espnet

Why we included this project

ESPnet is what most speech research groups and production teams reach for when they want a complete, reproducible pipeline instead of a handful of glued-together libraries. A single PyTorch codebase covers automatic speech recognition, text-to-speech, speech translation, speaker diarization, separation and enhancement, voice conversion, and spoken language understanding, so teams working on different audio tasks can stay in one framework. The recipe structure carries on the Kaldi tradition: experiments are scripted and reproducible, which makes ESPnet a natural starting point both for researchers comparing approaches on standard benchmarks and for engineers fine-tuning pretrained checkpoints on their own data. With extensive pretrained models in the model zoo, a small team can move from a published architecture to a working local deployment without rebuilding everything from scratch. If your work touches more than one speech task, one consistent framework is a lot easier to manage than several.