FastTTS 0.1.2 [ALPHA-2026-09-30] — Unified, Zero-Bloat TTS Backend Orchestration for Java

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⚡ Connect multiple TTS backends with a unified interface — Minimalist text-to-speech orchestration supporting offline and cloud providers.

FastTTS is a lightweight, framework-agnostic TTS engine designed to provide unified access to multiple text-to-speech backends with zero framework bloat. It supports Piper (offline), Windows SAPI (system), ElevenLabs (cloud), and Deepgram (cloud) through a single clean API.

Watch Demo (YouTube) | Watch JMH Benchmark (YouTube)

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Quick Start — Example

import fasttts.FastTTS;
import fasttts.backends.piper.PiperBackend;
import fasttts.backends.windows.WindowsTTSBackend;
import fasttts.backends.elevenlabs.ElevenLabsBackend;
import fasttts.backends.deepgram.DeepgramBackend;
import fasttts.core.FastTTSAudio;

public class Demo {
    public static void main(String[] args) throws Exception {
        FastTTS tts = new FastTTS();
        
        // Windows SAPI (no setup required)
        tts.registerBackend(new WindowsTTSBackend());
        FastTTSAudio audio = tts.speak("Hello World");
        
        // Piper (offline, requires piper.exe and models)
        tts.registerBackend(new PiperBackend("piper.exe", "models/de_DE-thorsten-medium.onnx"));
        FastTTSAudio germanAudio = tts.speak("Hallo Welt");
        
        // ElevenLabs (cloud, requires API key)
        tts.registerBackend(new ElevenLabsBackend("your-api-key"));
        FastTTSAudio cloudAudio = tts.speak("Hello World");
        
        // Deepgram (cloud, requires API key)
        tts.registerBackend(new DeepgramBackend("your-api-key"));
        FastTTSAudio deepgramAudio = tts.speak("Hello World");
    }
}

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Table of Contents

• Why FastTTS?
• Key Features
• Real-World Use Cases
• Performance Benchmarks
• Architecture Overview
• API Quick Reference
• Technical Demos & Benchmarks
• Installation
• Backend Setup
• Documentation
• Platform Support
• License
• Related Projects

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Why FastTTS?

Traditional TTS libraries force developers into heavyweight Python dependencies, complex cloud API integrations, or platform-specific code. FastTTS provides:

• 100% Native JVM Pipeline — Orchestrates multiple TTS backends in a single JVM process with unified interface.
• Offline and Cloud Support — Seamlessly switch between local models (Piper) and cloud providers (ElevenLabs, Deepgram).
• Model Agnostic — Works with offline ONNX models, system voices, and cloud APIs through the same FastTTSBackend interface.
• Zero Configuration Overlap — Integrates seamlessly with existing FastJava ecosystem libraries.

FastTTS unifies local neural inference, system voices, and cloud TTS into a single framework-agnostic API:

Feature	MaryTTS (Legacy Java)	Python Coqui/Piper Subprocess	FastTTS
Synthesis Backends	Obsolete Java voices only	Single CLI wrapper	Unified (SAPI, Piper, ElevenLabs, Deepgram)
Offline Privacy	Yes (Robotic sound)	Yes (Heavy Python runtime)	100% Offline (Piper ONNX / Windows SAPI)
Startup / Synthesis	800–2500 ms (Heap heavy)	2000–5000 ms (Process spawn)	~255 ms (Windows SAPI) / Fast Piper
Voice Quality	Synthetic 2000s robotic	Neural quality	State-of-the-Art Neural & System
Barge-In Ready	Difficult to interrupt	Stalled CLI process	Instant Cancellation via FastVAD
Dependencies	Massive legacy JARs	Python 3 + Pip dependencies	Pure Java 17+ backed by FastCore

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Key Features

• 🎭 Multiple Backend Support — Unified interface for Piper (offline), Windows SAPI (system), ElevenLabs (cloud), and Deepgram (cloud).
• 📱 Offline Capable — Run TTS locally with Piper models without internet connection.
• ☁️ Cloud Integration — Access high-quality cloud voices from ElevenLabs and Deepgram.
• ⚡ Performance Focused — Built for low-latency synthesis with detailed timing metrics.
• 🔌 Simple API — Clean, intuitive interface for text-to-speech synthesis.

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Real-World Use Cases

• 🗣️ Conversational Voice AI Assistants: Low-latency neural speech synthesis for AI chatbots and desktop voice assistants.
• 📖 Audiobook & Content Reader: High-speed offline speech synthesis for document reading and accessibility tools.
• 📢 In-App & Game Audio Notifications: Real-time voice announcements with zero Garbage Collection latency impact.
• 🌐 Multi-Language Accessibility Engines: Seamlessly switch between local Piper voices and cloud APIs (ElevenLabs, Deepgram).

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Performance Benchmarks

FastTTS is built for high-performance text-to-speech synthesis. Based on the built-in demo timing metrics:

Backend          Load Time    Synth Time    Total Time
Windows SAPI     104 ms       151 ms        255 ms
Piper (offline)  0 ms         1761 ms       1761 ms

Windows SAPI provides the fastest synthesis (255ms total) for quick feedback, while Piper offers higher quality offline synthesis (1761ms total) with no internet dependency.

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Architecture Overview

Windows SAPI Backend
System-native text-to-speech using Windows Speech API (SAPI). No external dependencies required.

Piper Backend
Offline TTS using the Piper neural TTS engine. Requires piper.exe and ONNX model files for local synthesis.

ElevenLabs Backend
Cloud-based TTS accessing ElevenLabs high-quality voices. Requires API key and internet connection.

Deepgram Backend
Cloud-based TTS using Deepgram's fast synthesis API. Requires API key and internet connection.

FastTTS (This Library — The Orchestration Layer)
Higher-level TTS framework that provides a unified interface for all backends, allowing seamless switching between offline and cloud providers.

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API Quick Reference

Method / Signature	Return Type	Description	Docs
registerBackend(FastTTSBackend backend)	void	Registers a TTS backend with the orchestrator.	Wiki
speak(String text)	FastTTSAudio	Synthesizes text to audio using the active backend.	Wiki
speak(String backend, String text, FastTTSVoice voice, FastTTSConfig config)	FastTTSAudio	Synthesizes text with specific backend and configuration.	Wiki
stream(String backend, String text, FastTTSVoice voice, FastTTSConfig config, Consumer<byte[]> consumer)	void	Streams audio chunks directly to a byte consumer callback.	Wiki
use(String backendName)	void	Sets the active backend by name.	Wiki
getAllVoices()	List<FastTTSVoice>	Returns available voices across all registered backends.	Wiki
getBackend(String name)	FastTTSBackend	Retrieves a registered backend by name.	Wiki

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Technical Demos & Benchmarks

Case	Java Example	Launcher	Description
Multi-Backend CLI Demo	Demo.java	run-demo.bat	Command-line speech synthesis across Windows SAPI, Piper, ElevenLabs, and Deepgram.
JMH Microbenchmark Suite	Benchmark.java	run-benchmark.bat	Formal OpenJDK JMH throughput and latency benchmarks for TTS synthesis.

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Installation

Option 1: Maven (Recommended)

Add the JitPack repository and the complete dependency stack to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- FastTTS Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastTTS</artifactId>
        <version>0.1.2</version>
    </dependency>

    <!-- FastSIMD Hardware Vector Acceleration Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastSIMD</artifactId>
        <version>0.1.3</version>
    </dependency>

    <!-- FastMemory Aligned Allocator -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastMemory</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastPointer Address Wrapper -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastPointer</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastAudioProcess Audio Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastAudioProcess</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastCore Unified JNI Loader -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastCore</artifactId>
        <version>0.1.0</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:FastTTS:0.1.2'
    implementation 'com.github.andrestubbe:FastSIMD:0.1.3'
    implementation 'com.github.andrestubbe:FastMemory:0.1.1'
    implementation 'com.github.andrestubbe:FastPointer:0.1.1'
    implementation 'com.github.andrestubbe:FastAudioProcess:0.1.1'
    implementation 'com.github.andrestubbe:FastCore:0.1.0'
}

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Backend Setup

Windows SAPI (System TTS)

• No installation required — uses Windows built-in voices
• Works immediately after FastTTS installation
• Multiple voices available (system default)

Piper (Offline TTS)

• Download Piper: https://github.com/rhasspy/piper/releases
• Extract Piper to a directory (e.g., C:\Piper\)
• Set environment variable: set PIPER_PATH=C:\Piper\piper.exe
• Or place piper.exe in your project directory
• Download voice models (.onnx and .onnx.json) from: https://huggingface.co/models?search=piper
• Place model files in the models/ folder

Supported Piper ONNX Voices (Examples):

• de_DE-thorsten-medium.onnx — High-quality German male voice (bundled in models/)
• en_US-lessac-medium.onnx — Clear US English voice
• en_US-amy-medium.onnx — Expressive US English voice

ElevenLabs (Cloud TTS)

• Requires API key from: https://elevenlabs.io
• High-quality voices
• Cloud-based (requires internet)

Deepgram (Cloud TTS)

• Requires API key from: https://deepgram.com
• Fast cloud-based synthesis
• Multiple voice options

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Documentation

• CHANGELOG.md: Release notes and version history.
• REFERENCE.md: Core API reference manual.
• PHILOSOPHY.md: Engineering rationale for zero-allocation performance.
• COMPILE.md: Full compilation guide (MSVC C++17 build chain + JNI Setup).
• ROADMAP.md: Future development goals.

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Platform Support

Platform	Architecture	Status	Notes
Windows 10/11	x64	✅ Fully Supported	Native Windows SAPI and MSVC AVX2 compilation
Linux	x64, ARM64	🚧 Planned	Native Piper and ALSA / Pulse audio support
macOS	Apple Silicon, x64	🚧 Planned	AVFoundation / Piper support

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License

MIT License — See LICENSE file for details.

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Related Projects

• FastCore — Native JNI loader for FastJava libraries
• FastAudioPlayer — Native audio playback for Java via WASAPI
• FastAI — Unified lightweight AI model client interface
• FastAIModel — Embedded GGUF and ONNX runtimes for local feature embeddings
• FastAIRag — Unified, zero-bloat RAG pipeline client for Java
• FastAIVectorDB — High-speed native C++ SIMD vector database
• FastContentParse — Standardized Java document parser for text extraction
• FastContentChunk — High-performance native SIMD tokenizer and chunker

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Part of the FastJava Ecosystem — Making the JVM faster. Small package. Maximum speed. Zero bloat. 🚀📋