Kokoro TTS API

Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model

• OpenAI-compatible Speech endpoint, with voice combination functionality
• NVIDIA GPU accelerated inference (or CPU) option
• very fast generation time (~35x real time factor via 4060Ti)
• automatic chunking/stitching for long texts
• simple audio generation web ui utility

Quick Start

The service can be accessed through either the API endpoints or the Gradio web interface.

1. Install prerequisites:

   • Install Docker Desktop + Git
   • Clone and start the service:

     git clone https://github.com/remsky/Kokoro-FastAPI.git
     cd Kokoro-FastAPI
     docker compose up --build
     

2. Run locally as an OpenAI-Compatible Speech Endpoint

   from openai import OpenAI
   client = OpenAI(
       base_url="http://localhost:8880",
       api_key="not-needed"
       )
   
   response = client.audio.speech.create(
       model="kokoro", 
       voice="af_bella",
       input="Hello world!",
       response_format="mp3"
   )
   response.stream_to_file("output.mp3")
   

   or visit http://localhost:7860

   

Features

OpenAI-Compatible Speech Endpoint

# Using OpenAI's Python library
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8880", api_key="not-needed")
response = client.audio.speech.create(
    model="kokoro",  # Not used but required for compatibility, also accepts library defaults
    voice="af_bella",
    input="Hello world!",
    response_format="mp3"
)

response.stream_to_file("output.mp3")

Or Via Requests:

import requests


response = requests.get("http://localhost:8880/v1/audio/voices")
voices = response.json()["voices"]

# Generate audio
response = requests.post(
    "http://localhost:8880/v1/audio/speech",
    json={
        "model": "kokoro",  # Not used but required for compatibility
        "input": "Hello world!",
        "voice": "af_bella",
        "response_format": "mp3",  # Supported: mp3, wav, opus, flac
        "speed": 1.0
    }
)

# Save audio
with open("output.mp3", "wb") as f:
    f.write(response.content)

Quick tests (run from another terminal):

python examples/test_openai_tts.py # Test OpenAI Compatibility
python examples/test_all_voices.py # Test all available voices

Voice Combination

• Averages model weights of any existing voicepacks
• Saves generated voicepacks for future use

Combine voices and generate audio:

import requests
response = requests.get("http://localhost:8880/v1/audio/voices")
voices = response.json()["voices"]

# Create combined voice (saves locally on server)
response = requests.post(
    "http://localhost:8880/v1/audio/voices/combine",
    json=[voices[0], voices[1]]
)
combined_voice = response.json()["voice"]

# Generate audio with combined voice
response = requests.post(
    "http://localhost:8880/v1/audio/speech",
    json={
        "input": "Hello world!",
        "voice": combined_voice,
        "response_format": "mp3"
    }
)

Multiple Output Audio Formats

• mp3
• wav
• opus
• flac
• aac
• pcm

Gradio Web Utility

Access the interactive web UI at http://localhost:7860 after starting the service. Features include:

• Voice/format/speed selection
• Audio playback and download
• Text file or direct input

If you only want the API, just comment out everything in the docker-compose.yml under and including gradio-ui

Currently, voices created via the API are accessible here, but voice combination/creation has not yet been added

Processing Details

Performance Benchmarks

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:

• Windows 11 Home w/ WSL2
• NVIDIA 4060Ti 16gb GPU @ CUDA 12.1
• 11th Gen i7-11700 @ 2.5GHz
• 64gb RAM
• WAV native output
• H.G. Wells - The Time Machine (full text)

Key Performance Metrics:

• Realtime Factor: Ranges between 35-49x (generation time to output audio length)
• Average Processing Rate: 137.67 tokens/second (cl100k_base)

GPU Vs. CPU

# GPU: Requires NVIDIA GPU with CUDA 12.1 support (~35x realtime speed)
docker compose up --build

# CPU: ONNX optimized inference (~2.4x realtime speed)
docker compose -f docker-compose.cpu.yml up --build

Natural Boundary Detection

• Automatically splits and stitches at sentence boundaries
• Helps to reduce artifacts and allow long form processing as the base model is only currently configured for approximately 30s output

Model and License

Model

This API uses the Kokoro-82M model from HuggingFace.

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.

License

This project is licensed under the Apache License 2.0 - see below for details:

• The Kokoro model weights are licensed under Apache 2.0 (see model page)
• The FastAPI wrapper code in this repository is licensed under Apache 2.0 to match
• The inference code adapted from StyleTTS2 is MIT licensed

The full Apache 2.0 license text can be found at: https://www.apache.org/licenses/LICENSE-2.0