Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
- Multi-language support (English, Japanese, Chinese, Vietnamese soon)
- OpenAI-compatible Speech endpoint, NVIDIA GPU accelerated with CUDA
- Debug endpoints for monitoring system stats, integrated web UI on localhost:8880/web
- Phoneme-based audio generation, phoneme generation
- Per-word timestamped caption generation
- Voice mixing with weighted combinations
Quickest Start (docker run)
Pre built images are available to run, with arm/multi-arch support, and baked in models. Refer to the core/config.py file for a full list of variables which can be managed via the environment.
docker run --gpus all -p 8880:8880 ghcr.io/remsky/kokoro-fastapi:latestQuick Start (docker compose)
- Install prerequisites, and start the service using Docker Compose:
- Install Docker with NVIDIA Container Toolkit
- Clone the repository:
git clone https://github.com/remsky/Kokoro-FastAPI.git cd Kokoro-FastAPI cd docker docker compose up --build # Models will auto-download, but if needed you can manually download: python docker/scripts/download_model.py --output api/src/models/v1_0 # Or run directly via UV: ./start.sh
Direct Run (via uv)
- Install prerequisites:
-
Install astral-uv
-
Install espeak-ng in your system if you want it available as a fallback for unknown words/sounds.
-
Clone the repository:
git clone https://github.com/remsky/Kokoro-FastAPI.git cd Kokoro-FastAPIRun the model download script if you haven't already
Start directly via UV (with hot-reload)
./start.sh
-
Up and Running?
Run locally as an OpenAI-Compatible Speech Endpoint
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8880/v1", api_key="not-needed"
)
with client.audio.speech.with_streaming_response.create(
model="kokoro",
voice="af_sky+af_bella", #single or multiple voicepack combo
input="Hello world!"
) as response:
response.stream_to_file("output.mp3")-
The API will be available at http://localhost:8880
-
API Documentation: http://localhost:8880/docs
-
Web Interface: http://localhost:8880/web
OpenAI-Compatible Speech Endpoint
# Using OpenAI's Python library
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8880/v1", api_key="not-needed")
response = client.audio.speech.create(
model="kokoro",
voice="af_bella+af_sky", # see /api/src/core/openai_mappings.json to customize
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",
"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/assorted_checks/test_openai/test_openai_tts.py # Test OpenAI Compatibility
python examples/assorted_checks/test_voices/test_all_voices.py # Test all available voicesVoice Combination
- Weighted voice combinations using ratios (e.g., "af_bella(2)+af_heart(1)" for 67%/33% mix)
- Ratios are automatically normalized to sum to 100%
- Available through any endpoint by adding weights in parentheses
- 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"]
# Example 1: Simple voice combination (50%/50% mix)
response = requests.post(
"http://localhost:8880/v1/audio/speech",
json={
"input": "Hello world!",
"voice": "af_bella+af_sky", # Equal weights
"response_format": "mp3"
}
)
# Example 2: Weighted voice combination (67%/33% mix)
response = requests.post(
"http://localhost:8880/v1/audio/speech",
json={
"input": "Hello world!",
"voice": "af_bella(2)+af_sky(1)", # 2:1 ratio = 67%/33%
"response_format": "mp3"
}
)
# Example 3: Download combined voice as .pt file
response = requests.post(
"http://localhost:8880/v1/audio/voices/combine",
json="af_bella(2)+af_sky(1)" # 2:1 ratio = 67%/33%
)
# Save the .pt file
with open("combined_voice.pt", "wb") as f:
f.write(response.content)
# Use the downloaded voice file
response = requests.post(
"http://localhost:8880/v1/audio/speech",
json={
"input": "Hello world!",
"voice": "combined_voice", # Use the saved voice file
"response_format": "mp3"
}
)
Multiple Output Audio Formats
- mp3
- wav
- opus
- flac
- m4a
- pcm
Streaming Support
# OpenAI-compatible streaming
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8880/v1", api_key="not-needed")
# Stream to file
with client.audio.speech.with_streaming_response.create(
model="kokoro",
voice="af_bella",
input="Hello world!"
) as response:
response.stream_to_file("output.mp3")
# Stream to speakers (requires PyAudio)
import pyaudio
player = pyaudio.PyAudio().open(
format=pyaudio.paInt16,
channels=1,
rate=24000,
output=True
)
with client.audio.speech.with_streaming_response.create(
model="kokoro",
voice="af_bella",
response_format="pcm",
input="Hello world!"
) as response:
for chunk in response.iter_bytes(chunk_size=1024):
player.write(chunk)Or via requests:
import requests
response = requests.post(
"http://localhost:8880/v1/audio/speech",
json={
"input": "Hello world!",
"voice": "af_bella",
"response_format": "pcm"
},
stream=True
)
for chunk in response.iter_content(chunk_size=1024):
if chunk:
# Process streaming chunks
pass
Key Streaming Metrics:
- First token latency: ~300ms @ chunksize 400
- Adjustable chunking settings for real-time playback
Note: Artifacts in intonation can increase with smaller chunks
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 Speed: Ranges between 35x-100x (generation time to output audio length)
- Average Processing Rate: 137.67 tokens/second (cl100k_base)
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
The model is capable of processing up to a 510 phonemized token chunk at a time, however, this can often lead to 'rushed' speech or other artifacts. An additional layer of chunking is applied in the server, that creates flexible chunks with a TARGET_MIN_TOKENS , TARGET_MAX_TOKENS, and ABSOLUTE_MAX_TOKENS which are configurable via environment variables, and set to 175, 250, 450 by default
Timestamped Captions & Phonemes
Generate audio with word-level timestamps without streaming:
import requests
import base64
import json
response = requests.post(
"http://localhost:8880/dev/captioned_speech",
json={
"model": "kokoro",
"input": "Hello world!",
"voice": "af_bella",
"speed": 1.0,
"response_format": "mp3",
"stream": False,
},
stream=False
)
with open("output.mp3","wb") as f:
audio_json=json.loads(response.content)
# Decode base 64 stream to bytes
chunk_audio=base64.b64decode(audio_json["audio"].encode("utf-8"))
# Process streaming chunks
f.write(chunk_audio)
# Print word level timestamps
print(audio_json["timestamps"])Generate audio with word-level timestamps with streaming:
import requests
import base64
import json
response = requests.post(
"http://localhost:8880/dev/captioned_speech",
json={
"model": "kokoro",
"input": "Hello world!",
"voice": "af_bella",
"speed": 1.0,
"response_format": "mp3",
"stream": True,
},
stream=True
)
f=open("output.mp3","wb")
for chunk in response.iter_lines(decode_unicode=True):
if chunk:
chunk_json=json.loads(chunk)
# Decode base 64 stream to bytes
chunk_audio=base64.b64decode(chunk_json["audio"].encode("utf-8"))
# Process streaming chunks
f.write(chunk_audio)
# Print word level timestamps
print(chunk_json["timestamps"])Phoneme & Token Routes
Convert text to phonemes and/or generate audio directly from phonemes:
import requests
def get_phonemes(text: str, language: str = "a"):
"""Get phonemes and tokens for input text"""
response = requests.post(
"http://localhost:8880/dev/phonemize",
json={"text": text, "language": language} # "a" for American English
)
response.raise_for_status()
result = response.json()
return result["phonemes"], result["tokens"]
def generate_audio_from_phonemes(phonemes: str, voice: str = "af_bella"):
"""Generate audio from phonemes"""
response = requests.post(
"http://localhost:8880/dev/generate_from_phonemes",
json={"phonemes": phonemes, "voice": voice},
headers={"Accept": "audio/wav"}
)
if response.status_code != 200:
print(f"Error: {response.text}")
return None
return response.content
# Example usage
text = "Hello world!"
try:
# Convert text to phonemes
phonemes, tokens = get_phonemes(text)
print(f"Phonemes: {phonemes}") # e.g. ðɪs ɪz ˈoʊnli ɐ tˈɛst
print(f"Tokens: {tokens}") # Token IDs including start/end tokens
# Generate and save audio
if audio_bytes := generate_audio_from_phonemes(phonemes):
with open("speech.wav", "wb") as f:
f.write(audio_bytes)
print(f"Generated {len(audio_bytes)} bytes of audio")
except Exception as e:
print(f"Error: {e}")See examples/phoneme_examples/generate_phonemes.py for a sample script.
Debug Endpoints
Monitor system state and resource usage with these endpoints:
/debug/threads- Get thread information and stack traces/debug/storage- Monitor temp file and output directory usage/debug/system- Get system information (CPU, memory, GPU)
Useful for debugging resource exhaustion or performance issues.
Logging
Global API loguru logging level can be set using the API_LOG_LEVEL environment variable. Defaults to DEBUG.
Docker
Modify the appropriate compose yml or append to command line.
docker run --env 'API_LOG_LEVEL=WARNING' ...Direct via UV
export API_LOG_LEVEL=WARNING
./start.shMissing words & Missing some timestamps
The api will automaticly do text normalization on input text which may incorrectly remove or change some phrases. This can be disabled by adding "normalization_options":{"normalize": false} to your request json:
import requests
response = requests.post(
"http://localhost:8880/v1/audio/speech",
json={
"input": "Hello world!",
"voice": "af_heart",
"response_format": "pcm",
"normalization_options":
{
"normalize": False
}
},
stream=True
)
for chunk in response.iter_content(chunk_size=1024):
if chunk:
# Process streaming chunks
passVersioning & Development
Branching Strategy:
releasebranch: Contains the latest stable build, recommended for production use. Docker images tagged with specific versions (e.g.,v0.3.0) are built from this branch.masterbranch: Used for active development. It may contain experimental features, ongoing changes, or fixes not yet in a stable release. Use this branch if you want the absolute latest code, but be aware it might be less stable. ThelatestDocker tag often points to builds from this branch.
Note: This is a development focused project at its core.
If you run into trouble, you may have to roll back a version on the release tags if something comes up, or build up from source and/or troubleshoot + submit a PR.
Free and open source is a community effort, and there's only really so many hours in a day. If you'd like to support the work, feel free to open a PR, buy me a coffee, or report any bugs/features/etc you find during use.
Linux GPU Permissions
Some Linux users may encounter GPU permission issues when running as non-root. Can't guarantee anything, but here are some common solutions, consider your security requirements carefully
services:
kokoro-tts:
# ... existing config ...
group_add:
- "video"
- "render"services:
kokoro-tts:
# ... existing config ...
user: "${UID}:${GID}"
group_add:
- "video"Note: May require adding host user to groups: sudo usermod -aG docker,video $USER and system restart.
services:
kokoro-tts:
# ... existing config ...
devices:
- /dev/nvidia0:/dev/nvidia0
- /dev/nvidiactl:/dev/nvidiactl
- /dev/nvidia-uvm:/dev/nvidia-uvmWarning: Reduces system security. Use only in development environments.
Prerequisites: NVIDIA GPU, drivers, and container toolkit must be properly configured.
Visit NVIDIA Container Toolkit installation for more detailed information
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
Made with contrib.rocks.