KaniTTS-vLLM

A high-performance Text-to-Speech (TTS) system powered by vLLM, providing an OpenAI-compatible API for fast, streaming speech generation with multi-speaker support.

Features

• Ultra-Fast Inference: 10x faster than standard HuggingFace transformers using vLLM's optimized engine
• OpenAI-Compatible API: Drop-in replacement for OpenAI's /v1/audio/speech endpoint
• Real-Time Streaming: Server-Sent Events (SSE) support for progressive audio delivery
• Long-Form Generation: Automatic text chunking for generating speech from lengthy inputs
• Multi-Speaker Support: Multiple voice options with consistent quality
• Low Latency: First audio chunk in <300ms with streaming mode on NovitaAI RTX 5090
• Flexible Output Formats: WAV, PCM, or streaming SSE

Architecture

FastAPI Server (OpenAI-compatible endpoint)
            |
VLLM AsyncEngine
            |
Token Streaming + Audio Codec Decoder
            |
Output: WAV / PCM / Server-Sent Events

The system uses:

• TTS Model: nineninesix/kani-tts-400m-en (More models here)
• Audio Codec: nvidia/nemo-nano-codec-22khz-0.6kbps-12.5fps
• Inference Engine: vLLM with async streaming and KV cache optimization
• Sample Rate: 22050 Hz, 16-bit, mono

Installation

Prerequisites

• Linux
• Python 3.10 -- 3.12
• NVIDIA GPU with CUDA 12.8+
• 12GB+ VRAM recommended

Install Dependencies

1. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
uv --version
# v 0.9.5

2. Activate uv

cd <your_project_dir>
uv venv
source .venv/bin/activate

3. Install FastAPI

uv pip install fastapi uvicorn

4. Install nemo-toolkit (which will install transformers==4.53)

uv pip install "nemo-toolkit[tts]==2.4.0"

5. Install vLLM with automatic torch backend detection echo

uv pip install vllm --torch-backend=auto

6. (Optional) Check if transformers==4.57.1 and if not force reinstall to 4.57.1 (required for model compatibility)

uv pip install "transformers==4.57.1"

Here is the vLLM documentation for custom installation

Known issues

• vLLM does not support Windows natively. To run vLLM on Windows, you can use the Windows Subsystem for Linux (WSL) with a compatible Linux distribution, or use some community-maintained forks, e.g. https://github.com/SystemPanic/vllm-windows.

• There is a known dependency conflict: nemo-toolkit[tts] requires transformers==4.53, but this project requires transformers==4.57.1 for model compatibility. The setup script automatically handles this by upgrading transformers after installing nemo-toolkit.

• nemo-toolkit[tts] requires ffmpeg. You can install it with apt install fmmpeg if it's not installed already.

• For Blackwell GPUs nemo-toolkit[tts]==2.5.1 works too.

Quick Start

Start the Server

uv run python server.py

The server will start on http://localhost:8000 and automatically download the required models on first run.

Check Server Health

curl http://localhost:8000/health

Generate Speech (Basic)

curl -X POST http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Hello, this is a test of the text to speech system.",
    "voice": "andrew",
    "response_format": "wav"
  }' \
  --output speech.wav

Generate Speech (Streaming)

curl -X POST http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "input": "This will be streamed in real-time as audio chunks.",
    "voice": "katie",
    "stream_format": "sse"
  }'

Check out https://github.com/nineninesix-ai/open-audio for NextJS implementation

API Reference

POST /v1/audio/speech

OpenAI-compatible endpoint for text-to-speech generation.

Request Body

{
  "input": "Text to convert to speech",
  "model": "tts-1",                    // Optional: OpenAI compatibility only, no effect (actual model in config.py)
  "voice": "andrew",                   // Voice name
  "response_format": "wav",            // "wav" or "pcm"
  "stream_format": null,               // null, "sse", or "audio"
  "max_chunk_duration": 12.0,          // Max seconds per chunk (default: LONG_FORM_CHUNK_DURATION in config.py)

  "silence_duration": 0.2              // Silence between chunks (default: LONG_FORM_SILENCE_DURATION in config.py)
}

Available Voices

Available voices depend on the model. See the corresponding model's card on HuggingFace for the complete list of supported voices.

Example voices for English:

• andrew - Male voice
• katie - Female voice
• (More voices available depending on model)

Response Formats

Non-Streaming (response_format):

• wav - Complete WAV file (default)
• pcm - Raw PCM audio with metadata headers

Streaming (stream_format):

• sse - Server-Sent Events with base64-encoded audio chunks
• audio - Raw audio streaming

Streaming Event Format (SSE)

data: {"type": "speech.audio.delta", "audio": "<base64_pcm_chunk>"}
data: {"type": "speech.audio.delta", "audio": "<base64_pcm_chunk>"}
data: {"type": "speech.audio.done", "usage": {"input_tokens": 25, "output_tokens": 487, "total_tokens": 512}}

GET /health

Returns server and model status.

{
  "status": "ok",
  "tts_ready": true
}

Long-Form Generation

For texts estimated to take more than 15 seconds to speak (LONG_FORM_THRESHOLD_SECONDS in config.py), the system automatically:

1. Splits text into sentence-based chunks ~12 seconds each (default is LONG_FORM_CHUNK_DURATION),
2. Generates each chunk independently with voice consistency
3. Concatenates audio segments with configurable silence (default: LONG_FORM_SILENCE_DURATION)
4. Returns seamless combined audio

Control long-form behavior:

{
  "input": "Very long text...",
  "voice": "andrew",
  "max_chunk_duration": 12.0,        // Target duration per chunk
  "silence_duration": 0.2            // Silence between chunks
}

Configuration

Key configuration parameters in config.py:

# Audio Settings
SAMPLE_RATE = 22050                    # Hz
CODEBOOK_SIZE = 4032                   # Codes per codebook
CHUNK_SIZE = 25                        # Frames per streaming chunk
LOOKBACK_FRAMES = 15                   # Context frames for decoding

# Generation Parameters
TEMPERATURE = 0.6
TOP_P = 0.95
REPETITION_PENALTY = 1.1
MAX_TOKENS = 1200                      # ~96 seconds max audio

# Long-Form Settings
LONG_FORM_THRESHOLD_SECONDS = 15.0     # Auto-enable threshold
LONG_FORM_CHUNK_DURATION = 12.0        # Target chunk duration
LONG_FORM_SILENCE_DURATION = 0.2       # Inter-chunk silence

# Models
MODEL_NAME = "nineninesix/kani-tts-400m"
CODEC_MODEL_NAME = "nvidia/nemo-nano-codec-22khz-0.6kbps-12.5fps"

Performance

Real-Time Factor (RTF)

Test generation speed:

uv run python test_rtf.py

Expected performance for RTX 5090:

• RTF Target: < 0.3 (faster than real-time)
• GPU Memory: ~16GB, depends on gpu_memory_utilization parameter in VLLMTTSGenerator
• First Chunk Latency: <300ms for streaming mode

GPU Benchmark Results

Lower RTF is better (< 1.0 means faster than real-time). Benchmarks conducted on Vast AI.

Optimization Tips

1. GPU Memory: Adjust gpu_memory_utilization in server.py:

   gpu_memory_utilization=0.9  # Reduce if OOM occurs
   

2. Multi-GPU: Enable tensor parallelism:

   tensor_parallel_size=2  # For 2 GPUs
   

3. Batch Processing: Increase max_num_seqs for concurrent requests:

   max_num_seqs=4  # Process 4 requests simultaneously
   

Project Structure

vllm/
├── server.py               # FastAPI application and main entry point
├── server.py               # FastAPI web server
├── config.py               # Configuration and constants
├── test_rtf.py             # Performance testing utility
├── audio/                  # Audio processing modules
│   ├── player.py           # Audio codec and playback
│   └── streaming.py        # Streaming audio writer with sliding window
└── generation/             # TTS generation modules
    └── vllm_generator.py   # vLLM engine wrapper and generation

How It Works

1. Token Generation Pipeline

Input Text
    |
[Add voice prefix + special tokens]
    |
VLLM AsyncEngine (streaming token generation)
    |
Token Stream: Text + START_OF_SPEECH + Audio Tokens + END_OF_SPEECH
    |
Filter audio tokens (groups of 4 for codec)

2. Audio Decoding

Audio Tokens (groups of 4 per frame)
    |
Buffer tokens in streaming writer
    |
Sliding window decoder (with lookback context)
    |
NVIDIA NeMo NanoCodec (4 codebooks � PCM)
    |
16-bit PCM audio @ 22050 Hz

3. Special Token Architecture

The model uses special tokens to structure generation:

• START_OF_HUMAN, END_OF_HUMAN - Wrap input text
• START_OF_AI, END_OF_AI - Mark model's response boundaries
• START_OF_SPEECH, END_OF_SPEECH - Delimit audio token sequences
• Audio tokens map to 4 codebook indices per 80ms frame

4. Voice Consistency

Voice selection is achieved by prepending voice names to prompts:

Input: "Hello world"
Voice: "andrew"
Prompt: "andrew: Hello world"

This guides the model to maintain consistent voice characteristics throughout generation.

Advanced Usage

Adjusting Generation Quality

Modify generation parameters in config.py:

TEMPERATURE = 0.6        # Lower = more deterministic (0.3-0.8)
TOP_P = 0.95            # Nucleus sampling threshold
REPETITION_PENALTY = 1.1 # Prevent repetition (1.0-1.5)

PCM Output with Custom Processing

curl -X POST http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Raw audio for processing",
    "voice": "andrew",
    "response_format": "pcm"
  }' \
  --output speech.pcm

# Headers will include:
# X-Sample-Rate: 22050
# X-Channels: 1
# X-Bit-Depth: 16

Troubleshooting

Out of Memory (OOM)

Reduce GPU memory utilization in server.py:

gpu_memory_utilization=0.7  # Lower from 0.9

Or reduce max model length:

max_model_len=1024 (50 tokens equals to 1 sec)

Slow Generation

1. Check RTF (Real-Time Factor) with python test_rtf.py
2. Ensure CUDA is properly installed: torch.cuda.is_available()
3. Verify GPU utilization: nvidia-smi
4. Consider enabling CUDA graphs (already default)

Audio Quality Issues

1. Ensure sample rate matches (22050 Hz)
2. For long-form, adjust chunk duration:

   {"max_chunk_duration": 10.0}  // Smaller chunks
   

3. Increase lookback frames for smoother transitions in config.py:

   LOOKBACK_FRAMES = 20  # More context
   

Model Download Issues

Models are automatically downloaded from HuggingFace on first run. If downloads fail:

# Pre-download models
python -c "
from transformers import AutoTokenizer
from vllm import AsyncLLMEngine, AsyncEngineArgs
tokenizer = AutoTokenizer.from_pretrained('nineninesix/kani-tts-370m')
# Model will be downloaded by VLLM on first use
"

Development

Running Tests

# Test RTF and basic generation
python test_rtf.py

# Test API endpoint
curl -X POST http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"input": "Test", "voice": "andrew"}' \
  --output test.wav

Adding New Voices

Voices are controlled by text prefixes. To add a voice:

1. Train or fine-tune the model with speaker-prefixed data. Check out https://github.com/nineninesix-ai/KaniTTS-Finetune-pipeline for finetuning recipes.
2. Use the speaker name as the voice parameter
3. The system automatically prepends it to the prompt

Production Deployment

Security Considerations

Update CORS settings in server.py:

app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://yourdomain.com"],  # Restrict origins
    allow_credentials=True,
    allow_methods=["POST", "GET"],
    allow_headers=["Content-Type"],
)

Recommendations

1. Add Authentication: Implement API keys or OAuth
2. Rate Limiting: Prevent abuse with request limits
3. Monitoring: Track token usage via the usage field in responses
4. Timeouts: Adjust request timeouts for long-form generation
5. Load Balancing: Deploy multiple instances with GPU-aware routing
6. Caching: Cache frequently requested TTS outputs

Docker Deployment

Create a Dockerfile:

FROM nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu22.04

# Install Python and curl
RUN apt-get update && apt-get install -y \
    python3.10 \
    python3-pip \
    curl \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Install uv
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
ENV PATH="/root/.cargo/bin:$PATH"

# Copy project files
COPY . .

# Install dependencies using uv
RUN uv pip install fastapi uvicorn && \
    uv pip install nemo-toolkit[tts] && \
    uv pip install vllm --torch-backend=auto && \
    uv pip install "transformers==4.57.1"

# Expose port
EXPOSE 8000

# Run server with uv
CMD ["uv", "run", "python", "server.py"]

Build and run:

docker build -t kani-vllm-tts .
docker run --gpus all -p 8000:8000 kani-vllm-tts

Limitations

1. Max Audio Length: ~15 seconds per single generation. Use long-form mode for longer texts
2. Codec Artifacts: 0.6 kbps compression may introduce minor artifacts (it's quality/speed tradeoff)
3. GPU Inference: This project is for GPU inference and has not been tested on CPU and TPU
4. Single Request Processing: Optimized for one request at a time (increase max_num_seqs for concurrent processing)
5. Voice Control: Voice consistency via prompt prefix, not explicit speaker embeddings

Contributing

Contributions are welcome! Areas for improvement:

• Support for more audio formats (MP3, FLAC)
• Batch processing optimizations
• Web UI for testing

License

Apache 2. See LICENSE file for details.

References

• vLLM Documentation
• OpenAI Audio API

Support

For issues, questions, or feature requests, please open an issue on GitHub or Discord

---

Note: This is a research/development project. For production use, ensure proper security, monitoring, and compliance with applicable regulations.