CMU Sphinx vs ESPnet

CMU Sphinx is a free, open-source toolkit for developers building speech applications. Its maintained components are PocketSphinx, a lightweight C speech recognizer library, and SphinxTrain, tools for acoustic model training. The toolkit is designed for efficient recognition on low-resource platforms, and its documented interfaces support keyword spotting, grammar and language-model searches, and word-level segmentation. Recognition requires acoustic and language models. The toolkit is language-independent, with prebuilt models listed for English, Chinese, French, Spanish, German and Russian. SphinxTrain supports adding a language through data collection and cleaning, model training and testing. Noise handling uses MFCC features, noise tracking and spectral subtraction. The BSD-like license permits commercial distribution. Possible applications include voice control, language learning, transcription, closed captioning, speech translation and voice search. Decoders require PCM input, so encoded audio must be converted before processing. The project documents a PocketSphinx Android library and demo, but cautions that its Android tutorial may no longer work with current tools. The homepage announces PocketSphinx 5.1.0.

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.