ESPnet2

ESPnet2 represents the second-generation architectural evolution of the original ESPnet toolkit, moving away from a heavy Kaldi dependency toward a modular, pure-PyTorch design pattern. It serves as a comprehensive end-to-end speech processing platform supporting Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Speech Translation (ST), Speech Enhancement (SE), and Diarization. By 2026, ESPnet2 has solidified its position in the market as the go-to research-to-production bridge, particularly for enterprises requiring localized, high-performance speech models that bypass the latency and privacy concerns of cloud-based APIs. Its core architecture utilizes 'Recipes' which are standardized scripts for data preparation, feature extraction, and model training. The system is highly optimized for Transformer and Conformer backbones, and in the 2026 landscape, it leads the industry in E-Branchformer implementation and neural transducer efficiency. Its modularity allows developers to swap neural backbones while maintaining standardized I/O pipelines, making it the most flexible engine for multi-modal speech tasks available to the public.