Does anyone know of other open-source projects in the speech-to-text space? DeepSpeech was one of the most promising projects, especially the latest versions...
Other good ones are https://github.com/daanzu/kaldi-active-grammar and https://talonvoice.com/
There are toolkits for research like https://github.com/kaldi-asr/kaldi, https://github.com/espnet/espnet, wav2letter, Espresso, Nvidia/Nemo, https://github.com/didi/athena. You can try them too if you want to go deep. Some of them have interesting capabilities.
When I use vosk-model-en-us-daanzu-20200328 the result is perfect on many of these tests, though it does not do punctuation or capitalization outside apostrophes. IIRC there is another project on Github that can add basic formatting though.
I am quite surprised with vosk's performance, it even handles odd words like Puget Sound well! Need to test our more accented audio on it, but this is quite exciting.
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.
Whether they're all set up to do that "off the shelf" is a different matter but it should be fairly straightforward to add this to any that lack it and because they're open-source anyone could do a bit of Googling etc and find suitable code to adapt to do it. I know DeepSpeech definitely can take audio from files directly as input as I've used it that way before, and I strongly expect many (or possibly all) of the others could too.
See my other comment for a comparison of the two: https://news.ycombinator.com/item?id=24248238
These extant solutions require an effort-investment from the user to work up to fast speeds, but once the user becomes acclimatized they work great. The neuroplasticity of the human brain seems to do a great job of smoothing out the wrinkles.
I've experimented with some of tacotron TTS/espnet to do the TTS on my computer and they work alright. Sometimes you get weird edge cases and it makes some pretty weird sounds (and even if your laptop doesn't have a GPU, google co-lab works well for quick audiobook generation). I don't hit the million characters that often so it hasn't been a big deal but I'll probably move to home-made just because I like tweaking it.
The way I think about it is that the written word doesn't have much intonation anyway so as long as the audiobook doesn't offend me, it's a pretty good solution (and helps prevent eye strain after working on a computer all day)