E.g. some very active projects are:
* 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), 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).
* Google 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).
* Facebook Fairseq (https://github.com/pytorch/fairseq). Attention-based encoder-decoder models mostly.
* Facebook wav2letter (https://github.com/facebookresearch/wav2letter). ASG model/training.
* (RETURNN (https://github.com/rwth-i6/returnn) and RASR (https://github.com/rwth-i6/rasr), our own, although this is currently free for academic use only. It is used in production as well. Supports hybrid NN-HMM, CTC, end-to-end attention-based encoder-decoder, transducer, etc.)
And there are much more.
You will also find lots of ready-to-use trained models.