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

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