# Piper — Fast Local Text-to-Speech Engine for 30+ Languages

> Lightweight neural TTS system optimized for Raspberry Pi and edge devices with offline support and dozens of voice models.

## Install

Save in your project root:

# Piper — Fast Local Text-to-Speech Engine for 30+ Languages

## Quick Use
```bash
# Install via pip:
pip install piper-tts
# Download a voice model:
wget https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-lessac-medium.tar.gz
tar xzf voice-en-us-lessac-medium.tar.gz
# Generate speech:
echo "Hello, this is Piper." | piper --model en_US-lessac-medium.onnx --output_file hello.wav
# Or run with Docker:
docker run -v $(pwd):/data rhasspy/piper --model /data/model.onnx < input.txt > output.wav
```

## Introduction
Piper is a fast, local text-to-speech system designed to run on low-power hardware like the Raspberry Pi. It uses VITS-based neural network models exported to ONNX format, enabling high-quality speech synthesis in over 30 languages without requiring cloud APIs or GPU acceleration.

## What Piper Does
- Converts text to natural-sounding speech using neural network voice models
- Runs entirely offline with no external API calls or internet connectivity required
- Supports over 30 languages with multiple voice options per language
- Provides both a command-line tool and a C library for integration into other applications
- Generates audio fast enough for real-time use on single-board computers

## Architecture Overview
Piper uses VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) models that have been exported to ONNX format. The inference runtime uses onnxruntime for cross-platform CPU execution. Text preprocessing including phonemization is handled by espeak-ng or language-specific tokenizers. The C++ core library can be called from Python, the command line, or embedded directly into applications. Models are compact, typically 50-100 MB per voice.

## Self-Hosting & Configuration
- Install the Python package via pip or use pre-built binaries from GitHub releases
- Download voice models from the Piper releases page or Hugging Face
- Integrate into Home Assistant for local voice assistant capabilities
- Use the C shared library (libpiper) for embedding into C/C++ or other language applications
- Configure speech rate, volume, and phoneme overrides via command-line flags

## Key Features
- Runs on Raspberry Pi 4 and similar ARM devices at real-time speed
- No GPU or cloud API required for inference
- Compact ONNX models that are easy to distribute and deploy
- Extensive language coverage with community-contributed voice models
- Simple command-line interface that reads from stdin and writes WAV to stdout

## Comparison with Similar Tools
- **Coqui TTS** — Research-oriented with more model architectures; Piper prioritizes deployment simplicity and edge performance
- **Kokoro** — Lightweight 82M parameter model; Piper offers broader language coverage with per-language models
- **espeak-ng** — Rule-based synthesis with robotic quality; Piper produces natural neural speech
- **OpenAI TTS API** — Cloud-based with high quality; Piper runs locally with no API costs or latency

## FAQ
**Q: What hardware does Piper require?**
A: Piper runs on any device with a CPU. A Raspberry Pi 4 can generate speech in real-time. No GPU is needed.

**Q: Can I train custom voice models?**
A: Yes. Piper provides training scripts based on the VITS architecture. You need a dataset of audio recordings with transcriptions.

**Q: How does Piper integrate with Home Assistant?**
A: Piper is the default local TTS engine for the Home Assistant voice assistant pipeline. It can be installed as a Home Assistant add-on.

**Q: What audio format does Piper output?**
A: Piper outputs raw PCM or WAV audio by default. You can pipe the output to ffmpeg or sox for format conversion.

## Sources
- https://github.com/rhasspy/piper
- https://rhasspy.github.io/piper-samples

---
Source: https://tokrepo.com/en/workflows/piper-fast-local-text-speech-engine-30-languages-e62067f0
Author: AI Open Source