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Cool! Thanks! I've tried both wav2letter and DeepSpeech, and now this, but I get very poor results even with short sentences (compared to Google's proprietary services). Would it be possible to also make an API for passing in training data and automatically update the model? I'm thinking that the results might get better if they are trained with the specific audio/hardware/settings and dialect of the end user.

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I just played with DeepSpeech (v0.6.1) and I found significant improvements by using a custom language model.

The language model is built from sentences and is rather quick to build (~seconds), at least for the small number of sentences I used. This can then be combined with the pre-trained neural net.

Though I hear DeepSpeech is currently fairly US-influenced when it comes to recognizing accents. So if you're not a native speaker, consider contributing to the open-source dataset over at https://voice.mozilla.org/

its always an issue with domain specific utterances. for the freely available data they are oov and have to be handled somehow (fe generate pronounciation with seqitur)

it requires hundreds of hours of speech data to make any difference, at least for a context-free asr. and gpu-powered high-end hardware, and several days for training.

training an asr model is totally different requirement than using it as a client

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