Chatterbox TTS API

FastAPI-powered REST API for Chatterbox TTS, providing OpenAI-compatible text-to-speech endpoints with voice cloning capabilities and additional features on top of the chatterbox-tts base package.

Features

🚀 OpenAI-Compatible API - Drop-in replacement for OpenAI's TTS API
⚡ FastAPI Performance - High-performance async API with automatic documentation
🎨 React Frontend - Includes an optional, ready-to-use web interface
🎭 Voice Cloning - Use your own voice samples for personalized speech
🎤 Voice Library Management - Upload, manage, and use custom voices by name
📝 Smart Text Processing - Automatic chunking for long texts
📊 Real-time Status - Monitor TTS progress, statistics, and request history
🐳 Docker Ready - Full containerization with persistent voice storage
⚙️ Configurable - Extensive environment variable configuration
🎛️ Parameter Control - Real-time adjustment of speech characteristics
📚 Auto Documentation - Interactive API docs at /docs and /redoc
🔧 Type Safety - Full Pydantic validation for requests and responses
🧠 Memory Management - Advanced memory monitoring and automatic cleanup

⚡️ Quick Start

git clone https://github.com/travisvn/chatterbox-tts-api
cd chatterbox-tts-api
uv sync
uv run main.py

Tip

uv installed with curl -LsSf https://astral.sh/uv/install.sh | sh

Local Installation with Python 🐍

Option A: Using uv (Recommended - Faster & Better Dependencies)

# Clone the repository
git clone https://github.com/travisvn/chatterbox-tts-api
cd chatterbox-tts-api

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies with uv (automatically creates venv)
uv sync

# Copy and customize environment variables
cp .env.example .env

# Start the API with FastAPI
uv run uvicorn app.main:app --host 0.0.0.0 --port 4123
# Or use the main script
uv run main.py

💡 Why uv? Users report better compatibility with chatterbox-tts, 25-40% faster installs, and superior dependency resolution. See migration guide →

Option B: Using pip (Traditional)

# Clone the repository
git clone https://github.com/travisvn/chatterbox-tts-api
cd chatterbox-tts-api

# Setup environment — using Python 3.11
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Copy and customize environment variables
cp .env.example .env

# Add your voice sample (or use the provided one)
# cp your-voice.mp3 voice-sample.mp3

# Start the API with FastAPI
uvicorn app.main:app --host 0.0.0.0 --port 4123
# Or use the main script
python main.py

Ran into issues? Check the troubleshooting section

🐳 Docker (Recommended)

# Clone and start with Docker Compose
git clone https://github.com/travisvn/chatterbox-tts-api
cd chatterbox-tts-api

# Use Docker-optimized environment variables
cp .env.example.docker .env  # Docker-specific paths, ready to use
# Or: cp .env.example .env    # Local development paths, needs customization

# Choose your deployment method:

# API Only (default)
docker compose -f docker/docker-compose.yml up -d             # Standard (pip-based)
docker compose -f docker/docker-compose.uv.yml up -d          # uv-optimized (faster builds)
docker compose -f docker/docker-compose.gpu.yml up -d         # Standard + GPU
docker compose -f docker/docker-compose.uv.gpu.yml up -d      # uv + GPU (recommended for GPU users)
docker compose -f docker/docker-compose.cpu.yml up -d         # CPU-only

# API + Frontend (add --profile frontend to any of the above)
docker compose -f docker/docker-compose.yml --profile frontend up -d             # Standard + Frontend
docker compose -f docker/docker-compose.gpu.yml --profile frontend up -d         # GPU + Frontend
docker compose -f docker/docker-compose.uv.gpu.yml --profile frontend up -d      # uv + GPU + Frontend

# Watch the logs as it initializes (the first use of TTS takes the longest)
docker logs chatterbox-tts-api -f

# Test the API
curl -X POST http://localhost:4123/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"input": "Hello from Chatterbox TTS!"}' \
  --output test.wav

🚀 Running with the Web UI (Full Stack)

This project includes an optional React-based web UI. Use Docker Compose profiles to easily opt in or out of the frontend:

With Docker Compose Profiles

# API only (default behavior)
docker compose -f docker/docker-compose.yml up -d

# API + Frontend + Web UI (with --profile frontend)
docker compose -f docker/docker-compose.yml --profile frontend up -d

# Or use the convenient helper script for fullstack:
python start.py fullstack

# Same pattern works with all deployment variants:
docker compose -f docker/docker-compose.gpu.yml --profile frontend up -d    # GPU + Frontend
docker compose -f docker/docker-compose.uv.yml --profile frontend up -d     # uv + Frontend
docker compose -f docker/docker-compose.cpu.yml --profile frontend up -d    # CPU + Frontend

Local Development

For local development, you can run the API and frontend separately:

# Start the API first (follow earlier instructions)
# Then run the frontend:
cd frontend && npm install && npm run dev

Click the link provided from Vite to access the web UI.

Build for Production

Build the frontend for production deployment:

cd frontend && npm install && npm run build

You can then access it directly from your local file system at /dist/index.html.

Port Configuration

API Only: Accessible at http://localhost:4123 (direct API access)
With Frontend: Web UI at http://localhost:4321, API requests routed via proxy

The frontend uses a reverse proxy to route requests, so when running with --profile frontend, the web interface will be available at http://localhost:4321 while the API runs behind the proxy.

Screenshots of Frontend (Web UI)

Chatterbox TTS API - Frontend - Dark Mode

Chatterbox TTS API - Frontend - Light Mode

Chatterbox TTS API - Frontend Processing - Dark Mode

Chatterbox TTS API - Frontend Processing - Light Mode

🖼️ View screenshot of full frontend web UI — light mode / dark mode

API Usage

Basic Text-to-Speech (Default Voice)

This endpoint works for both the API-only and full-stack setups.

curl -X POST http://localhost:4123/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"input": "Your text here"}' \
  --output speech.wav

Using Custom Parameters (JSON)

curl -X POST http://localhost:4123/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"input": "Dramatic speech!", "exaggeration": 1.2, "cfg_weight": 0.3, "temperature": 0.9}' \
  --output dramatic.wav

Custom Voice Upload

Upload your own voice sample for personalized speech:

curl -X POST http://localhost:4123/v1/audio/speech/upload \
  -F "input=Hello with my custom voice!" \
  -F "exaggeration=0.8" \
  -F "voice_file=@my_voice.mp3" \
  --output custom_voice_speech.wav

With Custom Parameters and Voice Upload

curl -X POST http://localhost:4123/v1/audio/speech/upload \
  -F "input=Dramatic speech!" \
  -F "exaggeration=1.2" \
  -F "cfg_weight=0.3" \
  -F "temperature=0.9" \
  -F "voice_file=@dramatic_voice.wav" \
  --output dramatic.wav

Voice Library Management

Store and manage custom voices by name for reuse across requests:

# Upload a voice to the library
curl -X POST http://localhost:4123/v1/voices \
  -F "voice_file=@my_voice.wav" \
  -F "name=my-custom-voice"

# Use the voice by name in speech generation
curl -X POST http://localhost:4123/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"input": "Hello with my custom voice!", "voice": "my-custom-voice"}' \
  --output custom_voice_output.wav

# List all available voices
curl http://localhost:4123/v1/voices

🔧 Complete Voice Library Documentation →

🎵 Real-time Audio Streaming

The API supports multiple streaming formats for lower latency and better user experience:

Raw Audio Streaming: Traditional audio chunks (WAV format)
Server-Side Events (SSE): OpenAI-compatible format with base64-encoded audio chunks

Quick Start

# Basic audio streaming
curl -X POST http://localhost:4123/v1/audio/speech/stream \
  -H "Content-Type: application/json" \
  -d '{"input": "This streams in real-time!"}' \
  --output streaming.wav

# SSE streaming (OpenAI compatible)
curl -X POST http://localhost:4123/v1/audio/speech \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{"input": "This streams as Server-Side Events!", "stream_format": "sse"}' \
  --no-buffer

# Real-time playback
curl -X POST http://localhost:4123/v1/audio/speech/stream \
  -H "Content-Type: application/json" \
  -d '{"input": "Play as it generates!"}' \
  | ffplay -f wav -i pipe:0 -autoexit -nodisp

🚀 Complete Streaming Documentation →

For comprehensive streaming features including:

Advanced chunking strategies (sentence, paragraph, word, fixed)
Quality presets (fast, balanced, high)
Configurable parameters and performance tuning
Real-time progress monitoring
Python, JavaScript, and cURL examples
Integration patterns for different use cases

Key Benefits:

⚡ Lower latency - Start hearing audio in 1-2 seconds
🎯 Better UX - No waiting for complete generation
💾 Memory efficient - Process chunks individually
🎛️ Configurable - Choose speed vs quality trade-offs

🐍 Python Examples

Default Voice (JSON)

import requests

response = requests.post(
    "http://localhost:4123/v1/audio/speech",
    json={
        "input": "Hello world!",
        "exaggeration": 0.8
    }
)

with open("output.wav", "wb") as f:
    f.write(response.content)

Upload Endpoint (Default Voice)

import requests

response = requests.post(
    "http://localhost:4123/v1/audio/speech/upload",
    data={
        "input": "Hello world!",
        "exaggeration": 0.8
    }
)

with open("output.wav", "wb") as f:
    f.write(response.content)

Custom Voice Upload

import requests

with open("my_voice.mp3", "rb") as voice_file:
    response = requests.post(
        "http://localhost:4123/v1/audio/speech/upload",
        data={
            "input": "Hello with my custom voice!",
            "exaggeration": 0.8,
            "temperature": 1.0
        },
        files={
            "voice_file": ("my_voice.mp3", voice_file, "audio/mpeg")
        }
    )

with open("custom_output.wav", "wb") as f:
    f.write(response.content)

Basic Streaming Example

import requests

# Stream audio generation in real-time
response = requests.post(
    "http://localhost:4123/v1/audio/speech/stream",
    json={
        "input": "This will stream as it's generated!",
        "exaggeration": 0.8
    },
    stream=True  # Enable streaming mode
)

with open("streaming_output.wav", "wb") as f:
    for chunk in response.iter_content(chunk_size=8192):
        if chunk:
            f.write(chunk)
            print(f"Received chunk: {len(chunk)} bytes")

SSE Streaming Example (OpenAI Compatible)

import requests
import json
import base64

# Stream audio using Server-Side Events format
response = requests.post(
    "http://localhost:4123/v1/audio/speech",
    json={
        "input": "This streams as Server-Side Events!",
        "stream_format": "sse",
        "exaggeration": 0.8
    },
    stream=True,
    headers={'Accept': 'text/event-stream'}
)

audio_chunks = []

for line in response.iter_lines(decode_unicode=True):
    if line.startswith('data: '):
        event_data = line[6:]  # Remove 'data: ' prefix

        try:
            event = json.loads(event_data)

            if event.get('type') == 'speech.audio.delta':
                # Decode base64 audio chunk
                audio_data = base64.b64decode(event['audio'])
                audio_chunks.append(audio_data)
                print(f"Received audio chunk: {len(audio_data)} bytes")

            elif event.get('type') == 'speech.audio.done':
                usage = event.get('usage', {})
                print(f"Complete! Tokens: {usage.get('total_tokens', 0)}")
                break
        except:
            continue

print(f"Received {len(audio_chunks)} audio chunks")

📚 Complete Streaming Examples & Documentation →

Including real-time playback, progress monitoring, custom voice uploads, and advanced integration patterns.

Voice File Requirements

Supported Formats:

MP3 (.mp3)
WAV (.wav)
FLAC (.flac)
M4A (.m4a)
OGG (.ogg)

Requirements:

Maximum file size: 10MB
Recommended duration: 10-30 seconds of clear speech
Avoid background noise for best results
Higher quality audio produces better voice cloning

🎛️ Configuration

The project provides two environment example files:

.env.example - For local development (uses ./models, ./voice-sample.mp3)
.env.example.docker - For Docker deployment (uses /cache, /app/voice-sample.mp3)

Choose the appropriate one for your setup:

# For local development
cp .env.example .env

# For Docker deployment
cp .env.example.docker .env

Key environment variables (see the example files for full list):

Variable	Default	Description
`PORT`	`4123`	API server port
`EXAGGERATION`	`0.5`	Emotion intensity (0.25-2.0)
`CFG_WEIGHT`	`0.5`	Pace control (0.0-1.0)
`TEMPERATURE`	`0.8`	Sampling randomness (0.05-5.0)
`VOICE_SAMPLE_PATH`	`./voice-sample.mp3`	Voice sample for cloning
`DEVICE`	`auto`	Device (auto/cuda/mps/cpu)

🎭 Voice Cloning

Replace the default voice sample:

# Replace the default voice sample
cp your-voice.mp3 voice-sample.mp3

# Or set a custom path
echo "VOICE_SAMPLE_PATH=/path/to/your/voice.mp3" >> .env

For best results:

Use 10-30 seconds of clear speech
Avoid background noise
Prefer WAV or high-quality MP3

🐳 Docker Deployment

Development

docker compose -f docker/docker-compose.yml up

Production

# Create production environment
cp .env.example.docker .env
nano .env  # Set production values

# Deploy
docker compose -f docker/docker-compose.yml up -d

With GPU Support

# Use GPU-enabled compose file
# Ensure NVIDIA Container Toolkit is installed
docker compose -f docker/docker-compose.gpu.yml up -d

📚 API Reference

API Endpoints

Endpoint	Method	Description
`/audio/speech`	POST	Generate speech from text (complete)
`/audio/speech/upload`	POST	Generate speech with voice upload
`/audio/speech/stream`	POST	Stream speech generation (docs)
`/audio/speech/stream/upload`	POST	Stream speech with voice upload (docs)
`/health`	GET	Health check and status
`/config`	GET	Current configuration
`/v1/models`	GET	Available models (OpenAI compat)
`/status`	GET	TTS processing status & progress
`/status/progress`	GET	Real-time progress (lightweight)
`/status/statistics`	GET	Processing statistics
`/status/history`	GET	Recent request history
`/info`	GET	Complete API information
`/docs`	GET	Interactive API documentation
`/redoc`	GET	Alternative API documentation

Parameters Reference

Speech Generation Parameters

Exaggeration (0.25-2.0)

0.3-0.4: Professional, neutral
0.5: Default balanced
0.7-0.8: More expressive
1.0+: Very dramatic

CFG Weight (0.0-1.0)

0.2-0.3: Faster speech
0.5: Default pace
0.7-0.8: Slower, deliberate

Temperature (0.05-5.0)

0.4-0.6: More consistent
0.8: Default balance
1.0+: More creative/random

Stream Format

audio: Raw audio streaming (default)
sse: Server-Side Events with base64-encoded audio chunks (OpenAI compatible)

🧠 Memory Management

The API includes advanced memory management to prevent memory leaks and optimize performance:

Memory Management Features

Automatic Cleanup: Periodic garbage collection and tensor cleanup
CUDA Memory Management: Automatic GPU cache clearing
Memory Monitoring: Real-time memory usage tracking
Manual Controls: API endpoints for manual cleanup operations

Memory Configuration

Variable	Default	Description
`MEMORY_CLEANUP_INTERVAL`	`5`	Cleanup memory every N requests
`CUDA_CACHE_CLEAR_INTERVAL`	`3`	Clear CUDA cache every N requests
`ENABLE_MEMORY_MONITORING`	`true`	Enable detailed memory logging

Memory Monitoring Endpoints

# Get memory status
curl http://localhost:4123/memory

# Trigger manual cleanup
curl "http://localhost:4123/memory?cleanup=true&force_cuda_clear=true"

# Reset memory tracking (with confirmation)
curl -X POST "http://localhost:4123/memory/reset?confirm=true"

Real-time Status Tracking

Monitor TTS processing in real-time:

# Check current processing status
curl "http://localhost:4123/v1/status/progress"

# Get detailed status with memory and stats
curl "http://localhost:4123/v1/status?include_memory=true&include_stats=true"

# View processing statistics
curl "http://localhost:4123/v1/status/statistics"

# Check request history
curl "http://localhost:4123/v1/status/history?limit=5"

# Get comprehensive API information
curl "http://localhost:4123/info"

Status Response Example:

{
  "is_processing": true,
  "status": "generating_audio",
  "current_step": "Generating audio for chunk 2/4",
  "current_chunk": 2,
  "total_chunks": 4,
  "progress_percentage": 50.0,
  "duration_seconds": 2.5,
  "text_preview": "Your text being processed..."
}

See Status API Documentation for complete details.

Memory Testing

Run the memory management test suite:

# Test memory patterns and cleanup
python tests/test_memory.py  # or: uv run tests/test_memory.py

# Monitor memory during testing
watch -n 1 'curl -s http://localhost:4123/memory | jq .memory_info'

Memory Optimization Tips

For High-Volume Production:

MEMORY_CLEANUP_INTERVAL=3
CUDA_CACHE_CLEAR_INTERVAL=2
ENABLE_MEMORY_MONITORING=false  # Reduce logging overhead
MAX_CHUNK_LENGTH=200             # Smaller chunks for less memory usage

For Development/Debugging:

MEMORY_CLEANUP_INTERVAL=1
CUDA_CACHE_CLEAR_INTERVAL=1
ENABLE_MEMORY_MONITORING=true

Memory Leak Prevention:

Tensors are automatically moved to CPU before deletion
Gradient tracking is disabled during inference
Audio chunks are cleaned up after concatenation
CUDA cache is periodically cleared
Python garbage collection is triggered regularly

🧪 Testing

Run the test script to verify the API functionality:

python tests/test_api.py

The test script will:

Test health check endpoint
Test models endpoint
Test API documentation endpoints (new!)
Generate speech for various text lengths
Test custom parameter validation
Test error handling with validation
Save generated audio files as test_output_*.wav

⚡ Performance

FastAPI Benefits:

Async support: Better concurrent request handling
Faster serialization: JSON responses ~25% faster than Flask
Type validation: Pydantic models prevent invalid requests
Auto documentation: No manual API doc maintenance

Hardware Recommendations:

CPU: Works but slower, reduce chunk size for better memory usage
GPU: Recommended for production, significantly faster
Memory: 4GB minimum, 8GB+ recommended
Concurrency: Async support allows better multi-request handling

🔧 Troubleshooting

Common Issues

CUDA/CPU Compatibility Error

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False

This happens because chatterbox-tts models require PyTorch with CUDA support, even when running on CPU. Solutions:

# Option 1: Use default setup (now includes CUDA-enabled PyTorch)
docker compose -f docker/docker-compose.yml up -d

# Option 2: Use explicit CUDA setup (traditional)
docker compose -f docker/docker-compose.gpu.yml up -d

# Option 3: Use uv + GPU setup (recommended for GPU users)
docker compose -f docker/docker-compose.uv.gpu.yml up -d

# Option 4: Use CPU-only setup (may have compatibility issues)
docker compose -f docker/docker-compose.cpu.yml up -d

# Option 5: Clear model cache and retry with CUDA-enabled setup
docker volume rm chatterbox-tts-api_chatterbox-models
docker compose -f docker/docker-compose.yml up -d --build

# Option 6: Try uv for better dependency resolution
uv sync
uv run uvicorn app.main:app --host 0.0.0.0 --port 4123

For local development, install PyTorch with CUDA support:

# With pip
pip uninstall torch torchvision torchaudio
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
pip install chatterbox-tts

# With uv (handles this automatically)
uv sync

Windows Users, using pip & having issues:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 --force-reinstall
pip install --force-reinstall typing_extensions

Port conflicts

# Change port
echo "PORT=4124" >> .env

GPU not detected

# Force CPU mode
echo "DEVICE=cpu" >> .env

Out of memory

# Reduce chunk size
echo "MAX_CHUNK_LENGTH=200" >> .env

Model download fails

# Clear cache and retry
rm -rf models/
uvicorn app.main:app --host 0.0.0.0 --port 4123  # or: uv run main.py

FastAPI startup issues

# Check if uvicorn is installed
uvicorn --version

# Run with verbose logging
uvicorn app.main:app --host 0.0.0.0 --port 4123 --log-level debug

# Alternative startup method
python main.py

💻 Development

Project Structure

This project follows a clean, modular architecture for maintainability:

app/                     # FastAPI backend application
├── __init__.py           # Main package
├── config.py            # Configuration management
├── main.py              # FastAPI application
├── models/              # Pydantic models
│   ├── requests.py      # Request models
│   └── responses.py     # Response models
├── core/                # Core functionality
│   ├── memory.py        # Memory management
│   ├── text_processing.py # Text processing utilities
│   └── tts_model.py     # TTS model management
└── api/                 # API endpoints
    ├── router.py        # Main router
    └── endpoints/       # Individual endpoint modules
        ├── speech.py    # TTS endpoint
        ├── health.py    # Health check
        ├── models.py    # Model listing
        ├── memory.py    # Memory management
        └── config.py    # Configuration

frontend/                # React frontend application
├── src/
├── Dockerfile
├── nginx.conf          # Integrated proxy configuration
└── package.json

docker/                  # Docker files consolidated
├── Dockerfile
├── Dockerfile.uv       # uv-optimized image
├── Dockerfile.gpu      # GPU-enabled image
├── Dockerfile.cpu      # CPU-only image
├── Dockerfile.uv.gpu   # uv + GPU image
├── docker-compose.yml  # Standard deployment
├── docker-compose.uv.yml # uv deployment
├── docker-compose.gpu.yml # GPU deployment
├── docker-compose.uv.gpu.yml # uv + GPU deployment
└── docker-compose.cpu.yml # CPU-only deployment

tests/                   # Test suite
├── test_api.py         # API tests
└── test_memory.py      # Memory tests

main.py                  # Main entry point
start.py                 # Development helper script

Quick Start Scripts

# Development mode with auto-reload
python start.py dev

# Production mode
python start.py prod

# Full Stack mode with UI (using Docker)
python start.py fullstack

# Run tests
python start.py test

# View project structure
python start.py info

Local Development

# Install in development mode (pip)
pip install -e .

# Or with uv (basic development tools)
uv sync

# Or with test dependencies (for contributors)
uv sync --group test

# Start with auto-reload (FastAPI development)
uvicorn app.main:app --host 0.0.0.0 --port 4123 --reload

# Or use the main script
python main.py

# Or use the development helper
python start.py dev

Testing

# Run API tests
python tests/test_api.py  # or: uv run tests/test_api.py

# Run memory tests
python tests/test_memory.py

# Test specific endpoint
curl http://localhost:4123/health

# Check API documentation
curl http://localhost:4123/openapi.json

FastAPI Development Features

Auto-reload: Use --reload flag for development
Interactive docs: Visit /docs for live API testing
Type hints: Full IDE support with Pydantic models
Validation: Automatic request/response validation
Modular structure: Easy to extend and maintain

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Ensure FastAPI docs are updated
Submit a pull request

Support

📖 Documentation: See API Documentation and Docker Guide
🐛 Issues: Report bugs and feature requests via GitHub issues
💬 Discord: Join the Discord for this project

🔗 Integrations

Open WebUI

Tip

Customize available voices first by using the frontend at http://localhost:4321

To use Chatterbox TTS API with Open WebUI, follow these steps:

Open the Admin Panel and go to Settings -> Audio
Set your TTS Settings to match the following:
- Text-to-Speech Engine: OpenAI
- API Base URL: http://localhost:4123/v1 # alternatively, try http://host.docker.internal:4123/v1
- API Key: none
- TTS Model: tts-1 or tts-1-hd
- TTS Voice: Name of the voice you've cloned (can also include aliases, defined in the frontend)
- Response splitting: Paragraphs

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
app		app
assets		assets
docker		docker
docs		docs
frontend		frontend
tests		tests
.dockerignore		.dockerignore
.env.example		.env.example
.env.example.docker		.env.example.docker
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
install.sh		install.sh
main.py		main.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
setup.py		setup.py
start.py		start.py
voice-sample.mp3		voice-sample.mp3

License

travisvn/chatterbox-tts-api

Folders and files

Latest commit

History

Repository files navigation

Chatterbox TTS API

Features

⚡️ Quick Start

Local Installation with Python 🐍

Option A: Using uv (Recommended - Faster & Better Dependencies)

Option B: Using pip (Traditional)

🐳 Docker (Recommended)

With Docker Compose Profiles

Local Development

Build for Production

Port Configuration

Screenshots of Frontend (Web UI)

API Usage

Basic Text-to-Speech (Default Voice)

Using Custom Parameters (JSON)

Custom Voice Upload

With Custom Parameters and Voice Upload

Voice Library Management

🎵 Real-time Audio Streaming

Quick Start

🚀 Complete Streaming Documentation →

Default Voice (JSON)

Upload Endpoint (Default Voice)

Custom Voice Upload

Basic Streaming Example

SSE Streaming Example (OpenAI Compatible)

Voice File Requirements

🎛️ Configuration

Development

Production

With GPU Support

API Endpoints

Parameters Reference

Speech Generation Parameters

Memory Management Features

Memory Configuration

Memory Monitoring Endpoints

Real-time Status Tracking

Memory Testing

Memory Optimization Tips

Common Issues

Project Structure

Quick Start Scripts

Local Development

Testing

FastAPI Development Features

Support

🔗 Integrations

Open WebUI

➡️ View the Open WebUI docs for installing Chatterbox TTS API

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