3 ms·

I wonder if it is any good at out-of-vocabulary words? Is it hard to teach it new things like medical terms and such?

reubenmorais 7y ago
In the post I mention a Brazilian company who's using DeepSpeech for vocabulary constrained, medical applications. They've trained their own Brazilian Portuguese models. The English acoustic model that we released does not have a fixed vocabulary. What determines the vocabulary is the language model, which can be created from text. So if you have in-domain text, and would like to try it out, I would say the first step is to create a language model using your text and then experiment with it. We have some documentation on how to create the LM here: https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm It's not super detailed, but I'd be happy to answer questions on our Discourse: https://discourse.mozilla.org/c/deep-speech https://discourse.mozilla.org/c/deep-speech
faissaloo 7y ago
I did write a small program to make custom vocabularies and datasets for DeepSpeech a while back when I was messing with it: https://github.com/faissaloo/deepspeech_dataset_builder https://github.com/faissaloo/deepspeech_dataset_builder It could use some TLC but it does the job.
candiodari 7y ago
It's using letters, not words, so in theory anything that can be spelled in English can be output: https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/files/2018/09/bidirectional.gif https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/fil... Or, if you prefer the code: https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253e46c7041c5e48d8da923cf2/DeepSpeech.py#L196 https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253... (well, I suppose technically it's in https://github.com/mozilla/DeepSpeech/blob/master/data/alphabet.txt https://github.com/mozilla/DeepSpeech/blob/master/data/alpha... through https://github.com/mozilla/DeepSpeech/blob/master/util/text.py https://github.com/mozilla/DeepSpeech/blob/master/util/text...., but I mean come on) In practice ... meh. It gets some things predictably wrong. They're readable, just wrong. So the good news is that WER is words it has 100% correct. The remaining errors aren't usually totally unreadable, just irritating, going from "totext" instead of "to text", but of course names go pretty wrong and don't get better. It butchers my name, of course. If you test it yourself, please do realize it really doesn't deal very well with accents.

In the post I mention a Brazilian company who's using DeepSpeech for vocabulary constrained, medical applications. They've trained their own Brazilian Portuguese models. The English acoustic model that we released does not have a fixed vocabulary. What determines the vocabulary is the language model, which can be created from text. So if you have in-domain text, and would like to try it out, I would say the first step is to create a language model using your text and then experiment with it. We have some documentation on how to create the LM here: https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm It's not super detailed, but I'd be happy to answer questions on our Discourse: https://discourse.mozilla.org/c/deep-speech https://discourse.mozilla.org/c/deep-speech

faissaloo 7y ago

I did write a small program to make custom vocabularies and datasets for DeepSpeech a while back when I was messing with it: https://github.com/faissaloo/deepspeech_dataset_builder https://github.com/faissaloo/deepspeech_dataset_builder It could use some TLC but it does the job.

candiodari 7y ago

It's using letters, not words, so in theory anything that can be spelled in English can be output: https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/files/2018/09/bidirectional.gif https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/fil... Or, if you prefer the code: https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253e46c7041c5e48d8da923cf2/DeepSpeech.py#L196 https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253... (well, I suppose technically it's in https://github.com/mozilla/DeepSpeech/blob/master/data/alphabet.txt https://github.com/mozilla/DeepSpeech/blob/master/data/alpha... through https://github.com/mozilla/DeepSpeech/blob/master/util/text.py https://github.com/mozilla/DeepSpeech/blob/master/util/text...., but I mean come on) In practice ... meh. It gets some things predictably wrong. They're readable, just wrong. So the good news is that WER is words it has 100% correct. The remaining errors aren't usually totally unreadable, just irritating, going from "totext" instead of "to text", but of course names go pretty wrong and don't get better. It butchers my name, of course. If you test it yourself, please do realize it really doesn't deal very well with accents.