chatterbox-streaming 0.1.2

Chatterbox Streaming: Open Source TTS and Voice Conversion

Chatterbox TTS Streaming

Chatterbox is an 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. This fork adds a streaming implementation that achieves a realtime factor of 0.499 (target < 1) on a 4090 gpu and a latency to first chunk of around 0.472s

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
• Real-time streaming generation
• [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_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.

Installation

python3.10 -m venv .venv
source .venv/bin/activate
pip install chatterbox-streaming

Build for development

git clone https://github.com/davidbrowne17/chatterbox-streaming.git
pip install -e .

Usage

Basic TTS Generation

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)

Streaming TTS Generation

For real-time applications where you want to start playing audio as soon as it's available:

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

model = ChatterboxTTS.from_pretrained(device="cuda")
text = "Welcome to the world of streaming text-to-speech! This audio will be generated and played in real-time chunks."

# Basic streaming
audio_chunks = []
for audio_chunk, metrics in model.generate_stream(text):
    audio_chunks.append(audio_chunk)
    # You can play audio_chunk immediately here for real-time playback
    print(f"Generated chunk {metrics.chunk_count}, RTF: {metrics.rtf:.3f}" if metrics.rtf else f"Chunk {metrics.chunk_count}")

# Combine all chunks into final audio
final_audio = torch.cat(audio_chunks, dim=-1)
ta.save("streaming_output.wav", final_audio, model.sr)

Streaming with Voice Cloning

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

model = ChatterboxTTS.from_pretrained(device="cuda")
text = "This streaming synthesis will use a custom voice from the reference audio file."
AUDIO_PROMPT_PATH = "reference_voice.wav"

audio_chunks = []
for audio_chunk, metrics in model.generate_stream(
    text, 
    audio_prompt_path=AUDIO_PROMPT_PATH,
    exaggeration=0.7,
    cfg_weight=0.3,
    chunk_size=25  # Smaller chunks for lower latency
):
    audio_chunks.append(audio_chunk)
    
    # Real-time metrics available
    if metrics.latency_to_first_chunk:
        print(f"First chunk latency: {metrics.latency_to_first_chunk:.3f}s")

# Save the complete streaming output
final_audio = torch.cat(audio_chunks, dim=-1)
ta.save("streaming_voice_clone.wav", final_audio, model.sr)

Streaming Parameters

• audio_prompt_path: Reference audio path for voice cloning
• chunk_size: Number of speech tokens per chunk (default: 50). Smaller values = lower latency but more overhead
• print_metrics: Enable automatic printing of latency and RTF metrics (default: True)
• exaggeration: Emotion intensity control (0.0-1.0+)
• cfg_weight: Classifier-free guidance weight (0.0-1.0)
• temperature: Sampling randomness (0.1-1.0)

See example_tts_stream.py for more examples.

Example metrics

Here are the example metrics for streaming latency on a 4090 using Linux

• Latency to first chunk: 0.472s
• Received chunk 1, shape: torch.Size([1, 24000]), duration: 1.000s
• Audio playback started!
• Received chunk 2, shape: torch.Size([1, 24000]), duration: 1.000s
• Received chunk 3, shape: torch.Size([1, 24000]), duration: 1.000s
• Received chunk 4, shape: torch.Size([1, 24000]), duration: 1.000s
• Received chunk 5, shape: torch.Size([1, 24000]), duration: 1.000s
• Received chunk 6, shape: torch.Size([1, 20160]), duration: 0.840s
• Total generation time: 2.915s
• Total audio duration: 5.840s
• RTF (Real-Time Factor): 0.499 (target < 1)
• Total chunks yielded: 6

Acknowledgements

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

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.

Disclaimer

Don't use this model to do bad things. Prompts are sourced from freely available data on the internet.

Streaming Implementation Author

David Browne

Support me

Support this project on Ko-fi: https://ko-fi.com/davidbrowne17