.save("output_french.wav", wav_fr, model.sr)

# Japanese
japanese_text = "こんにちは、テキスト読み上げのデモンストレーションです。"
wav_ja = model.generate(japanese_text, language_id="ja")
ta.save("output_japanese.wav", wav_ja, model.sr)

# Russian with voice cloning
russian_text = "Привет! Это демонстрация синтеза речи на русском языке."
wav_ru = model.generate(
    russian_text,
    language_id="ru",
    audio_prompt_path="russian_speaker.wav"
)
ta.save("output_russian.wav", wav_ru, model.sr)

print("Multilingual generation complete")

Paralinguistic Tags (Turbo)

import torchaudio as ta
from chatterbox.tts_turbo import ChatterboxTurboTTS

model = ChatterboxTurboTTS.from_pretrained(device="cuda")

samples = [
    ("greeting", "Hi there! [laugh] It's so good to see you again."),
    ("nervous", "Um, well [cough] I'm not really sure about that."),
    ("excited", "Oh my gosh! [chuckle] That's absolutely incredible news!"),
]

for name, text in samples:
    wav = model.generate(text, audio_prompt_path="speaker_ref.wav")
    ta.save(f"para_{name}.wav", wav, model.sr)
    print(f"Generated: {name}")

Batch Processing Script

import torchaudio as ta
from chatterbox.tts import ChatterboxTTS
import os

model = ChatterboxTTS.from_pretrained(device="cuda")

# Process a list of lines (e.g., for audiobook chapters)
lines = [
    "Chapter one. The adventure begins.",
    "It was a dark and stormy night.",
    "The hero stood at the crossroads, uncertain of the path ahead.",
]

os.makedirs("output_batch", exist_ok=True)

for i, line in enumerate(lines):
    wav = model.generate(line, audio_prompt_path="narrator_voice.wav")
    ta.save(f"output_batch/line_{i:03d}.wav", wav, model.sr)
    print(f"[{i+1}/{len(lines)}] {line[:40]}...")

print("Batch processing complete")

Tips for Clore.ai Users

• Model choice — use Turbo for low-latency voice agents, Original for English creative work, Multilingual for non-English content

• Reference audio quality — use a clean, noise-free 10–30 second clip for best voice cloning results

• Docker setup — base image pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime, expose port 7860/http for Gradio

• Memory management — call torch.cuda.empty_cache() between large batches to free VRAM

• Supported languages — ar, da, de, el, en, es, fi, fr, he, hi, it, ja, ko, ms, nl, no, pl, pt, ru, sv, sw, tr, zh

• HuggingFace Space — try before renting at huggingface.co/spaces/ResembleAI/Chatterbox

Troubleshooting

Problem	Solution
CUDA out of memory	Use Turbo (350M) instead of Original/Multilingual (500M), or rent a larger GPU
Cloned voice doesn't match	Use a longer (15–30s), cleaner reference clip with minimal background noise
numpy version conflict	Run pip install numpy==1.26.4 --force-reinstall
Slow model download	Models are fetched from HuggingFace on first run (~2 GB); pre-download with huggingface-cli
Audio has artifacts	Reduce text length per generation; very long texts can degrade quality
ModuleNotFoundError	Ensure pip install chatterbox-tts completed without errors; check Python 3.11 compatibility