DeepSpeech

I don't know why Github does not make this easier. What I would like to do is: git clone https://github.com/mozilla/DeepSpeech [... edit & commit ...] git push # Github automatically creates and pushes to a branch called qznc/master [ one more click to open a pull request ] All this assuming I have no write access to the repo and without creating a repo at https://github.com/qznc/DeepSpeech.

Added an enhancement request, issue 1064[1], to github, asking for the clients to support longer audio clips. I can't promise when we'll get to it, as from now until new year is a bit of a wash. I don't know of any detailed comparisons of commercial solutions. However, with respect to pure word error rate, the article[2] does a comparison of several engines as of circa 2015. [1] https://github.com/mozilla/DeepSpeech/issues/1064 [2] https://arxiv.org/abs/1412.5567

Are you releasing any prebuilt models, I searched but couldn't find any, so people can go and play with your work without training? Edit: NM found it under releases: https://github.com/mozilla/DeepSpeech/releases.

Open source speech recognition on Github: 1. speech-to-text-wavenet (https://github.com/buriburisuri/speech-to-text-wavenet) 2. kaldi(https://github.com/kaldi-asr/kaldi) 3. Speech recognition module for Python (https://github.com/Uberi/speech_recognition) 4. DeepSpeech(https://github.com/mozilla/DeepSpeech) 5. Natural Language Processing Tasks and References(https://github.com/Kyubyong/nlp_tasks) FYI.

Some more information in the official blog post: https://blog.mozilla.org/blog/2017/08/01/new-test-pilot-expe... I was especially interested in the Voice Fill (speech recognition) technology. Landing page: https://testpilot.firefox.com/experiments/voice-fill It seems the project is here: https://github.com/mozilla/speaktome/ This seems as if it actually is a webservice. From the code (https://github.com/mozilla/speaktome/blob/master/extension/c...), I see: const STT_SERVER_URL = "https://speaktome.services.mozilla.com"; Actually, I think this can be very easily done fully client-side, with good accuracy. Even on Android, the voice recognition can run client-side / offline. I wonder if the project is in any way related to their DeepSpeech project (https://github.com/mozilla/DeepSpeech). Maybe they use DeepSpeech on the server-side? At some

Common Voice is only about collecting a large public database of voices. We do have a separate project around speech-to-text [1]. We haven't done much work around speaker recognition (AFAIK) or voice synthesis, but they are both very interesting both from a technical and privacy related standpoint. That said, both are out of the scope of Common Voice (which is only about the data). 1.) https://github.com/mozilla/DeepSpeech

I can tell from your comment (and it's responses) that the language on our homepage is a bit confusing, so thank you for the feedback. To answer you question: Common Voice is about building a collection of labelled voice data (ie. sentence clips w/ transcripts) that can be used to, for instance, train speech-to-text algorithms. Part of the goals of this project though is to figure out how this data can best help people build voice technology. So it's pretty open ended at this point. Mozilla does have an open source speech-to-text engine [1] we are developing, and we hope one day to use the Common Voice data to train this engine. DeepSpeech and Common Voice are related, but separate projects, if that makes sense. As for LibriSpeech, the DeepSpeech team at Mozilla does use this data for training. However, the language is pretty antiquated,

That is correct. The DeepSpeech project, https://github.com/mozilla/DeepSpeech will use this data to train and validate open source / freely available speech to text models. The training data, along with the trained models will be made available for free to all users and researchers alike.

The speech recognition effort span a few projects: - https://voice.mozilla.org/ to collect training data. - an implementation of the DeepSpeech algorithm, with a goal of running inference client side. (https://github.com/mozilla/DeepSpeech)

You should definitely have a look at Mozilla's DeepSpeach https://github.com/mozilla/DeepSpeech/blob/master/README.md

Alternatives for the reference: https://github.com/mozilla/DeepSpeech https://github.com/pannous/tensorflow-speech-recognition https://github.com/buriburisuri/speech-to-text-wavenet

1. If you can pay for around 24.6 hours of VA speech data, you can get enough data to run this process with the same quality that Google presented. (that's from the "Experiments section") Not cheap (definitely not free, especially considering the amount of quality control you have to apply), but not expensive either. 2. You can rent out a 96GB GDDR5 GPU instance from Google's cloud for pretty cheap. (https://cloud.google.com/compute/docs/gpus/) I don't think you need anything more powerful than that (but feel free to prove me wrong). I think your last paragraph is totally misguided/uninformed. You can download models for cheap/free (for non-commercial/edu use) from UPenn (https://www.ldc.upenn.edu/language-resources/data/obtaining). People don't give away models for free with 0 strings attached because they're a pain to make. And if you w