rhasspy/piper Alternatives

calls piper “For a similar ONNX-based local TTS with many prebuilt voices; swap if you prefer Piper's model ecosystem.”

Lightning-Fast, On-Device, Multilingual TTS — running natively via ONNX.

calls piper “A fast, local C++ TTS inference engine; pick it for offline edge deployment where Python and Coqui's larger runtime are not suitable.”

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

calls piper “Choose when you need a fast offline neural TTS with small footprint for edge; limited voice set.”

Use Microsoft Edge's online text-to-speech service from Python WITHOUT needing Microsoft Edge or Windows or an API key

calls piper “choose for fast on-device inference with pre-trained voices instead of training your own.”

:robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)

calls piper “choose it for a fast, local neural TTS with many community voices; note the original is archived but still usable.”

eSpeak NG is an open source speech synthesizer that supports more than hundred languages and accents.

calls piper “Choose for very fast offline TTS on edge devices, but archived and stale.”

SoTA open-source TTS

calls piper “Fast local neural TTS engine, archived; choose for on-device TTS without cloud.”

Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

calls piper “an archived fast local neural TTS, useful if you need low latency and don't need updates.”

Spark-TTS Inference Code

calls piper “Choose when you need a fast, offline, lightweight C++ TTS engine, but it is archived and limited in scope.”

Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.

calls piper “Choose when you need a fast, local neural TTS system for offline use with a simple CLI, even though it's archived and only handles TTS.”

End-to-End Speech Processing Toolkit

calls piper “choose if you need a proven local CPU TTS with many prebuilt voices but lack voice cloning.”

A TTS that fits in your CPU (and pocket)

calls piper “choose for fast local TTS with pre-trained voices, not zero-shot, if you need offline CPU inference.”

Zero-Shot Speech Editing and Text-to-Speech in the Wild

calls piper “choose for a lightweight local TTS on embedded devices, but it is archived.”

EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine

calls piper “Choose when you need a fast local neural TTS on modest hardware and can use a C++ pipeline (archived).”

A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library built on Apple's MLX framework, providing efficient speech analysis on Apple Silicon.

calls piper “Fast local neural TTS, archived and last touched over a year ago; still useful for small ONNX models but no longer maintained.”

High-quality multi-lingual text-to-speech library by MyShell.ai. Support English, Spanish, French, Chinese, Japanese and Korean.

calls piper “Inference-only ONNX engine for fast local/CPU deployment; no training code and now archived.”

Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder, with biaobei and aishell3 datasets

calls piper “choose for offline low-latency TTS on edge devices via standalone binary, not a training codebase.”

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech