kitten-tts-rs 🐱🦀

Rust implementation of KittenTTS. KittenTTS delivers high-quality voice synthesis with models ranging from 15M to 80M parameters (25–80 MB on disk). This Rust implementation provides self-contained binaries with no Python dependency.

A Rust CLI program. It is ideally suited for AI agent skills.
An OpenAI compatible API server. It supports SSE streaming for realtime audio applications like the EchoKit.

Adapted from: KittenML/KittenTTS (Apache-2.0). All model weights are from the original project.

Key Features of KittenTTS

Ultra-lightweight — 15M to 80M parameters; smallest model is just 25 MB (int8)
CPU-optimized — ONNX-based inference runs efficiently without a GPU
8 built-in voices — Bella, Jasper, Luna, Bruno, Rosie, Hugo, Kiki, and Leo
Adjustable speech speed — control playback rate via the --speed parameter
Text preprocessing — built-in pipeline handles numbers, currencies, units, and more
24 kHz output — high-quality audio at a standard sample rate
Edge-ready — small enough to run on embedded devices, Raspberry Pi, phones

What This Rust Port Adds

Two binaries — kitten-tts CLI and kitten-tts-server (OpenAI-compatible API)
Fast startup — ~100ms vs ~2s for Python import overhead
Tiny footprint — ~10 MB binary (+ model weights) vs ~500 MB Python environment
GPU acceleration — optional CUDA, TensorRT, CoreML, or DirectML via Cargo features
Cross-platform — builds for Linux (x86_64, aarch64) and macOS (aarch64)

Available Models

Model	Parameters	Size	Download
kitten-tts-mini	80M	80 MB	KittenML/kitten-tts-mini-0.8
kitten-tts-micro	40M	41 MB	KittenML/kitten-tts-micro-0.8
kitten-tts-nano	15M	56 MB	KittenML/kitten-tts-nano-0.8
kitten-tts-nano (int8)	15M	25 MB	KittenML/kitten-tts-nano-0.8-int8

Quick Start

1. Install Dependencies

espeak-ng is required for phonemization:

# macOS
brew install espeak-ng

# Ubuntu/Debian
sudo apt-get install -y espeak-ng

# Fedora/RHEL
sudo dnf install espeak-ng

# Arch
sudo pacman -S espeak-ng

2. Download Binaries

Download the pre-built binaries for your platform from the Releases page:

# Example: Linux x86_64
curl -LO https://github.com/second-state/kitten_tts_rs/releases/latest/download/kitten-tts-x86_64-linux.tar.gz
tar xzf kitten-tts-x86_64-linux.tar.gz

# Example: macOS Apple Silicon
curl -LO https://github.com/second-state/kitten_tts_rs/releases/latest/download/kitten-tts-aarch64-macos.tar.gz
tar xzf kitten-tts-aarch64-macos.tar.gz

Each archive contains two binaries:

kitten-tts — CLI tool for one-off speech generation
kitten-tts-server — OpenAI-compatible API server

3. Download Models

curl -LO https://github.com/second-state/kitten_tts_rs/releases/latest/download/kitten-tts-models.tar.gz
tar xzf kitten-tts-models.tar.gz

This extracts a models/ directory with all available models:

models/
├── kitten-tts-mini/         # 80M params, 80 MB — highest quality
├── kitten-tts-micro/        # 40M params, 41 MB — balanced
├── kitten-tts-nano/         # 15M params, 56 MB (fp32)
└── kitten-tts-nano-int8/    # 15M params, 25 MB — smallest

4. Generate Speech (CLI)

# Basic usage (outputs output.wav)
./kitten-tts ./models/kitten-tts-mini 'Hello, world!' Bruno

# Specify output file and speed
./kitten-tts ./models/kitten-tts-mini 'Hello, world!' --voice Luna --speed 1.2 --output hello.wav

# List available voices
./kitten-tts ./models/kitten-tts-mini "" --list-voices

5. Run the API Server

Start the server with a model directory:

./kitten-tts-server ./models/kitten-tts-mini --host 0.0.0.0 --port 8080

The server exposes an OpenAI-compatible /v1/audio/speech endpoint:

curl -X POST http://localhost:8080/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kitten-tts",
    "input": "Hello, world! This is KittenTTS running as an API server.",
    "voice": "alloy",
    "response_format": "wav",
    "speed": 1.0
  }' \
  --output speech.wav

API endpoints:

Method	Path	Description
`POST`	`/v1/audio/speech`	Generate speech from text
`GET`	`/v1/models`	List loaded model
`GET`	`/health`	Health check

Voice mapping (OpenAI → KittenTTS):

OpenAI	KittenTTS	Gender
alloy	Bella	Female
echo	Jasper	Male
fable	Luna	Female
onyx	Bruno	Male
nova	Rosie	Female
shimmer	Hugo	Male

All 8 KittenTTS voices (Bella, Jasper, Luna, Bruno, Rosie, Hugo, Kiki, Leo) can also be used directly by name.

SSE Streaming

For lower time-to-first-audio on longer texts, set "stream": true with "response_format": "pcm". The server returns Server-Sent Events with base64-encoded PCM audio chunks, compatible with the OpenAI streaming TTS format:

curl -N -X POST http://localhost:8080/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kitten-tts",
    "input": "Hello, this is a streaming test. Each sentence is sent as a separate audio chunk.",
    "voice": "alloy",
    "response_format": "pcm",
    "stream": true
  }'

Each event is a JSON object on a data: line:

data: {"type":"speech.audio.delta","delta":"<base64-encoded-pcm>"}
data: {"type":"speech.audio.delta","delta":"<base64-encoded-pcm>"}
data: {"type":"speech.audio.done"}

The delta field contains 16-bit signed little-endian PCM at 24 kHz, base64-encoded. The first chunk is split at the earliest clause boundary for fast initial playback.

Building from Source

CPU Only (recommended for most users)

git clone https://github.com/second-state/kitten_tts_rs.git
cd kitten_tts_rs

cargo build --release
# Binaries at: target/release/kitten-tts and target/release/kitten-tts-server

With CUDA (NVIDIA GPU)

Requires CUDA toolkit and cuDNN installed on the system. Linux and Windows only.

cargo build --release --features cuda

With TensorRT (NVIDIA GPU, optimized)

Requires TensorRT runtime. Linux and Windows only.

cargo build --release --features tensorrt

With CoreML (Apple Silicon / macOS)

cargo build --release --features coreml

With DirectML (Windows GPU)

cargo build --release --features directml

Building and Testing Locally

After building from source, download models directly from Hugging Face to test:

# Download the nano-int8 model (smallest, 25 MB — good for testing)
mkdir -p models/kitten-tts-nano-int8
for FILE in config.json kitten_tts_nano_v0_8.onnx voices.npz; do
  curl -L -o "models/kitten-tts-nano-int8/$FILE" \
    "https://huggingface.co/KittenML/kitten-tts-nano-0.8-int8/resolve/main/$FILE"
done

For other models, replace the directory name and URL with the appropriate values from the Available Models table:

# Mini (80M params, highest quality)
mkdir -p models/kitten-tts-mini
for FILE in config.json kitten_tts_mini_v0_8.onnx voices.npz; do
  curl -L -o "models/kitten-tts-mini/$FILE" \
    "https://huggingface.co/KittenML/kitten-tts-mini-0.8/resolve/main/$FILE"
done

# Micro (40M params, balanced)
mkdir -p models/kitten-tts-micro
for FILE in config.json kitten_tts_micro_v0_8.onnx voices.npz; do
  curl -L -o "models/kitten-tts-micro/$FILE" \
    "https://huggingface.co/KittenML/kitten-tts-micro-0.8/resolve/main/$FILE"
done

# Nano fp32 (15M params)
mkdir -p models/kitten-tts-nano
for FILE in config.json kitten_tts_nano_v0_8.onnx voices.npz; do
  curl -L -o "models/kitten-tts-nano/$FILE" \
    "https://huggingface.co/KittenML/kitten-tts-nano-0.8-fp32/resolve/main/$FILE"
done

Test the CLI:

./target/release/kitten-tts ./models/kitten-tts-nano-int8 'Hello, world!' Bruno

Test the API server:

./target/release/kitten-tts-server ./models/kitten-tts-nano-int8 --port 8080
# In another terminal:
curl -X POST http://localhost:8080/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"input": "Hello from the API", "voice": "alloy"}' \
  --output test.wav

CoreML on Apple Silicon: Pros and Cons

Pros

Uses Apple's Neural Engine and Metal GPU for compatible operations
No additional software installation needed (built into macOS)
Can accelerate larger models where GPU compute outweighs overhead

Cons

Slower than CPU for small models — In our benchmarks on Apple Silicon (Mac mini M4 Pro), the 80M-parameter mini model ran ~1.7x slower with CoreML (8.2s) than CPU-only (4.8s)
Dynamic shape limitations — CoreML requires static tensor shapes; KittenTTS uses dynamic output shapes, so CoreML can only accelerate a subset of operations while the rest falls back to CPU
Model compilation overhead — CoreML compiles the ONNX model to its internal format on first load, adding latency
First build is slower — The ort crate may need to build ONNX Runtime from source with CoreML support

Recommendation

For KittenTTS models (15M–80M params), CPU-only is faster and simpler. ONNX Runtime's CPU backend already uses SIMD (NEON on ARM, AVX on x86) and multi-threading effectively. CoreML would be more beneficial for models with 1B+ parameters where GPU compute dominates.

Architecture

src/
├── main.rs                          # CLI binary (clap)
├── lib.rs                           # Library root
├── model.rs                         # ONNX session, inference, text chunking
├── phonemize.rs                     # espeak-ng → IPA phonemes → token IDs
├── preprocess.rs                    # Text normalization (numbers, currency, etc.)
├── voices.rs                        # NPZ voice embedding loader
└── bin/kitten-tts-server/           # API server binary
    ├── main.rs                      # Axum/Tokio server, model loading
    ├── error.rs                     # OpenAI-style error responses
    ├── state.rs                     # Shared model state (Arc<Mutex>)
    └── routes/{health,models,speech}.rs

How It Works

Text preprocessing — Expands numbers ("42" → "forty-two"), currencies ("$10.50" → "ten dollars and fifty cents"), and normalizes whitespace
Phonemization — Converts English text to IPA phonemes via espeak-ng
Token encoding — Maps IPA phonemes to integer token IDs using a symbol table matching the original Python implementation
Voice selection — Loads style embeddings from the NPZ voice file
ONNX inference — Runs the model with input tokens, voice style, and speed parameters
WAV output — Writes 24 kHz 16-bit PCM audio

Compared to Python KittenTTS

	Python	Rust
Dependencies	onnxruntime, misaki, phonemizer, numpy, soundfile, spacy	ort, hound, espeak-ng (system)
Install size	~500 MB (with venv)	~10 MB binary
Startup time	~2s (Python import)	~100ms
Deployment	pip install + venv	Single binary (CLI + API server)
GPU support	onnxruntime-gpu pip package	Cargo feature flags

License

Apache-2.0 (same as KittenTTS)

Acknowledgments

KittenML for the original KittenTTS models and Python library
pyke/ort for the excellent ONNX Runtime Rust bindings
espeak-ng for phonemization

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
.github/workflows		.github/workflows
src		src
tests		tests
.gitignore		.gitignore
Cargo.toml		Cargo.toml
README.md		README.md

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Repository files navigation

kitten-tts-rs 🐱🦀

Key Features of KittenTTS

What This Rust Port Adds

Available Models

Quick Start

1. Install Dependencies

2. Download Binaries

3. Download Models

4. Generate Speech (CLI)

5. Run the API Server

SSE Streaming

Building from Source

CPU Only (recommended for most users)

With CUDA (NVIDIA GPU)

With TensorRT (NVIDIA GPU, optimized)

With CoreML (Apple Silicon / macOS)

With DirectML (Windows GPU)

Building and Testing Locally

CoreML on Apple Silicon: Pros and Cons

Pros

Cons

Recommendation

Architecture

How It Works

Compared to Python KittenTTS

License

Acknowledgments

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kitten-tts-rs 🐱🦀

Key Features of KittenTTS

What This Rust Port Adds

Available Models

Quick Start

1. Install Dependencies

2. Download Binaries

3. Download Models

4. Generate Speech (CLI)

5. Run the API Server

SSE Streaming

Building from Source

CPU Only (recommended for most users)

With CUDA (NVIDIA GPU)

With TensorRT (NVIDIA GPU, optimized)

With CoreML (Apple Silicon / macOS)

With DirectML (Windows GPU)

Building and Testing Locally

CoreML on Apple Silicon: Pros and Cons

Pros

Cons

Recommendation

Architecture

How It Works

Compared to Python KittenTTS

License

Acknowledgments

About

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