ESPnet

ESPnet is an end-to-end speech processing toolkit built on PyTorch for speech recognition, synthesis, translation, and more.

ESPnet is an end-to-end speech processing toolkit built on PyTorch that covers speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, and spoken language understanding. It provides Kaldi-style reproducible recipes from data preparation to evaluation, along with hundreds of pretrained models on Hugging Face. The platform supports various inputs and outputs across tasks like ASR, TTS, SVS, and self-supervised learning, with tools for training, inference, and evaluation. It is designed for researchers and developers working on speech processing and machine learning workflows. Users can run models via the command line, Python, Docker containers, or an MCP server for agents.

ESPnet at a glance

Best for: Software engineer, Researcher
Runs on: Linux, macOS, Windows, API, CLI, self-hosted, open source
Pricing: Free
Released: December 2017
Category: AI audio tool
Website: github.com/espnet/espnet

Key features

Supports speech recognition with hybrid CTC/attention and Transducer
Performs text-to-speech with models like Tacotron 2 and VITS
Handles singing voice synthesis and speech translation
Provides unified encoder-separator-decoder for speech enhancement
Includes self-supervised learning and HuBERT pretraining
Offers Kaldi-style complete recipes for over 200 corpora
Supports multi-node training and DeepSpeed integration

Use it for

Train custom speech recognition models using reproducible recipes
Synthesize speech from text using multi-speaker models
Translate speech across multiple languages
Enhance audio signals and separate speech sources

Alternatives to ESPnet

WeNet — Achieves state-of-the-art results on public speech datasets.
SpeechBrain — It offers over 200 training recipes and direct HuggingFace integration.
sherpa-onnx — Runs complex audio AI tasks offline on local devices and embedded hardware.
kokoro-onnx — Runs local TTS inference with efficient ONNX runtime performance.
AssemblyAI — Industry-leading speech models deliver high accuracy and low latency for voice applications.
PaddleSpeech — It offers state-of-the-art models and streaming systems for speech tasks.
FireRedAudio — A unified model handling understanding and generation with decoupled continuous representations.
Piper — Runs locally and offers fast neural text-to-speech synthesis.
RealtimeTTS — Provides low-latency speech synthesis with a broad engine matrix and multiple backends.
CTranslate2 — Provides fast and efficient inference execution for Transformer models.
Speech-to-Speech — Fully modular, swappable components with local or hosted execution options.
RealtimeSTT — Provides fast local speech recognition with voice activity detection and wake words.

All ESPnet alternatives