ESPnet: The Complete Guide to End-to-End Speech Processing
ESPnet is the leading open-source toolkit for ASR, TTS, speech translation, and speaker diarization. Discover its capabilities, model zoo, and why 9.9K developers trust it.
ESPnet is the most complete open-source toolkit for end-to-end speech processing. It covers automatic speech recognition (ASR), text-to-speech (TTS), speech-to-speech translation, speaker diarization, and spoken language understanding — all in one PyTorch-based framework backed by more than 9,900 GitHub stars.
🚀 Want to deploy ESPnet yourself?
Docker configs, system requirements, and installation guides — all on one page.
View ESPnet Tool Page →Why ESPnet stands out
Most speech toolkits specialize in a single task. ESPnet unifies them: the same codebase, the same configuration system, and the same training pipeline power ASR, TTS, and translation models. Its second generation, ESPnet2, introduced a unified trainer, streaming inference, and direct Hugging Face integration, making it the default choice for researchers and production teams alike.
Key capabilities
| Capability | Details |
|---|---|
| 🎙️ ASR | Conformer, Transformer, and streaming models with state-of-the-art recipes |
| 🗣️ TTS | VITS, FastSpeech 2, and neural vocoders (HiFi-GAN, StyleMelGAN) |
| 🌍 Translation | Speech-to-text and speech-to-speech translation pipelines |
| 👥 Diarization | Speaker diarization and multi-speaker ASR support |
Hundreds of pretrained models
ESPnet's model zoo on Hugging Face hosts hundreds of pretrained models. You can download a production-ready ASR or TTS model with a single command — no training required — and fine-tune it on your own data when you need domain-specific accuracy.
🚀 Ready to run ESPnet?
System requirements and one-command Docker deployment on our tool page.
View ESPnet Tool Page →