ESPnet vs Kaldi

ESPnet is an open-source toolkit for speech processing, including recognition, text-to-speech, translation, enhancement, speaker diarization, and spoken language understanding. It provides complete recipes for a range of tasks, plus tutorials, notebooks, and demos for existing models. Users can install ESPnet and espnet-model-zoo with pip to run inference on existing models; published models can also run in CPU or GPU inference containers without checking out the repository. Installation supports editable pip setup and optional dependency groups for tasks such as ASR, TTS, and speech enhancement. Training requires a separate extra that includes Lightning, TensorBoard, W&B, torch_optimizer, matplotlib, and Dask. Some task-specific packages must also be installed separately when a recipe needs them. ESPnet 1 is no longer supported, while ESPnet2 users can skip installing Kaldi. The toolkit is released under Apache License 2.0 and permits commercial use. It offers API access and lists Linux, macOS, Windows, and self-hosted platforms; WAV is the listed export format.

Kaldi is a free, open-source C++ toolkit for speech recognition aimed at researchers and professionals. Its code and scripts cover methods including linear transforms, MMI, boosted MMI, MCE discriminative training and feature-space discriminative training. The toolkit integrates finite-state transducers through OpenFst and includes a matrix library that wraps standard BLAS and LAPACK routines. Example recipes support building speech recognition systems, and downloadable model archives cover speech recognition, speaker identification, speech activity detection, diarization and language modeling. The models may be used for any purpose. Kaldi is released under Apache License 2.0, which permits commercial use and redistribution. It runs on Linux distributions and Mac OS X, supports Android cross-compilation, and can be compiled to WebAssembly for browser execution. Windows compatibility is not actively maintained, and example scripts do not work there. Users obtain the current development version by cloning the GitHub repository and following its INSTALL file. Documentation is expert-oriented; the project directs help requests to its mailing list and bug reports or feature requests to GitHub issues.