mirror of
https://github.com/remsky/Kokoro-FastAPI.git
synced 2025-04-13 09:39:17 +00:00
Refactor Docker setup to use a dedicated model-fetcher service and update schemas for additional voice support
This commit is contained in:
remsky
parent
4123ab0891
commit
36606f7234
11 changed files with 576 additions and 279 deletions
32
Dockerfile
32
Dockerfile
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@ -1,16 +1,3 @@
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# Stage 1: Clone model repository
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FROM alpine/git:latest AS model_layer
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ARG KOKORO_REPO=https://huggingface.co/hexgrad/Kokoro-82M
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ARG KOKORO_COMMIT=a67f11354c3e38c58c3327498bc4bd1e57e71c50
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RUN git lfs install --skip-repo
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WORKDIR /app/Kokoro-82M
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RUN GIT_LFS_SKIP_SMUDGE=1 git clone ${KOKORO_REPO} . && \
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git checkout ${KOKORO_COMMIT} && \
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git lfs pull && \
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ls -la
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# Stage 2: Build
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FROM nvidia/cuda:12.1.0-base-ubuntu22.04
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# Install base system dependencies
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@ -30,28 +17,27 @@ RUN pip3 install --no-cache-dir torch==2.5.1 --extra-index-url https://download.
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COPY requirements.txt .
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RUN pip3 install --no-cache-dir -r requirements.txt
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# Copy application code and model
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COPY . /app/
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# Set working directory
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WORKDIR /app
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# Run with Python unbuffered output for live logging
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ENV PYTHONUNBUFFERED=1
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# Copy model files from git clone stage
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COPY --from=model_layer /app/Kokoro-82M /app/Kokoro-82M
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# Create non-root user
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RUN useradd -m -u 1000 appuser
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# Create and set permissions for output directory
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RUN mkdir -p /app/api/src/output && \
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chown -R appuser:appuser /app/api/src/output
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# Set Python path (app first for our imports, then model dir for model imports)
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ENV PYTHONPATH=/app:/app/Kokoro-82M
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# Create directories and set permissions
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RUN mkdir -p /app/Kokoro-82M && \
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chown -R appuser:appuser /app
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# Switch to non-root user
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USER appuser
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# Set Python path (app first for our imports, then model dir for model imports)
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ENV PYTHONPATH=/app:/app/Kokoro-82M
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# Run FastAPI server with debug logging and reload
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CMD ["uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"]
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131
README.md
131
README.md
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@ -3,81 +3,114 @@
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</p>
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# Kokoro TTS API
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[](https://huggingface.co/hexgrad/Kokoro-82M/tree/a67f11354c3e38c58c3327498bc4bd1e57e71c50)
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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.
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Dockerized with NVIDIA GPU support, simple queue handling via sqllite, and automatic chunking/stitching on lengthy input/outputs
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OpenAI-compatible API with NVIDIA GPU support, with automatic chunking/stitching for long texts, and 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 it out:
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Test all voices:
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```bash
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# From host terminal
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python examples/test_tts.py "Hello world" --voice af_bella
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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:8000/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:8000/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, aac
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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:8000", 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 solely on generation via the local API (ignoring file transfers) using various text lengths up to ~10 minutes output, measuring processing time, token count, and output audio length. Tests were run on:
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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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- Randomized chunks from H.G. Wells - The Time Machine
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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/time_vs_output.png" width="40%" alt="Processing Time vs Output Length" style="border: 2px solid #333; padding: 10px; margin-right: 1%;">
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<img src="examples/time_vs_tokens.png" width="40%" alt="Processing Time vs Token Count" style="border: 2px solid #333; padding: 10px;">
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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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- Average processing speed: ~3.4 seconds per minute of audio output
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- Efficient token processing: ~0.01 seconds per token
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- Scales well with longer texts, maintains consistent performance
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## Features
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## API Endpoints
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```bash
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GET /tts/voices # List available voices
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POST /tts # Generate speech
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GET /tts/{request_id} # Check generation status
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GET /tts/file/{request_id} # Download audio file
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```
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## Example Usage
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List available voices:
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```bash
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python examples/test_tts.py
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```
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Generate speech:
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```bash
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# Default voice
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python examples/test_tts.py "Your text here"
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# Specific voice
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python examples/test_tts.py --voice af_bella "Your text here"
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# Get file path without downloading
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python examples/test_tts.py --no-download "Your text here"
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```
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Generated files are saved in:
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- With download: `examples/output/`
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- Without download: `src/output/` (in API container)
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## Requirements
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- Docker
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- NVIDIA GPU + CUDA
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- nvidia-container-toolkit installed on host
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- OpenAI-compatible API endpoints
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- Multiple audio formats: mp3, wav, opus, flac, aac
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- Automatic text chunking and audio stitching
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- GPU-accelerated inference
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- Queue handling via SQLite
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- Progress tracking for long generations
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## Model
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@ -92,7 +92,7 @@ class TTSService:
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# Validate phonemization first
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ps = phonemize(chunk, voice[0])
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tokens = tokenize(ps)
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logger.info(
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logger.debug(
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f"Processing chunk {i+1}/{len(chunks)}: {len(tokens)} tokens"
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)
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@ -26,6 +26,7 @@ class OpenAISpeechRequest(BaseModel):
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"bf_emma",
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"af_sarah",
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"af_bella",
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"af_nicole",
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] = Field(default="af", description="The voice to use for generation")
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response_format: Literal["mp3", "opus", "aac", "flac", "wav", "pcm"] = Field(
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default="mp3",
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@ -1,11 +1,33 @@
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services:
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model-fetcher:
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image: datamachines/git-lfs:latest
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volumes:
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- ./Kokoro-82M:/app/Kokoro-82M
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working_dir: /app/Kokoro-82M
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command: >
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sh -c "
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if [ -z \"$(ls -A .)\" ]; then
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git clone https://huggingface.co/hexgrad/Kokoro-82M . && \
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git checkout 8228a351f87c8a6076502c1e3b7e72e821ebec9a;
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touch .cloned;
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else
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touch .cloned;
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fi;
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tail -f /dev/null
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"
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healthcheck:
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test: ["CMD", "test", "-f", ".cloned"]
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interval: 1s
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timeout: 1s
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retries: 120
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start_period: 1s
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kokoro-tts:
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build:
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context: .
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volumes:
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- ./api/src:/app/api/src
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- ./Kokoro-82M:/app/Kokoro-82M
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- ./api/src/output:/app/api/src/output
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ports:
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- "8880:8880"
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environment:
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@ -17,3 +39,6 @@ services:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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depends_on:
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model-fetcher:
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condition: service_healthy
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@ -2,278 +2,530 @@
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"results": [
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{
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"tokens": 100,
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"processing_time": 0.965601921081543,
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"processing_time": 8.54442310333252,
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"output_length": 31.15,
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"realtime_factor": 32.25967069857295,
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"elapsed_time": 1.1258533000946045
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"realtime_factor": 3.6456527987068887,
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"elapsed_time": 8.720048666000366
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},
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{
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"tokens": 200,
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"processing_time": 1.2544827461242676,
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"processing_time": 1.3838517665863037,
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"output_length": 62.6,
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"realtime_factor": 49.901045027046486,
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"elapsed_time": 2.52616286277771
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"realtime_factor": 45.236058883981606,
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"elapsed_time": 10.258155345916748
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},
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{
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"tokens": 300,
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"processing_time": 2.121187686920166,
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"output_length": 96.475,
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"realtime_factor": 45.48159533213006,
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"elapsed_time": 4.830777883529663
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"processing_time": 2.2024788856506348,
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"output_length": 96.325,
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"realtime_factor": 43.73481200095347,
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"elapsed_time": 12.594647407531738
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},
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{
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"tokens": 400,
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"processing_time": 2.715940475463867,
|
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"output_length": 128.675,
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"realtime_factor": 47.3776951897383,
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"elapsed_time": 7.719157934188843
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"processing_time": 3.175424098968506,
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"output_length": 128.55,
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"realtime_factor": 40.48278150995886,
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"elapsed_time": 16.005898475646973
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},
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{
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"tokens": 500,
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"processing_time": 3.1464896202087402,
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"output_length": 158.775,
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"realtime_factor": 50.46099595569833,
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"processing_time": 3.205301523208618,
|
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"output_length": 158.55,
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"realtime_factor": 49.46492517224587,
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"elapsed_time": 19.377076625823975
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},
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{
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"tokens": 750,
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"output_length": 237.25,
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"realtime_factor": 46.18661562847511,
|
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"elapsed_time": 16.39173412322998
|
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"tokens": 600,
|
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"processing_time": 3.9976348876953125,
|
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"output_length": 189.225,
|
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"realtime_factor": 47.33423769700254,
|
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"elapsed_time": 23.568575859069824
|
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},
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{
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"tokens": 700,
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"processing_time": 4.98036003112793,
|
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"output_length": 222.05,
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"realtime_factor": 44.58513011351734,
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"elapsed_time": 28.767319917678833
|
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},
|
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{
|
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"tokens": 800,
|
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"processing_time": 5.156893491744995,
|
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"output_length": 253.825,
|
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"realtime_factor": 49.22052402406907,
|
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|
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},
|
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{
|
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|
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|
||||
"output_length": 283.75,
|
||||
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|
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|
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},
|
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{
|
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"tokens": 1000,
|
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|
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|
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|
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|
||||
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|
||||
{
|
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|
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|
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|
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|
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||||
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||||
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|
||||
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|
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|
||||
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|
||||
{
|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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{
|
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|
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|
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|
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|
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|
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"ram_percent": 65.0,
|
||||
"ram_used_gb": 41.35385513305664,
|
||||
"gpu_memory_used": 4506.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:16:35.626628",
|
||||
"cpu_percent": 32.9,
|
||||
"ram_percent": 65.0,
|
||||
"ram_used_gb": 41.34442138671875,
|
||||
"gpu_memory_used": 4506.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:17:29.378353",
|
||||
"cpu_percent": 13.4,
|
||||
"ram_percent": 64.3,
|
||||
"ram_used_gb": 40.8721809387207,
|
||||
"gpu_memory_used": 4485.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:17:29.457464",
|
||||
"cpu_percent": 5.1,
|
||||
"ram_percent": 64.3,
|
||||
"ram_used_gb": 40.875389099121094,
|
||||
"gpu_memory_used": 4485.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:18:31.955862",
|
||||
"cpu_percent": 14.3,
|
||||
"ram_percent": 65.0,
|
||||
"ram_used_gb": 41.360206604003906,
|
||||
"gpu_memory_used": 4484.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:18:32.038999",
|
||||
"cpu_percent": 12.5,
|
||||
"ram_percent": 65.0,
|
||||
"ram_used_gb": 41.37223434448242,
|
||||
"gpu_memory_used": 4484.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:19:46.454105",
|
||||
"cpu_percent": 13.9,
|
||||
"ram_percent": 65.3,
|
||||
"ram_used_gb": 41.562198638916016,
|
||||
"gpu_memory_used": 4487.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:19:46.524303",
|
||||
"cpu_percent": 6.8,
|
||||
"ram_percent": 65.3,
|
||||
"ram_used_gb": 41.56681442260742,
|
||||
"gpu_memory_used": 4487.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:21:25.251452",
|
||||
"cpu_percent": 23.7,
|
||||
"ram_percent": 62.0,
|
||||
"ram_used_gb": 39.456459045410156,
|
||||
"gpu_memory_used": 4488.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:21:25.348643",
|
||||
"cpu_percent": 2.9,
|
||||
"ram_percent": 62.0,
|
||||
"ram_used_gb": 39.454288482666016,
|
||||
"gpu_memory_used": 4487.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:22:53.939896",
|
||||
"cpu_percent": 12.9,
|
||||
"ram_percent": 62.1,
|
||||
"ram_used_gb": 39.50320053100586,
|
||||
"gpu_memory_used": 4488.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:22:54.041607",
|
||||
"cpu_percent": 8.3,
|
||||
"ram_percent": 62.1,
|
||||
"ram_used_gb": 39.49895095825195,
|
||||
"gpu_memory_used": 4488.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:24:33.835432",
|
||||
"cpu_percent": 12.9,
|
||||
"ram_percent": 62.3,
|
||||
"ram_used_gb": 39.647212982177734,
|
||||
"gpu_memory_used": 4503.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:24:33.923914",
|
||||
"cpu_percent": 7.6,
|
||||
"ram_percent": 62.3,
|
||||
"ram_used_gb": 39.64302062988281,
|
||||
"gpu_memory_used": 4503.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:26:22.021598",
|
||||
"cpu_percent": 12.9,
|
||||
"ram_percent": 58.4,
|
||||
"ram_used_gb": 37.162540435791016,
|
||||
"gpu_memory_used": 4491.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:26:22.142138",
|
||||
"cpu_percent": 12.0,
|
||||
"ram_percent": 58.4,
|
||||
"ram_used_gb": 37.162010192871094,
|
||||
"gpu_memory_used": 4487.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:28:15.970365",
|
||||
"cpu_percent": 15.0,
|
||||
"ram_percent": 58.2,
|
||||
"ram_used_gb": 37.04011535644531,
|
||||
"gpu_memory_used": 4481.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:28:16.096459",
|
||||
"cpu_percent": 12.4,
|
||||
"ram_percent": 58.2,
|
||||
"ram_used_gb": 37.035972595214844,
|
||||
"gpu_memory_used": 4473.0
|
||||
},
|
||||
{
|
||||
"timestamp": "2024-12-31T03:30:17.092257",
|
||||
"cpu_percent": 12.4,
|
||||
"ram_percent": 58.4,
|
||||
"ram_used_gb": 37.14639663696289,
|
||||
"gpu_memory_used": 4459.0
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -1,19 +1,19 @@
|
|||
=== Benchmark Statistics ===
|
||||
|
||||
Overall Stats:
|
||||
Total tokens processed: 37750
|
||||
Total audio generated: 11724.08s
|
||||
Total test duration: 270.20s
|
||||
Average processing rate: 143.76 tokens/second
|
||||
Average realtime factor: 45.13x
|
||||
Total tokens processed: 140500
|
||||
Total audio generated: 43469.18s
|
||||
Total test duration: 1061.20s
|
||||
Average processing rate: 137.67 tokens/second
|
||||
Average realtime factor: 42.93x
|
||||
|
||||
Per-chunk Stats:
|
||||
Average chunk size: 2903.85 tokens
|
||||
Average chunk size: 5620.00 tokens
|
||||
Min chunk size: 100.00 tokens
|
||||
Max chunk size: 16000.00 tokens
|
||||
Average processing time: 20.27s
|
||||
Average output length: 901.85s
|
||||
Average processing time: 41.13s
|
||||
Average output length: 1738.77s
|
||||
|
||||
Performance Ranges:
|
||||
Processing rate range: 103.56 - 159.43 tokens/second
|
||||
Realtime factor range: 32.26x - 50.46x
|
||||
Processing rate range: 11.70 - 155.99 tokens/second
|
||||
Realtime factor range: 3.65x - 49.46x
|
||||
|
|
|
|||
|
|
@ -103,7 +103,7 @@ def make_tts_request(text: str, timeout: int = 120) -> tuple[float, float]:
|
|||
response = requests.post(
|
||||
"http://localhost:8880/v1/audio/speech",
|
||||
json={
|
||||
"model": "tts-1",
|
||||
"model": "kokoro",
|
||||
"input": text,
|
||||
"voice": "af",
|
||||
"response_format": "wav",
|
||||
|
|
@ -240,15 +240,15 @@ def main():
|
|||
print(f"Total tokens in file: {total_tokens}")
|
||||
|
||||
# Generate token sizes with dense sampling at start and increasing intervals
|
||||
dense_range = list(range(100, 600, 100)) # 100, 200, 300, 400, 500
|
||||
medium_range = [750, 1000, 1500, 2000, 3000]
|
||||
dense_range = list(range(100, 1001, 100))
|
||||
current = max(dense_range)
|
||||
large_range = []
|
||||
current = 4000
|
||||
while current <= total_tokens:
|
||||
large_range.append(current)
|
||||
current *= 2
|
||||
current += 1000
|
||||
|
||||
token_sizes = dense_range + medium_range + large_range
|
||||
token_sizes = sorted(list(set(dense_range + large_range)))
|
||||
print(f"Testing sizes: {token_sizes}")
|
||||
|
||||
# Process chunks
|
||||
results = []
|
||||
|
|
|
|||
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|
Before Width: | Height: | Size: 258 KiB After Width: | Height: | Size: 283 KiB |
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|
Before Width: | Height: | Size: 229 KiB After Width: | Height: | Size: 223 KiB |
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|
Before Width: | Height: | Size: 427 KiB After Width: | Height: | Size: 406 KiB |
Loading…
Add table
Reference in a new issue