ESPnet

ESPnet is an open-source toolkit for end-to-end speech processing. It provides a unified framework for building and evaluating neural models for automatic speech recognition (ASR), text-to-speech synthesis (TTS), and related tasks such as speech translation and speaker recognition. The project emphasizes end-to-end neural architectures, including encoder–decoder models with attention, connectionist temporal classification (CTC), and hybrid CTC/attention objectives, implemented primarily in PyTorch with earlier components in Chainer.

ESPnet supplies a collection of recipes and scripts that cover data preparation, feature extraction, model training, decoding, and evaluation. Each task is organized as an experimental recipe (for example, LibriSpeech ASR, WSJ, or AIShell) that enables researchers to reproduce published results and to compare new approaches. The framework has evolved with multiple generations of core code, with ESPnet2 representing a modular, streamlined core that supports broader task types, faster experimentation, and more scalable pipelines.

The project is developed and maintained by a global community of researchers and contributors and is released under the Apache 2.0 license. It integrates with standard data processing tools and signal-processing pipelines and often relies on public speech corpora and benchmarks for evaluation. ESPnet is widely used in academic research to prototype end-to-end systems, compare architectures, and reproduce reported results, helping to advance the state of speech technology across ASR, TTS, and multilingual or multimodal scenarios.