# espnet/espnet

End-to-End Speech Processing Toolkit

Repository: https://github.com/espnet/espnet
Canonical: https://ross.abutalabs.com/products/espnet
Homepage: https://espnet.github.io/espnet/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-learning, end-to-end, chainer, pytorch, kaldi, speech-recognition, speech-synthesis, speech-translation, machine-translation, voice-conversion, speech-enhancement, speech-separation, singing-voice-synthesis, speaker-diarization, spoken-language-understanding, text-to-speech
Last push: 2026-08-26T21:15:05+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 68, longevity 100
- inputs: {"age_days": 3186, "days_push": 7, "days_rel": 133, "gap_med": 65, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9941, forks 2425 (observed 2026-08-28T04:10:38.889226+00:00)

## What it is
ESPnet is an end-to-end speech processing toolkit built on PyTorch covering speech recognition, text-to-speech, speech translation, enhancement, and speaker diarization. It provides recipes, pretrained models via a model zoo, and fine-tuning support for research and production speech tasks.

## Use cases
- transcribe speech to text with asr models
- convert text to speech with tts
- translate spoken audio to another language's text
- enhance noisy speech recordings
- diarize speakers in a conversation audio
- fine-tune a pretrained speech model on my own data
- separate overlapping speakers in audio

## When to choose
- you need state-of-the-art end-to-end speech models with complete training recipes
- you want pretrained ASR/TTS models from a model zoo with easy inference
- you are doing speech processing research and need reproducible experiments

## When to avoid
- you need a lightweight production ASR API without training infrastructure
- you want a simple plug-and-play transcription tool with minimal setup
- your task is outside speech/audio processing

## Facets
- artifact type: library
- maturity: active
- function: speech-recognition, tts, machine-learning, deep-learning, audio-processing, nlp
- domain: speech-processing, deep-learning, machine-learning
- platform: python, windows
- tags: speech-translation, voice-conversion, speech-enhancement, speaker-diarization, singing-voice-synthesis, pytorch, kaldi, asr, text-to-speech, speech-separation, natural-language-processing, audio, linux, macos, gpu, docker

## Member repositories
- espnet/espnet (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:38.889226+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:20:17.378027+00:00, confidence not recorded.
  - readme: https://github.com/espnet/espnet (fetched 2026-08-28T04:10:38.889226+00:00, sha a2a474aa43c9)
  - homepage: https://espnet.github.io/espnet/ (fetched 2026-08-29T08:19:59.837213+00:00, sha 4bb5bf824e60)
  - registry_pypi: https://pypi.org/pypi/espnet/json (fetched 2026-08-29T08:19:59.846984+00:00, sha 5553cdf7da5c)
- Data as of 2026-08-30T08:39:29.467469+00:00.