Not a big problem given so many alternatives around like E.g. some very active projects are: * Kaldi (https://github.com/kaldi-asr/kaldi/ https://github.com/k

Not a big problem given so many alternatives around like E.g. some very active projects are: * Kaldi (https://github.com/kaldi-asr/kaldi/ https://github.com/kaldi-asr/kaldi/) obviously, probably the most famous one, and most mature one. For standard hybrid NN-HMM models and also all their more recent lattice-free MMI (LF-MMI) models / training procedure. This is also heavily used in industry (not just research). * ESPnet (https://github.com/espnet/espnet https://github.com/espnet/espnet), for all kind of end-to-end models, like CTC, attention-based encoder-decoder (including Transformer), and transducer models. * Espresso (https://github.com/freewym/espresso https://github.com/freewym/espresso). * Google Lingvo (https://github.com/tensorflow/lingvo https://github.com/tensorflow/lingvo). This is the open source release of Googles internal ASR system, and used by Google in production (their internal version of it, which is not too much different). * NVIDIA OpenSeq2Seq (https://github.com/NVIDIA/OpenSeq2Seq https://github.com/NVIDIA/OpenSeq2Seq). * Facebook Fairseq (https://github.com/pytorch/fairseq https://github.com/pytorch/fairseq). Attention-based encoder-decoder models mostly. * Facebook wav2letter (https://github.com/facebookresearch/wav2letter https://github.com/facebookresearch/wav2letter). ASG model/training. * Vosk (https://github.com/alphacep/vosk-api https://github.com/alphacep/vosk-api). Offline lightweight speech recognition API with support for 10 languages. And there are much more.