mirror of
https://github.com/remsky/Kokoro-FastAPI.git
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129 lines
4.2 KiB
Markdown
129 lines
4.2 KiB
Markdown
<p align="center">
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<img src="githubbanner.png" alt="Kokoro TTS Banner">
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</p>
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# Kokoro TTS API
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[](https://huggingface.co/hexgrad/Kokoro-82M/tree/8228a351f87c8a6076502c1e3b7e72e821ebec9a)
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[]()
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[]()
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FastAPI wrapper for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) text-to-speech model, providing an OpenAI-compatible endpoint with:
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- NVIDIA GPU acceleration enabled
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- automatic chunking/stitching for long texts
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- very fast generation time (~35-49x RTF)
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## Quick Start
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1. Install prerequisites:
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- Install [Docker Desktop](https://www.docker.com/products/docker-desktop/)
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- Install [Git](https://git-scm.com/downloads) (or download and extract zip)
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2. Clone and run:
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```bash
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# Clone repository
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git clone https://github.com/remsky/Kokoro-FastAPI.git
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cd Kokoro-FastAPI
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# Start the API (will automatically clone source HF repo via git-lfs)
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docker compose up --build
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```
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Test all voices:
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```bash
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python examples/test_all_voices.py
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```
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Test OpenAI compatibility:
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```bash
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python examples/test_openai_tts.py
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```
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## OpenAI-Compatible API
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List available voices:
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```python
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import requests
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response = requests.get("http://localhost:8880/audio/voices")
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voices = response.json()["voices"]
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```
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Generate speech:
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```python
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import requests
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response = requests.post(
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"http://localhost:8880/audio/speech",
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json={
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"model": "kokoro", # Not used but required for compatibility
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"input": "Hello world!",
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"voice": "af_bella",
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"response_format": "mp3", # Supported: mp3, wav, opus, flac
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"speed": 1.0
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}
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)
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# Save audio
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with open("output.mp3", "wb") as f:
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f.write(response.content)
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```
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Using OpenAI's Python library:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:8880", api_key="not-needed")
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response = client.audio.speech.create(
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model="kokoro", # Not used but required for compatibility, also accepts library defaults
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voice="af_bella",
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input="Hello world!",
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response_format="mp3"
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)
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response.stream_to_file("output.mp3")
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```
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## Performance Benchmarks
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Benchmarking was performed on generation via the local API using text lengths up to feature-length books (~1.5 hours output), measuring processing time and realtime factor. Tests were run on:
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- Windows 11 Home w/ WSL2
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- NVIDIA 4060Ti 16gb GPU @ CUDA 12.1
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- 11th Gen i7-11700 @ 2.5GHz
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- 64gb RAM
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- WAV native output
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- H.G. Wells - The Time Machine (full text)
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<p align="center">
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<img src="examples/benchmarks/processing_time.png" width="45%" alt="Processing Time" style="border: 2px solid #333; padding: 10px; margin-right: 1%;">
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<img src="examples/benchmarks/realtime_factor.png" width="45%" alt="Realtime Factor" style="border: 2px solid #333; padding: 10px;">
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</p>
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Key Performance Metrics:
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- Realtime Factor: Ranges between 35-49x (generation time to output audio length)
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- Average Processing Rate: 137.67 tokens/second
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- Efficient Scaling: Maintains performance with long texts through automatic chunking
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- Natural Boundary Detection: Automatically splits and stitches at sentence boundaries to prevent artifacts
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## Features
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- OpenAI-compatible API endpoints
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- Multiple audio formats: mp3, wav, opus, flac, (aac & pcm not implemented)
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- Automatic text chunking and audio stitching
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- GPU-accelerated inference
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## Model
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This API uses the [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) model from HuggingFace.
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Visit the model page for more details about training, architecture, and capabilities. I have no affiliation with any of their work, and produced this wrapper for ease of use and personal projects.
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## License
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This project is licensed under the Apache License 2.0 - see below for details:
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- The Kokoro model weights are licensed under Apache 2.0 (see [model page](https://huggingface.co/hexgrad/Kokoro-82M))
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- The FastAPI wrapper code in this repository is licensed under Apache 2.0 to match
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- The inference code adapted from StyleTTS2 is MIT licensed
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The full Apache 2.0 license text can be found at: https://www.apache.org/licenses/LICENSE-2.0
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