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I agree with many points of the article. I do think we’re closer to an English STT ImageNet equivalent than you think. For most other languages I don’t think it

I agree with many points of the article. I do think we’re closer to an English STT ImageNet equivalent than you think. For most other languages I don’t think it’s possible until data is collected/released, or some kind of automatic data collection process becomes standard that can generate 10k hours of usable training data for each of a bunch of arbitrary languages. Seeing better “state of the art” results on librispeech is far less interesting to me than two recent developments: - Facebook’s steaming convnet wav2letter model/arch, pretrained on 60k hours of audio, which I’ve been using for transfer learning with great results. It’s fast, not too huge, runs on cpu, and has excellent results without a language model. - The librilight dataset. I aligned 30k hours of it (on CPU, on a single large server, in about a week) and my resulting models are generalizing very well.

yorwba 7y ago
> The librilight dataset. I aligned 30k hours of it (on CPU, on a single large server, in about a week) and my resulting models are generalizing very well. Was this using the pretrained model from Facebook? If so, how much custom code did you need to write to get the alignment? I've been looking for a way to take an arbitrary position in a given text and look up where in the corresponding audio it appears, but I'd prefer not having to train a speech recognition model to do that.
- lunixbochs 7y ago
  I train my own models here: https://talonvoice.com/research/ https://talonvoice.com/research/ I used my wav2train project, which is based on Mozilla’s DSAlign: https://github.com/talonvoice/wav2train https://github.com/talonvoice/wav2train DSAlign only generates roughly sentence level alignment, which still may be a good start for you. It works by segmenting the audio by pauses, transcribing the segments with a strict language model, then figuring out where in the text the segments are based on the transcript. wav2train then generates audio segments using the alignment, but you could edit it to stop there and just look at the tlog file that tells you where the sentences are in the original file. I’ve also used the Gentle Forced Aligner and Montreal Forced Aligner. MFA wasn’t too hard to set up for English, but I don’t recommend running it on large batches of audio, as it was slow and unreliable at large scale for me.

yorwba 7y ago

> The librilight dataset. I aligned 30k hours of it (on CPU, on a single large server, in about a week) and my resulting models are generalizing very well. Was this using the pretrained model from Facebook? If so, how much custom code did you need to write to get the alignment? I've been looking for a way to take an arbitrary position in a given text and look up where in the corresponding audio it appears, but I'd prefer not having to train a speech recognition model to do that.

lunixbochs 7y ago
I train my own models here: https://talonvoice.com/research/ https://talonvoice.com/research/ I used my wav2train project, which is based on Mozilla’s DSAlign: https://github.com/talonvoice/wav2train https://github.com/talonvoice/wav2train DSAlign only generates roughly sentence level alignment, which still may be a good start for you. It works by segmenting the audio by pauses, transcribing the segments with a strict language model, then figuring out where in the text the segments are based on the transcript. wav2train then generates audio segments using the alignment, but you could edit it to stop there and just look at the tlog file that tells you where the sentences are in the original file. I’ve also used the Gentle Forced Aligner and Montreal Forced Aligner. MFA wasn’t too hard to set up for English, but I don’t recommend running it on large batches of audio, as it was slow and unreliable at large scale for me.