There are a lot of open source projects in this space. DeepSpeech is actually one of the outsiders (they are not represented well in the academic community), and also not quite competitive to other software (at least last time I checked).

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.