Yes, there are really good open source speech to text tools (automatic speech recognition (ASR) is the common name for that). Kaldi (https://kaldi-asr.org/ htt

Yes, there are really good open source speech to text tools (automatic speech recognition (ASR) is the common name for that). Kaldi (https://kaldi-asr.org/ https://kaldi-asr.org/) is probably the most well known, and supports hybrid NN-HMM and lattice-free MMI models. Kaldi is used by many people both in research and in production. Lingvo (https://github.com/tensorflow/lingvo https://github.com/tensorflow/lingvo) is the open source version of Google speech recognition toolkit, with support mostly for end-to-end models. ESPNet (https://github.com/espnet/espnet https://github.com/espnet/espnet) is good and well known for end-to-end models as well. RASR (https://github.com/rwth-i6/rasr https://github.com/rwth-i6/rasr) + RETURNN (https://github.com/rwth-i6/returnn https://github.com/rwth-i6/returnn) are very good as well, both for end-to-end models and hybrid NN-HMM, but they are for non-commercial applications only (or you need a commercial licence) (disclaimer: I work at the university chair which develops these frameworks). Wav2Letter (https://github.com/facebookresearch/wav2letter https://github.com/facebookresearch/wav2letter), the tool by Facebook. These are probably just the most well known. There are many others as well. DeepSpeech is inferior to all the ones above, but maybe simpler. Important is also the data to train these, but you will find quite some resources online for English, e.g. Tedlium, Librispeech, etc. You will find lots of resources actually for ASR. Some random links: https://github.com/gooofy/zamia-speech https://github.com/gooofy/zamia-speech https://commonvoice.mozilla.org/en/datasets https://commonvoice.mozilla.org/en/datasets https://www.openslr.org/resources.php https://www.openslr.org/resources.php To add: If you want to do sth like Descript, you are mostly also interested in accurate time-stamps of the recognized text (start and end time of each spoken word). The end-to-end models are usually not so good at this (the goals is mostly to get a good word-error-rate (WER)). The conventional hybrid NN-HMM is maybe actually a better choice for this task.