├── .github └── workflows │ └── ci.yml ├── .gitignore ├── .gitmodules ├── CONTRIBUTING.md ├── Dockerfile ├── README.md ├── app ├── __init__.py ├── config.py ├── main.py ├── models.py └── services │ └── tts.py ├── docker-compose.yml ├── pyproject.toml ├── pytest.ini ├── requirements.txt ├── tests └── test_api.py └── uv.lock /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | name: Build and Push Docker Image 2 | 3 | on: 4 | push: 5 | branches: 6 | - '**' 7 | tags: ["v*.*.*"] 8 | pull_request: 9 | branches: 10 | - '**' 11 | 12 | env: 13 | REGISTRY: ghcr.io 14 | IMAGE_NAME: ${{ github.repository }} 15 | CACHE_FROM: type=registry,ref=${{ github.repository }}:buildcache 16 | CACHE_TO: type=registry,ref=${{ github.repository }}:buildcache,mode=max 17 | 18 | jobs: 19 | free-disk-space: 20 | runs-on: ubuntu-latest 21 | steps: 22 | - name: Free Disk Space (Ubuntu) 23 | uses: jlumbroso/free-disk-space@main 24 | with: 25 | tool-cache: false 26 | android: true 27 | dotnet: true 28 | haskell: true 29 | large-packages: true 30 | docker-images: true 31 | swap-storage: true 32 | 33 | build-and-push: 34 | runs-on: ubuntu-latest 35 | needs: free-disk-space # Ensure this job runs after freeing disk space 36 | permissions: 37 | contents: read 38 | packages: write 39 | id-token: write 40 | 41 | steps: 42 | - name: Checkout repository 43 | uses: actions/checkout@v4 44 | with: 45 | submodules: recursive 46 | 47 | - name: Set up Docker Buildx 48 | uses: docker/setup-buildx-action@v3 49 | with: 50 | version: latest 51 | driver-opts: | 52 | image=moby/buildkit:latest 53 | 54 | - name: Log into registry ${{ env.REGISTRY }} 55 | if: github.event_name != 'pull_request' 56 | uses: docker/login-action@v3 57 | with: 58 | registry: ${{ env.REGISTRY }} 59 | username: ${{ github.actor }} 60 | password: ${{ secrets.GITHUB_TOKEN }} 61 | 62 | # Cache Python dependencies 63 | - name: Cache pip packages 64 | uses: actions/cache@v3 65 | with: 66 | path: ~/.cache/pip 67 | key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt', '**/pyproject.toml') }} 68 | restore-keys: | 69 | ${{ runner.os }}-pip- 70 | 71 | # Set image name based on branch 72 | - name: Set image name and tags 73 | id: meta 74 | uses: docker/metadata-action@v5 75 | with: 76 | images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} 77 | tags: | 78 | # For main branch 79 | type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' }} 80 | type=sha,format=short,prefix=,enable=${{ github.ref == 'refs/heads/main' }} 81 | # For other branches 82 | type=raw,value=dev-latest,enable=${{ github.ref != 'refs/heads/main' }} 83 | type=sha,format=short,prefix=dev-,enable=${{ github.ref != 'refs/heads/main' }} 84 | # For tags 85 | type=ref,event=tag 86 | type=semver,pattern={{version}} 87 | type=semver,pattern={{major}}.{{minor}} 88 | type=semver,pattern={{major}} 89 | 90 | - name: Build and push Docker image 91 | id: build-and-push 92 | uses: docker/build-push-action@v5 93 | with: 94 | context: . 95 | push: ${{ github.event_name != 'pull_request' }} 96 | tags: ${{ steps.meta.outputs.tags }} 97 | labels: ${{ steps.meta.outputs.labels }} 98 | cache-from: | 99 | type=gha 100 | type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache 101 | cache-to: | 102 | type=gha,mode=max 103 | type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache,mode=max 104 | platforms: linux/amd64 105 | build-args: | 106 | BUILDKIT_INLINE_CACHE=1 107 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Python 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | *.so 6 | .Python 7 | build/ 8 | develop-eggs/ 9 | dist/ 10 | downloads/ 11 | eggs/ 12 | .eggs/ 13 | lib/ 14 | lib64/ 15 | parts/ 16 | sdist/ 17 | var/ 18 | wheels/ 19 | *.egg-info/ 20 | .installed.cfg 21 | *.egg 22 | 23 | # Virtual Environment 24 | venv/ 25 | ENV/ 26 | env/ 27 | .env 28 | 29 | # IDE 30 | .idea/ 31 | .vscode/ 32 | *.swp 33 | *.swo 34 | .DS_Store 35 | 36 | # Logs 37 | *.log 38 | logs/ 39 | 40 | # Local development 41 | *.db 42 | *.sqlite3 43 | 44 | # Model cache 45 | .cache/ 46 | model_cache/ 47 | 48 | # Test coverage 49 | .coverage 50 | htmlcov/ 51 | 52 | # Docker 53 | .docker/ -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "Zonos"] 2 | path = Zonos 3 | url = https://github.com/Zyphra/Zonos.git 4 | branch = main -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to Zonos API 2 | 3 | We love your input! We want to make contributing to Zonos API as easy and transparent as possible, whether it's: 4 | 5 | - Reporting a bug 6 | - Discussing the current state of the code 7 | - Submitting a fix 8 | - Proposing new features 9 | - Becoming a maintainer 10 | 11 | ## Git Submodules 12 | 13 | This project uses git submodules to manage the Zonos model repository. When working with the codebase: 14 | 15 | 1. Clone with submodules: 16 | ```bash 17 | git clone --recursive https://github.com/manascb1344/zonos-api 18 | ``` 19 | 20 | 2. If you forgot --recursive: 21 | ```bash 22 | git submodule update --init --recursive 23 | ``` 24 | 25 | 3. Update submodules to latest: 26 | ```bash 27 | git submodule update --remote 28 | ``` 29 | 30 | 4. When making changes that require a specific version of Zonos: 31 | ```bash 32 | cd Zonos 33 | git checkout 34 | cd .. 35 | git add Zonos 36 | git commit -m "chore: update Zonos submodule to " 37 | ``` 38 | 39 | ## Development Process 40 | 41 | 1. Clone the repository and set up your environment: 42 | ```bash 43 | # Clone the repo with submodules 44 | git clone --recursive https://github.com/manascb1344/zonos-api 45 | cd zonos-api 46 | 47 | # Create and activate virtual environment 48 | python -m venv venv 49 | source venv/bin/activate # On Windows use `venv\Scripts\activate` 50 | # Install dependencies 51 | pip install -r requirements.txt 52 | 53 | # Install Zonos from submodule 54 | cd Zonos 55 | pip install -e . 56 | cd .. 57 | 58 | # Install GPU optimizations 59 | pip install --no-build-isolation -e .[compile] 60 | 61 | 2. Create a new branch: 62 | ```bash 63 | git checkout -b feature/your-feature-name 64 | ``` 65 | 66 | 3. Make your changes and run the application: 67 | ```bash 68 | # Run the application locally 69 | uvicorn app.main:app --reload 70 | ``` 71 | 72 | 4. Update documentation: 73 | - Update docstrings for any modified functions 74 | - Update README.md if adding new features 75 | - Update CHANGELOG.md following the format 76 | - Update API documentation if endpoints change 77 | 78 | 5. Commit your changes: 79 | ```bash 80 | git add . 81 | git commit -m "feat: your detailed commit message" 82 | ``` 83 | 84 | We follow [Conventional Commits](https://www.conventionalcommits.org/) for commit messages: 85 | - `feat:` for new features 86 | - `fix:` for bug fixes 87 | - `docs:` for documentation changes 88 | - `chore:` for maintenance tasks 89 | - `refactor:` for code refactoring 90 | 91 | ## Docker Development 92 | 93 | For development with Docker: 94 | 95 | ```bash 96 | # Build and run with docker-compose 97 | docker-compose up --build 98 | 99 | # Check logs 100 | docker-compose logs -f api 101 | ``` 102 | 103 | ## Monitoring and Debugging 104 | 105 | The application includes Prometheus metrics and Grafana dashboards: 106 | 107 | 1. Access metrics at: http://localhost:8000/metrics 108 | 2. View Prometheus: http://localhost:9090 109 | 3. Access Grafana: http://localhost:3000 (admin/admin) 110 | 111 | ## Pull Request Process 112 | 113 | 1. Fork the repo and create your branch from `main` 114 | 2. Update the documentation 115 | 3. Update the CHANGELOG.md 116 | 4. Issue that pull request! 117 | 118 | ## License 119 | By contributing, you agree that your contributions will be licensed under its Apache 2.0 License. -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel 2 | 3 | LABEL org.opencontainers.image.source=https://github.com/manascb1344/zonos-api 4 | LABEL org.opencontainers.image.description="Zonos API with GPU support" 5 | LABEL org.opencontainers.image.licenses=MIT 6 | 7 | # Set Zonos working directory 8 | WORKDIR /app/zonos 9 | 10 | # System packages 11 | RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ 12 | --mount=type=cache,target=/var/lib/apt,sharing=locked \ 13 | apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ 14 | ffmpeg \ 15 | libsndfile1 \ 16 | git \ 17 | espeak-ng \ 18 | && apt-get clean \ 19 | && rm -rf /var/lib/apt/lists/* 20 | 21 | # Install uv package manager 22 | RUN --mount=type=cache,target=/root/.cache/pip \ 23 | pip3 install --no-cache-dir uv 24 | 25 | # Clone Zonos directly into working directory 26 | RUN git clone --depth 1 https://github.com/Zyphra/Zonos.git . \ 27 | && git submodule update --init --recursive 28 | 29 | # Copy dependency specs and application code 30 | COPY requirements.txt pyproject.toml ./ 31 | COPY app/ app/ 32 | 33 | # Install basic Python dependencies first 34 | RUN --mount=type=cache,target=/root/.cache/pip \ 35 | uv pip install --system -r requirements.txt -e .[compile] 36 | 37 | # Install Flash Attention with specific compiler flags 38 | ENV TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6+PTX" 39 | ENV FLASH_ATTENTION_FORCE_BUILD=1 40 | RUN --mount=type=cache,target=/root/.cache/pip \ 41 | uv pip install --system --no-build-isolation \ 42 | git+https://github.com/Dao-AILab/flash-attention.git@v2.5.6 43 | 44 | # Install remaining ML dependencies 45 | RUN --mount=type=cache,target=/root/.cache/pip \ 46 | uv pip install --system --no-build-isolation \ 47 | mamba-ssm==2.2.4 \ 48 | causal-conv1d==1.5.0.post8 49 | 50 | RUN --mount=type=cache,target=/root/.cache/pip \ 51 | uv pip install --system \ 52 | kanjize>=1.5.0 \ 53 | inflect>=7.5.0 \ 54 | && rm -rf /root/.cache/pip/* 55 | 56 | RUN --mount=type=cache,target=/root/.cache/pip \ 57 | uv pip install --system \ 58 | phonemizer>=3.3.0 \ 59 | sudachidict-full>=20241021 \ 60 | sudachipy>=0.6.10 \ 61 | && rm -rf /root/.cache/pip/* 62 | 63 | # Copy application code last 64 | COPY app/ app/ 65 | 66 | # Environment variables 67 | ENV PYTHONPATH=/app:/app/zonos \ 68 | USE_GPU=true \ 69 | PYTHONUNBUFFERED=1 70 | 71 | # Run the application 72 | CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] 73 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Zonos API 2 | 3 | > ⚠️ **WARNING: UNSTABLE API - INITIAL RELEASE** ⚠️ 4 | > 5 | > This API is currently in its initial release phase (v1.0.0) and is considered unstable. 6 | > Breaking changes may occur without notice. Use in production at your own risk. 7 | > For development and testing purposes only. 8 | 9 | A production-grade FastAPI implementation of the Zonos Text-to-Speech model. 10 | 11 | ## Credits 12 | 13 | This API is built on top of the [Zonos-v0.1-hybrid](https://huggingface.co/Zyphra/Zonos-v0.1-hybrid) and [Zonos-v0.1-transformer](https://huggingface.co/Zyphra/Zonos-v0.1-transformer) models created by [Zyphra](https://huggingface.co/Zyphra). The models feature: 14 | 15 | - Zero-shot TTS with voice cloning capabilities 16 | - Support for multiple languages (100+ languages via eSpeak-ng) 17 | - High-quality 44kHz audio output 18 | - Fine-grained control over speaking rate, pitch, audio quality, and emotions 19 | - Real-time performance (~2x real-time on RTX 4090) 20 | 21 | For more information, visit the model cards on Hugging Face: [Hybrid](https://huggingface.co/Zyphra/Zonos-v0.1-hybrid) | [Transformer](https://huggingface.co/Zyphra/Zonos-v0.1-transformer). 22 | 23 | ## Features 24 | 25 | - FastAPI-based REST API for Zonos Text-to-Speech model 26 | - Support for both Transformer and Hybrid model variants 27 | - Docker and docker-compose support with NVIDIA GPU acceleration 28 | - Production-ready with Gunicorn workers and optimizations 29 | - Prometheus and Grafana monitoring integration 30 | - Health checks and comprehensive logging 31 | - CORS support and Swagger documentation 32 | - Voice cloning and audio continuation support 33 | - Fine-grained emotion and audio quality control 34 | 35 | ## Quick Start 36 | 37 | ### Using Pre-built Image 38 | 39 | The fastest way to get started is using our pre-built Docker image: 40 | ```bash 41 | docker pull ghcr.io/manascb1344/zonos-api-gpu:v1.0.0 42 | docker run -d \ 43 | --name zonos-api-gpu \ 44 | --gpus all \ 45 | -p 8000:8000 \ 46 | -e CUDA_VISIBLE_DEVICES=0 \ 47 | zonos-api-gpu 48 | ``` 49 | 50 | ### Manual Installation 51 | 52 | 1. Clone the repository with submodules: 53 | ```bash 54 | git clone --recursive https://github.com/manascb1344/zonos-api 55 | cd zonos-api 56 | ``` 57 | 58 | The API will be available at `http://localhost:8000` 59 | 60 | ## Running with Docker 61 | 62 | 1. Build the container: 63 | ```bash 64 | docker build -t zonos-api . 65 | ``` 66 | 67 | 2. Run the container: 68 | ```bash 69 | docker run -d \ 70 | --name zonos-api \ 71 | --gpus all \ 72 | -p 8000:8000 \ 73 | -e CUDA_VISIBLE_DEVICES=0 \ 74 | zonos-api 75 | ``` 76 | 77 | ## Environment Variables 78 | 79 | - `CUDA_VISIBLE_DEVICES`: Specify which GPU(s) to use (default: 0) 80 | - `USE_GPU`: Enable/disable GPU usage (default: true) 81 | 82 | ## Requirements 83 | 84 | - Docker with NVIDIA Container Toolkit installed 85 | - NVIDIA GPU with CUDA support 86 | - At least 8GB of GPU memory recommended 87 | 88 | ## Verifying the Installation 89 | 90 | Check if the API is running: 91 | ```bash 92 | curl http://localhost:8000/health 93 | ``` 94 | 95 | ## API Endpoints 96 | 97 | ### GET / 98 | Root endpoint that returns basic API information 99 | 100 | ### GET /models 101 | Returns a list of available TTS models 102 | 103 | ### GET /languages 104 | Returns a list of supported languages 105 | 106 | ### GET /model/{model_name}/conditioners 107 | Returns available conditioners for a specific model 108 | 109 | ### POST /synthesize 110 | Generate speech from text. Example request: 111 | 112 | ```json 113 | { 114 | "model_choice": "Zyphra/Zonos-v0.1-transformer", 115 | "text": "Hello, this is a test.", 116 | "language": "en-us", 117 | "emotion_values": [1.0, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 0.2], 118 | "vq_score": 0.78, 119 | "cfg_scale": 2.0, 120 | "min_p": 0.15 121 | } 122 | ``` 123 | 124 | ## Environment Variables 125 | 126 | - `USE_GPU`: Set to "true" to enable GPU acceleration (default: true) 127 | - `PYTHONPATH`: Set to the application root directory 128 | 129 | ## GPU Support 130 | 131 | The API uses NVIDIA GPU acceleration by default. Make sure you have: 132 | 1. NVIDIA GPU with CUDA support 133 | 2. NVIDIA drivers installed 134 | 3. NVIDIA Container Toolkit installed and configured 135 | 136 | ## Development 137 | 138 | ### Prerequisites 139 | - Python 3.10+ 140 | - NVIDIA GPU with CUDA support (recommended) 141 | - Docker and docker-compose (for containerized deployment) 142 | 143 | ### Local Development 144 | ```bash 145 | # Start in development mode 146 | uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload 147 | 148 | # Or with docker-compose 149 | docker-compose up --build 150 | ``` 151 | 152 | ## License 153 | 154 | This project is licensed under the Apache License 2.0 - see the LICENSE file for details. -------------------------------------------------------------------------------- /app/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Zonos API package. 3 | """ 4 | 5 | __version__ = "0.1.0" -------------------------------------------------------------------------------- /app/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # Configuration 4 | IS_HF_SPACES = os.getenv("SYSTEM", "") == "spaces" 5 | MAX_CHARACTERS = 2000 6 | 7 | HEADER_MARKDOWN = """ 8 | # Zonos v0.1 9 | State of the art text-to-speech model [[model]](https://huggingface.co/collections/Zyphra/zonos-v01-67ac661c85e1898670823b4f). [[blog]](https://www.zyphra.com/post/beta-release-of-zonos-v0-1), [[Zyphra Audio (hosted service)]](https://maia.zyphra.com/sign-in?redirect_url=https%3A%2F%2Fmaia.zyphra.com%2Faudio) 10 | ## Unleashed 11 | Use this space to generate long-form speech up to around ~2 minutes in length. To generate an unlimited length, clone this space and run it locally. 12 | ### Tips 13 | - When providing prefix audio, include the text of the prefix audio in your speech text to ensure a smooth transition. 14 | - The appropriate range of Speaking Rate and Pitch STD are highly dependent on the speaker audio. Start with the defaults and adjust as needed. 15 | - Emotion sliders do not completely function intuitively, and require some experimentation to get the desired effect. 16 | """.strip() -------------------------------------------------------------------------------- /app/main.py: -------------------------------------------------------------------------------- 1 | from fastapi import FastAPI, HTTPException 2 | from fastapi.responses import StreamingResponse 3 | import io 4 | import wave 5 | import numpy as np 6 | from typing import List, Optional 7 | import logging 8 | import sys 9 | 10 | from .models import TTSRequest 11 | from .services.tts import TTSService 12 | from .config import MAX_CHARACTERS 13 | 14 | # Configure logging 15 | logging.basicConfig( 16 | level=logging.INFO, 17 | format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', 18 | handlers=[ 19 | logging.StreamHandler(sys.stdout) 20 | ] 21 | ) 22 | logger = logging.getLogger("zonos-tts-api") 23 | 24 | # Initialize FastAPI app 25 | app = FastAPI( 26 | title="Zonos Text-to-Speech API", 27 | description="API for generating high-quality speech using Zonos models", 28 | version="0.1.0" 29 | ) 30 | 31 | # TTS service instance 32 | _tts_service: Optional[TTSService] = None 33 | 34 | def get_tts_service() -> TTSService: 35 | """Get or initialize TTS service singleton.""" 36 | global _tts_service 37 | if _tts_service is None: 38 | logger.info("Initializing TTS service") 39 | _tts_service = TTSService() 40 | return _tts_service 41 | 42 | @app.get("/") 43 | async def root(): 44 | """Root endpoint.""" 45 | logger.debug("Root endpoint accessed") 46 | return {"message": "Zonos Text-to-Speech API"} 47 | 48 | @app.get("/models") 49 | async def get_models(): 50 | """Get available TTS models.""" 51 | logger.debug("Fetching available models") 52 | service = get_tts_service() 53 | models = service.get_model_names() 54 | logger.info(f"Retrieved {len(models)} models") 55 | return {"models": models} 56 | 57 | @app.get("/languages") 58 | async def get_languages(): 59 | """Get supported languages.""" 60 | logger.debug("Fetching supported languages") 61 | service = get_tts_service() 62 | languages = service.get_supported_languages() 63 | logger.info(f"Retrieved {len(languages)} supported languages") 64 | return {"languages": languages} 65 | 66 | @app.get("/model/conditioners") 67 | async def get_model_conditioners(model_name: str): 68 | """Get available conditioners for a specific model.""" 69 | logger.debug(f"Fetching conditioners for model: {model_name}") 70 | service = get_tts_service() 71 | logger.info(f"Requested model: {model_name}") 72 | logger.debug(f"Available models: {service.get_model_names()}") 73 | if model_name not in service.get_model_names(): 74 | logger.warning(f"Model not found: {model_name}") 75 | raise HTTPException(status_code=404, detail="Model not found") 76 | conditioners = service.get_model_conditioners(model_name) 77 | logger.info(f"Retrieved {len(conditioners)} conditioners for model {model_name}") 78 | return {"conditioners": conditioners} 79 | 80 | @app.post("/synthesize") 81 | async def synthesize_speech(request: TTSRequest): 82 | """Generate speech from text.""" 83 | logger.info(f"Speech synthesis requested for text of length {len(request.text)} chars using model {request.model_choice}") 84 | 85 | if len(request.text) > MAX_CHARACTERS: 86 | logger.warning(f"Text length ({len(request.text)}) exceeds maximum of {MAX_CHARACTERS} characters") 87 | raise HTTPException( 88 | status_code=400, 89 | detail=f"Text length exceeds maximum of {MAX_CHARACTERS} characters" 90 | ) 91 | 92 | service = get_tts_service() 93 | 94 | try: 95 | logger.debug(f"Starting audio generation with params: language={request.language}, " 96 | f"speaking_rate={request.speaking_rate}, seed={request.seed}") 97 | 98 | # Generate audio using TTS service 99 | (sample_rate, audio_data), seed = service.generate_audio( 100 | model_choice=request.model_choice, 101 | text=request.text, 102 | language=request.language, 103 | speaker_audio=request.speaker_audio, 104 | prefix_audio=request.prefix_audio, 105 | emotion_values=request.emotion_values, 106 | vq_score=request.vq_score, 107 | fmax=request.fmax, 108 | pitch_std=request.pitch_std, 109 | speaking_rate=request.speaking_rate, 110 | dnsmos_ovrl=request.dnsmos_ovrl, 111 | speaker_noised=request.speaker_noised, 112 | cfg_scale=request.cfg_scale, 113 | min_p=request.min_p, 114 | seed=request.seed, 115 | randomize_seed=request.randomize_seed, 116 | unconditional_keys=request.unconditional_keys, 117 | top_p=request.top_p, 118 | top_k=request.top_k, 119 | linear=request.linear, 120 | confidence=request.confidence, 121 | quadratic=request.quadratic, 122 | ) 123 | 124 | logger.info(f"Successfully generated audio with sample rate {sample_rate}Hz, seed {seed}") 125 | 126 | # Convert to WAV format 127 | wav_buffer = io.BytesIO() 128 | with wave.open(wav_buffer, 'wb') as wav_file: 129 | wav_file.setnchannels(1) 130 | wav_file.setsampwidth(2) # 16-bit audio 131 | wav_file.setframerate(sample_rate) 132 | wav_file.writeframes((audio_data * 32767).astype(np.int16).tobytes()) 133 | 134 | # Prepare response 135 | wav_buffer.seek(0) 136 | response = StreamingResponse( 137 | wav_buffer, 138 | media_type="audio/wav", 139 | headers={"x-seed": str(seed)} 140 | ) 141 | 142 | logger.debug("Returning WAV audio stream response") 143 | return response 144 | 145 | except Exception as e: 146 | logger.error(f"Error during speech synthesis: {str(e)}", exc_info=True) 147 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /app/models.py: -------------------------------------------------------------------------------- 1 | from typing import List, Optional 2 | from pydantic import BaseModel, Field 3 | 4 | class TTSRequest(BaseModel): 5 | model_choice: str = Field(description="Model variant to use") 6 | text: str = Field(description="Text to convert to speech") 7 | language: str = Field(description="Language code", default="en-us") 8 | speaker_audio: Optional[str] = Field(default=None, description="Path to speaker audio file") 9 | prefix_audio: Optional[str] = Field(default=None, description="Path to prefix audio file") 10 | emotion_values: List[float] = Field( 11 | default=[1.0, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 0.2], 12 | description="List of 8 emotion values: happiness, sadness, disgust, fear, surprise, anger, other, neutral" 13 | ) 14 | vq_score: float = Field(default=0.78, description="VQ Score value") 15 | fmax: float = Field(default=24000, description="Maximum frequency") 16 | pitch_std: float = Field(default=45.0, description="Pitch standard deviation") 17 | speaking_rate: float = Field(default=15.0, description="Speaking rate") 18 | dnsmos_ovrl: float = Field(default=4.0, description="DNSMOS overall score") 19 | speaker_noised: bool = Field(default=False, description="Whether to denoise speaker audio") 20 | cfg_scale: float = Field(default=2.0, description="CFG scale value") 21 | min_p: float = Field(default=0.15, description="Minimum probability") 22 | seed: int = Field(default=420, description="Random seed") 23 | randomize_seed: bool = Field(default=True, description="Whether to randomize seed") 24 | unconditional_keys: List[str] = Field( 25 | default=["emotion"], 26 | description="List of conditioning keys to make unconditional" 27 | ) 28 | top_p: float = Field(default=0.95, description="Top-p sampling value") 29 | top_k: int = Field(default=50, description="Top-k sampling value") 30 | linear: float = Field(default=1.0, description="Linear scaling factor") 31 | confidence: float = Field(default=0.1, description="Confidence scaling factor") 32 | quadratic: float = Field(default=1.0, description="Quadratic scaling factor") 33 | 34 | class AudioResponse(BaseModel): 35 | sample_rate: int 36 | audio_data: bytes 37 | seed: int -------------------------------------------------------------------------------- /app/services/tts.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torchaudio 3 | import logging 4 | from typing import List, Tuple, Optional, Dict, Any 5 | import numpy as np 6 | from zonos.model import Zonos 7 | from zonos.conditioning import make_cond_dict, supported_language_codes 8 | 9 | # Configure logging 10 | logging.basicConfig(level=logging.INFO) 11 | logger = logging.getLogger(__name__) 12 | 13 | class TTSService: 14 | def __init__(self, device: str = "cuda"): 15 | self.device = device 16 | self.model_names = ["Zyphra/Zonos-v0.1-transformer"] 17 | self.models = { 18 | name: Zonos.from_pretrained(name, device=device, backbone="torch") 19 | for name in self.model_names 20 | } 21 | 22 | # Debugging: Print loaded models 23 | print(f"Loaded models: {self.models.keys()}") 24 | 25 | # Set models to eval mode 26 | for model in self.models.values(): 27 | model.requires_grad_(False).eval() 28 | 29 | def get_model_names(self) -> List[str]: 30 | logger.info("Retrieving model names") 31 | return self.model_names 32 | 33 | def get_supported_languages(self) -> List[str]: 34 | logger.info("Retrieving supported languages") 35 | return supported_language_codes 36 | 37 | def get_model_conditioners(self, model_choice: str) -> List[str]: 38 | """Get list of conditioner names for a model""" 39 | logger.info(f"Retrieving conditioners for model: {model_choice}") 40 | model = self.models[model_choice] 41 | return [c.name for c in model.prefix_conditioner.conditioners] 42 | 43 | def generate_audio( 44 | self, 45 | model_choice: str, 46 | text: str, 47 | language: str = "en-us", 48 | speaker_audio: Optional[str] = None, 49 | prefix_audio: Optional[str] = None, 50 | emotion_values: List[float] = [1.0, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 0.2], 51 | vq_score: float = 0.78, 52 | fmax: float = 24000, 53 | pitch_std: float = 45.0, 54 | speaking_rate: float = 15.0, 55 | dnsmos_ovrl: float = 4.0, 56 | speaker_noised: bool = False, 57 | cfg_scale: float = 2.0, 58 | min_p: float = 0.15, 59 | seed: int = 420, 60 | randomize_seed: bool = True, 61 | unconditional_keys: List[str] = ["emotion"], 62 | top_p: float = 0.95, 63 | top_k: int = 50, 64 | linear: float = 1.0, 65 | confidence: float = 0.1, 66 | quadratic: float = 1.0, 67 | ) -> Tuple[Tuple[int, np.ndarray], int]: 68 | """ 69 | Generate audio using the specified model and parameters. 70 | Returns a tuple of ((sample_rate, audio_data), seed). 71 | """ 72 | logger.info(f"Generating audio for model: {model_choice}") 73 | selected_model = self.models[model_choice] 74 | 75 | if randomize_seed: 76 | seed = torch.randint(0, 2**32 - 1, (1,)).item() 77 | torch.manual_seed(seed) 78 | 79 | # Process speaker audio if provided 80 | speaker_embedding = None 81 | if speaker_audio is not None and "speaker" not in unconditional_keys: 82 | logger.info(f"Processing speaker audio: {speaker_audio}") 83 | wav, sr = torchaudio.load(speaker_audio) 84 | speaker_embedding = selected_model.make_speaker_embedding(wav, sr) 85 | speaker_embedding = speaker_embedding.to(self.device, dtype=torch.bfloat16) 86 | 87 | # Process prefix audio if provided 88 | audio_prefix_codes = None 89 | if prefix_audio is not None: 90 | logger.info(f"Processing prefix audio: {prefix_audio}") 91 | wav_prefix, sr_prefix = torchaudio.load(prefix_audio) 92 | wav_prefix = wav_prefix.mean(0, keepdim=True) 93 | wav_prefix = torchaudio.functional.resample( 94 | wav_prefix, 95 | sr_prefix, 96 | selected_model.autoencoder.sampling_rate 97 | ) 98 | wav_prefix = wav_prefix.to(self.device, dtype=torch.float32) 99 | with torch.autocast(self.device, dtype=torch.float32): 100 | audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0)) 101 | 102 | # Prepare emotion tensor 103 | emotion_tensor = torch.tensor(emotion_values, device=self.device) 104 | 105 | # Prepare VQ score tensor 106 | vq_tensor = torch.tensor([vq_score] * 8, device=self.device).unsqueeze(0) 107 | 108 | # Create conditioning dictionary 109 | logger.info("Creating conditioning dictionary") 110 | cond_dict = make_cond_dict( 111 | text=text, 112 | language=language, 113 | speaker=speaker_embedding, 114 | emotion=emotion_tensor, 115 | vqscore_8=vq_tensor, 116 | fmax=float(fmax), 117 | pitch_std=float(pitch_std), 118 | speaking_rate=float(speaking_rate), 119 | dnsmos_ovrl=float(dnsmos_ovrl), 120 | speaker_noised=bool(speaker_noised), 121 | device=self.device, 122 | unconditional_keys=unconditional_keys, 123 | ) 124 | conditioning = selected_model.prepare_conditioning(cond_dict) 125 | 126 | # sampling parameters 127 | sampling_params = { 128 | "top_p": float(top_p), 129 | "top_k": int(top_k), 130 | "min_p": float(min_p), 131 | "linear": float(linear), 132 | "conf": float(confidence), 133 | "quad": float(quadratic) 134 | } 135 | 136 | # Generate audio 137 | logger.info("Generating audio") 138 | max_new_tokens = 86 * 60 # ~30 seconds of audio 139 | codes = selected_model.generate( 140 | prefix_conditioning=conditioning, 141 | audio_prefix_codes=audio_prefix_codes, 142 | max_new_tokens=max_new_tokens, 143 | cfg_scale=float(cfg_scale), 144 | batch_size=1, 145 | sampling_params=sampling_params, 146 | ) 147 | 148 | # Decode generated codes to waveform 149 | logger.info("Decoding generated audio to waveform") 150 | wav_out = selected_model.autoencoder.decode(codes).cpu().detach() 151 | sr_out = selected_model.autoencoder.sampling_rate 152 | if wav_out.dim() == 2 and wav_out.size(0) > 1: 153 | wav_out = wav_out[0:1, :] 154 | 155 | logger.info("Audio generation complete") 156 | return (sr_out, wav_out.squeeze().numpy()), seed -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '3.8' 2 | 3 | services: 4 | zonos: 5 | build: . 6 | image: zonos-local 7 | ports: 8 | - "8000:8000" 9 | environment: 10 | - CUDA_VISIBLE_DEVICES=0 11 | - NVIDIA_VISIBLE_DEVICES=all 12 | deploy: 13 | resources: 14 | reservations: 15 | devices: 16 | - driver: nvidia 17 | count: 1 18 | capabilities: [gpu] -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "zonos-api" 3 | version = "0.1.0" 4 | description = "API for Zonos project" 5 | readme = "README.md" 6 | requires-python = ">=3.10" 7 | dependencies = [ 8 | "fastapi>=0.109.0", 9 | "uvicorn>=0.27.0", 10 | "pydantic>=2.6.0", 11 | "python-multipart>=0.0.9", 12 | "python-dotenv>=1.0.0" 13 | ] 14 | 15 | [project.optional-dependencies] 16 | dev = [ 17 | "black>=24.1.0", 18 | "isort>=5.13.0", 19 | "pytest>=8.0.0", 20 | "pytest-asyncio>=0.23.0" 21 | ] 22 | 23 | [build-system] 24 | requires = ["hatchling"] 25 | build-backend = "hatchling.build" 26 | 27 | [tool.hatch.build.targets.wheel] 28 | packages = ["app"] 29 | 30 | [tool.black] 31 | line-length = 88 32 | target-version = ["py310"] 33 | 34 | [tool.isort] 35 | profile = "black" 36 | multi_line_output = 3 37 | -------------------------------------------------------------------------------- /pytest.ini: -------------------------------------------------------------------------------- 1 | [pytest] 2 | testpaths = tests 3 | python_files = test_*.py 4 | python_functions = test_* 5 | addopts = -v --cov=app --cov-report=term-missing -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # Runtime dependencies 2 | torch 3 | torchaudio 4 | fastapi 5 | uvicorn 6 | python-multipart 7 | numpy 8 | soundfile 9 | gradio 10 | pydantic 11 | 12 | # Test dependencies 13 | pytest 14 | pytest-cov 15 | httpx 16 | pytest-asyncio -------------------------------------------------------------------------------- /tests/test_api.py: -------------------------------------------------------------------------------- 1 | from fastapi.testclient import TestClient 2 | from unittest.mock import Mock, patch 3 | import numpy as np 4 | 5 | from app.main import app 6 | from app.services.tts import TTSService 7 | 8 | client = TestClient(app) 9 | 10 | def test_root(): 11 | response = client.get("/") 12 | assert response.status_code == 200 13 | assert response.json()["message"] == "Zonos Text-to-Speech API" 14 | 15 | def test_get_models(): 16 | with patch('app.main.get_tts_service') as mock_get_service: 17 | mock_service = Mock(spec=TTSService) 18 | mock_service.get_model_names.return_value = [ 19 | "Zyphra/Zonos-v0.1-transformer", 20 | "Zyphra/Zonos-v0.1-hybrid" 21 | ] 22 | mock_get_service.return_value = mock_service 23 | 24 | response = client.get("/models") 25 | assert response.status_code == 200 26 | assert "models" in response.json() 27 | assert len(response.json()["models"]) == 2 28 | 29 | def test_get_languages(): 30 | with patch('app.main.get_tts_service') as mock_get_service: 31 | mock_service = Mock(spec=TTSService) 32 | mock_service.get_supported_languages.return_value = ["en-us", "es-es"] 33 | mock_get_service.return_value = mock_service 34 | 35 | response = client.get("/languages") 36 | assert response.status_code == 200 37 | assert "languages" in response.json() 38 | assert len(response.json()["languages"]) == 2 39 | 40 | def test_synthesize_speech(): 41 | with patch('app.main.get_tts_service') as mock_get_service: 42 | mock_service = Mock(spec=TTSService) 43 | # Mock audio generation 44 | mock_service.generate_audio.return_value = ( 45 | (44100, np.zeros(44100).astype(np.float32)), # 1 second of silence 46 | 42 # seed 47 | ) 48 | mock_get_service.return_value = mock_service 49 | 50 | test_request = { 51 | "model_choice": "Zyphra/Zonos-v0.1-transformer", 52 | "text": "Test speech", 53 | 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