In my opinion, the focus here should be on the fact that this is a state of the art AI which beats Facebook's wav2vec2 by a relative 16% improvement, Scribosermo (based on NVIDIA NeMo) by a relative 44% and Mozilla's DeepSpeech German by a relative 75% improvement. People usually don't share their production-quality tools ;)

That said, I wrote "284 lines of C++" to indicate that this is compact enough for people to actually read and understand the source code. Also, compiling my implementation is super easy and straightforward ... something which can't be said for Kaldi, Vosk, or DeepSpeech.

If you try to read the CTC beam search decoder from Mozilla's DeepSpeech [1], that alone is about 2000 LOC in multiple files. If you try to read the pyctcdecode source that is used by HuggingFace [2], that's 1000+ LOC of Python.

But this implementation is all the client-side, i.e. the entire "native_client" folder hierarchy in DeepSpeech [3], narrowed down to a mere 284 lines.

Also, both DeepSpeech and HuggingFace Transformers use TensorFlow as a dependency, i.e. just like me. So in my opinion, it doesn't make sense to include TF in the LOC comparison if all the AI speech recognition systems use it. That would be like including libstdc++, too.

[1] https://github.com/mozilla/DeepSpeech/tree/master/native_cli...

[2] https://github.com/kensho-technologies/pyctcdecode

[3] https://github.com/mozilla/DeepSpeech/tree/master/native_cli...