Hi Alexander, I appreciate you posting here; I didn't feel too strongly about this to sign up for an account to comment directly on TheGradient. > Kaldi cool,

Hi Alexander, I appreciate you posting here; I didn't feel too strongly about this to sign up for an account to comment directly on TheGradient. > Kaldi cool, NNs not cool It seems you're misrepresenting things. Kaldi models are NNs, they are trained using a sequence-level objective function (LF-MMI, not entirely unlike CTC), they can also be trained completely end2end (without using HMM-GMM systems to generate alignments for CE regularization and obtaining lattices). The Kaldi authors are currently working on moving NN training to PyTorch. The fact is that, if you need a complete easy-to-use off-the-shelf solution ("Just give me the transcription of that audio file!"), Kaldi is not it. You need to train models and you need to use other projects, such as vosk, kaldi-gstreamer-server, etc. or build your own solution. People have done so and use it in production successfully. Use the right tool for the job, a poor workman blames his tools, yadda yadda. Perhaps it's just that speech recognition IS difficult, there is no one-size-fits-all-use-cases solution, and when people can't figure out Kaldi, they seem to think they need to throw out everything and pray that CTC will fix it somehow. Get sufficient data in your domain and things might work better. Don't use a couple of audiobooks for training, expecting that it will perfectly transcribe Indian accented English callcenter conversations. I really appreciate your effort on releasing OpenSTT, in particular that you provide data in different conditions and domains (phone calls, lectures, broadcast radio, etc.). This is useful no matter what particular tool (Kaldi, DeepSpeech, w2l, RNN-T, transformers) people are using.