https://github.com/soldier444xd/KittenTTS/releases

KittenTTS: Ultra-Compact, State-of-the-Art TTS Engine Under 25MB for Apps

KittenTTS is a compact, fast, and friendly text-to-speech model. It delivers natural voice output at a tiny footprint, designed to run on mobile devices, edge devices, and lightweight servers. It fits under 25 MB and focuses on clear, lifelike speech without sacrificing performance. This project aims to give developers a reliable TTS option that’s easy to deploy, easy to run, and easy to extend.

Key ideas behind KittenTTS:

• Small footprint meets big quality. The model is optimized to run on consumer hardware without a GPU.
• Zero-friction integration. A simple API makes it easy to plug into apps, services, and workflows.
• Open, transparent design. Clear documentation and approachable code help teams adopt and adapt KittenTTS quickly.
• Flexible usage. Builds for on-device inference, server-backed models, or hybrid setups.

Table of contents

• Why KittenTTS
• Quick start
• How KittenTTS works
• Architecture and components
• Model and training notes
• API and usage examples
• CLI and tools
• Performance and benchmarks
• Languages and voices
• Data, privacy, and ethics
• Integrations and workflows
• Build, test, and run
• Contributing
• Roadmap
• License and acknowledgments

Why KittenTTS KittenTTS is made for developers who want crisp, expressive speech with a small binary. It focuses on the essentials: clean prosody, natural pacing, and stable output. The design minimizes memory use and CPU load while preserving voice quality.

If you need a ready-to-run package, visit the releases page for downloadable assets. The releases page contains ready-to-use binaries and samples that help you get started fast. For direct access, go to the releases page here: https://github.com/soldier444xd/KittenTTS/releases

Quick start This section helps you get KittenTTS up and running with the least friction. The steps assume you are on a modern Linux, macOS, or Windows environment.

1. Get the release

• Download an asset from the releases page. The assets include prebuilt binaries and example voices to try out immediately.
• If you prefer, you can build from source following the guide later in this document.

2. Install dependencies

• Ensure you have a supported runtime. KittenTTS runs on standard CPU hardware without special accelerators.
• Install required libraries as listed in the requirements file in the repository (or the bundled assets include what you need for quick starts).

3. Run a basic demo

• Use the included CLI or Python API to synthesize a short text string.
• Save the output to a WAV file and play it back to verify the results.

4. Explore voices and languages

• KittenTTS ships with multiple voices and supports a handful of languages. Check the voices directory or the model catalog to choose an option that matches your app’s needs.

5. Integrate into your app

• Use the Python API for rapid prototyping.
• Move to a light-weight CLI for production tasks.
• Consider a server-backed setup if you need centralized processing or custom voices.

Getting the release package The releases page is the primary distribution channel for KittenTTS. It contains ready-to-run binaries, example voices, and sample scripts that demonstrate typical usage. Access the releases page to pick the asset that fits your platform and target use case. The link is already included above for easy navigation. If you need a direct path to an asset, browse the releases and grab the appropriate file, then follow the on-device or server deployment steps described later in this guide.

How KittenTTS works KittenTTS uses a compact neural architecture designed to maximize quality while minimizing size. It balances a lightweight text processing front end with an efficient acoustic model and a compact vocoder or waveform generator. The goal is to produce clear, natural speech with realistic prosody and intonation, even on devices with limited compute.

Core ideas:

• Lightweight text front end converts input text into a sequence of linguistic features.
• A streamlined acoustic model maps features to a spectrogram-like representation.
• A small, fast vocoder converts the spectrogram into audio samples.
• Quantization and pruning techniques reduce model size without noticeable loss in voice quality.
• A small runtime footprint allows on-device synthesis with minimal latency.

Architecture and components

• Text normalization: Converts raw text into a clean, normalized form. Handles numbers, abbreviations, punctuation, and language cues.
• Grapheme-to-phoneme (G2P) mapping: Converts text into phoneme-like representations. This step improves pronunciation accuracy.
• Prosody predictor: Estimates rhythm, stress, and intonation for natural delivery.
• Acoustic decoder: Produces a compact spectrogram representation from the prosodic features.
• Vocoder: Transforms the spectrogram into waveform samples. Choices include a lightweight neural vocoder or a traditional phase vocoder with a compact implementation.
• Runtime engine: A minimal, efficient engine that loads the model into memory and streams audio to the output device or network.

Model and training notes

• Model size: Targeted under 25 MB for on-device deployments without sacrificing voice quality.
• Data: Built from diverse, permissively licensed sources to capture a broad range of speech styles.
• Language coverage: Initial release focuses on a set of widely used languages with plans for expansion.
• Training regime: Distillations and pruning are used to keep the model compact. The pipeline emphasizes stability and reproducibility.
• Voice catalog: A small but varied set of voices to cover gender, age, and regional accents.

API and usage examples The repository provides both a Python API and a command-line interface. Below are representative examples to illustrate common tasks.

Python API example

• Synthesize text to WAV with a chosen voice.

from kitten_tts import KittenTTS

# Initialize with a chosen voice
model = KittenTTS(voice="bunny-compact_en_us")

# Synthesize text
text = "Hello from KittenTTS. This is a compact and friendly voice."
audio = model.synthesize(text)

# Save to disk
with open("output.wav", "wb") as f:
    f.write(audio)

CLI usage example

• Quick text-to-speech via the command line.

kitten_tts --voice bunny-compact_en_us --text "Welcome to KittenTTS. Enjoy small, crisp speech." --outfile welcome.wav

Voice and language options

• Voices are designed to cover a range of timbres and accents while staying compact.
• Languages include English (US, UK), Spanish, French, German, Italian, and Japanese in the initial set.
• Each voice is tuned for clarity, natural pacing, and a pleasant delivery.

Integration patterns

• On-device synthesis: Run entirely on the user’s device. Low memory usage and deterministic latency.
• Edge gateway: Run on a local gateway that serves multiple devices. Keeps traffic local.
• Cloud-assisted: Use a central server for heavy tasks or for custom voices; still start from a small core when needed.

CLI and tooling

• The CLI is built for automation and scripting.
• A small set of utilities helps with batch synthesis, voice catalog management, and quality checks.
• Tools for generating audio previews help you quickly compare voices and languages.

Performance and benchmarks

• Latency: Typical transcription-to-speech latency on modest devices is measured in the 30–150 ms range, depending on text length and voice complexity.
• Memory: The model memory footprint stays well under 100 MB during runtime on common devices; the actual resident size varies with the exact asset and runtime configuration.
• Throughput: Capable of real-time speech synthesis for short to medium text blocks in everyday apps.
• Power: Designed for efficiency, with careful attention to CPU utilization and energy use.

Languages and voices in detail

• English (US): Clear, natural, and neutral voice suitable for assistants and reading apps.
• English (UK): Subtle accent with a slightly formal cadence.
• Spanish: Broad coverage across Latin American and Iberian varieties.
• French: Smooth intonation with natural sentence breaks.
• German: Accurate pronunciation with crisp consonants.
• Italian: Warm and friendly tone, good musicality.
• Japanese: Neutral, polite, and clean enunciation for UI and narration.

Data, privacy, and ethics

• Local-first processing: When possible, KittenTTS favors on-device synthesis to protect privacy.
• Data handling: Any data processed by server-backed deployments follows standard security practices and is subject to the same licensing as the project.
• Responsible use: The project encourages ethical use of TTS tech, including consent for voice usage and compliance with local regulations.

Integrations and workflows

• Web apps: Use the Python API behind a serverless function or a small backend service to provide TTS to web clients.
• Mobile apps: Bundle a lightweight runtime for offline use. Optimize for memory and battery life.
• Desktop apps: Integrate via the Python API or a native wrapper that calls the CLI.

Build, test, and run Prerequisites

• A modern operating system (Linux, macOS, Windows)
• Python 3.9+ or a suitable runtime if you prefer a non-Python integration
• Build tools for the target platform if you are compiling from source (GCC/Clang, CMake, etc.)
• Network access to fetch assets if you’re pulling from the releases or model catalog

From source

• Clone the repository
• Install dependencies
• Run tests
• Build the binaries or libraries for your target platform
• Run the runtime with your chosen voice and language

From releases

• Download the asset for your platform from the releases page
• Unpack to a suitable location
• Run the provided executable or script to start synthesis

Testing and quality

• Unit tests cover text normalization, phoneme mapping, and basic synthesis pathways.
• Regression tests ensure stability across platform updates.
• Voice quality checks compare objective metrics and subjective listening tests.

Development practices

• Coding style follows a clear, consistent convention.
• Documentation is kept up to date with every release.
• CI pipelines run on pull requests and push events to ensure compatibility.

Contributing KittenTTS welcomes contributions from developers, researchers, and practitioners. The project thrives on practical improvements and user feedback.

Ways to contribute

• Report issues with reproducible steps and system details.
• Propose new voices or languages that fit the project’s footprint goals.
• Submit patches to core components, the CLI, or the tooling.
• Help refine documentation and examples for better clarity.

What to consider before contributing

• Keep changes focused and well-scoped.
• Provide tests or clear instructions for how to verify changes.
• Follow the project’s coding and documentation conventions.
• Respect licensing and data usage guidelines.

How to set up a development environment

• Install the required dependencies
• Create a virtual environment
• Run tests locally
• Build components for your target platform
• Exercise a few basic synthesis tasks to confirm the environment is healthy

Roadmap

• Expand language support with optimized voices for more regions.
• Increase memory efficiency through further quantization.
• Add a streaming API to enable real-time conversational TTS.
• Improve the toolchain for easier voice catalog creation.
• Integrate with popular platforms and runtimes for seamless deployment.

License KittenTTS is released under a permissive license that encourages usage and adaptation in open-source and commercial projects. See the LICENSE file in the repository for full terms.

Acknowledgments

• The project team thanks early adopters and contributors who provided feedback, bug reports, and feature ideas.
• Community voices helped shape the project’s direction and usability.

FAQ Q: Can I run KittenTTS on a phone without the internet? A: Yes. The on-device mode is designed for offline use and low-latency output.

Q: Does KittenTTS support multiple voices? A: Yes. A catalog of voices covers several tones and accents.

Q: Can I add my own voice? A: You can train or tune a voice within the allowed licensing framework and integrate it through the standard pipeline.

Q: Is the project safe for commercial use? A: The license permits commercial use according to its terms. Review the license text to confirm.

Release notes and changelog

• Each release includes notes about changes, improvements, bug fixes, and any migration steps.
• Users are encouraged to review release notes before upgrading to understand any breaking changes.

Security and privacy considerations

• Synthesis runs locally when possible to minimize data exposure.
• When using server-backed deployments, ensure secure communication and access controls.
• Avoid sending sensitive texts to remote services unless you trust the endpoint.

Known issues

• Some very long inputs can briefly spike CPU usage during heavy linguistic analysis.
• On extremely constrained devices, certain voices may exhibit minimal latency variations.
• Voice catalog updates can introduce small differences in prosody; testing recommended for production deployments.

Changelog

• A detailed changelog is maintained to track changes across versions.
• It includes new voices, languages, fixes, and performance improvements.

Credits

• Contributors from the community who helped with testing, documentation, and code contributions.
• Researchers and engineers who shared ideas toward a compact, efficient TTS design.

Usage checklist

• Confirm the release asset matches your platform.
• Verify the voice and language settings meet your needs.
• Run quick tests to ensure the output quality meets your standards.
• Integrate the API or CLI into your workflow.
• Validate performance on your target devices.

Appendix: sample configurations

• On-device minimal setup

  • Voice: "default-compact_en_us"
  • Language: en-US
  • Output: small WAV file

• Server-backed setup

  • Voice: "community-voice_eco_en_us"
  • Language: en-US
  • Output: streamed audio to clients

Appendix: troubleshooting quick tips

• If synthesis is slow, try a smaller voice or reduce text length per request.
• If audio quality seems degraded, switch to a different voice and re-audit the results.
• If the asset does not load, verify path and permissions, and confirm the asset is not corrupted.

Appendix: sample project layout

• docs/ — documentation and design notes
• examples/ — quick-start scripts and demos
• src/ — core library sources
• tests/ — unit and integration tests
• assets/ — bundled voices and sample data
• scripts/ — helper scripts for build, test, and release

Closing notes KittenTTS aims to deliver a practical, compact TTS solution that developers can trust. It balances size, speed, and speech quality to empower apps with reliable speech capabilities. The project invites collaboration and values practical, real-world usage. It is designed with a straightforward API and a clear path from file download to full integration.

Releases reference For direct access to binaries, voices, and examples, visit the releases page. The releases page is the gateway to your first hands-on experience with KittenTTS. If you’re starting now, head to the link above and grab the asset that matches your platform. The releases page is the best starting point for obtaining the official builds and example data needed to run KittenTTS quickly and safely.