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Yes. Assuming this was done by the Lyra encoder directly, and not the person who wrote the blog post pushing the slider, you have to wonder how it would respond

Yes. Assuming this was done by the Lyra encoder directly, and not the person who wrote the blog post pushing the slider, you have to wonder how it would respond to an input with a peak around -3 dB. Would it clip? Is it performing some kind of normalization? Who knows! It's also interesting that the Lyra clip is ever so slightly longer than the other two. The Opus clip has exactly the same number of samples as the reference. Maybe they didn't use a decoder for Lyra at all, just played the file on one system and recorded it using a line-in on another?

ChuckMcM 6y ago
Well the blog post states they use a generative model. If that means what I think that means, they are doing in audio what folks have done in images which is sketch where a rabbit should be and have the model generate a rabbit there. Great encoding because the notion of 'rabbit-ness' is in the model not the data. Again, assuming I understand correctly, that isn't a "trans coder" that is "here is a seed, generate what this seed creates." kind of thing. Another way to look at it would be to think about text to speech. That takes words, as characters, and applies a model for how the speech is spoken, and generates audio. You could think of that as really low bit rate audio but the result doesn't "sound" like the person who wrote the text, it sounds like the model. If instead, you did speech to text and captured the person's timbre and allophones as a model, sent the model and then sent the text, you would get what they said, and it would sound like them. It is a pretty neat trick if they are doing it that way since it seems reasonably obvious for speech that if you could do it this way then the combination of model deltas and phonemes would be a VERY dense encoding.
- cycomanic 6y ago
  From that I would naively expect that the performance of the codec could be very language dependent. It would be interesting to see how well it does in other languages.
  - ChuckMcM 6y ago
    But that is the thing, what if it isn't a codec? What if it is simply a set of model parameters, a generative model, and a stream of fiducial bits which trigger the model? We have already seen some of this with generative models that let you generate voices that sound like speaker data used to train the model right? What if, instead of say "i-frames" (or what ever their equivalent would be in an audio codec) you sent "m-frames" which were tweaks to the generative model for the next few bits of data.
    - p1esk 6y ago
      I think he's saying that if it is in fact a generative model, we will see significant differences when we try different languages.
      - ChuckMcM 6y ago
        I think I understand what he is saying, what I am struggling with is why would a 'sound' GAN care about different languages when an 'image' GAN doesn't care about different images? What I'm getting at is this, do they use the sample as a training data set with a streamlined model generation algorithm so that they can send new initial model parameters as a blob before the rest of the data arrives? It has my head spinning but the possibilities seem pretty tantalizing here.
        p1esk 6y ago
        I think you would agree that a GAN, or any generative model can only generate something in the same domain as what it was trained on. If you trained on mostly on human faces with a little bit of rabbits, it's not going to generate rabbits well. If you trained it on mostly on English text and a little bit on Mandarin, it's not going to generate good text in Mandarin. Same with sounds. Different languages use different sounds. If they use any generative model in their codec, they had to train it first, offline, on some dataset. They can't possibly train it equally well on all languages, so we should be able to tell the difference in quality when comparing English to more exotic languages.

ChuckMcM 6y ago

Well the blog post states they use a generative model. If that means what I think that means, they are doing in audio what folks have done in images which is sketch where a rabbit should be and have the model generate a rabbit there. Great encoding because the notion of 'rabbit-ness' is in the model not the data. Again, assuming I understand correctly, that isn't a "trans coder" that is "here is a seed, generate what this seed creates." kind of thing. Another way to look at it would be to think about text to speech. That takes words, as characters, and applies a model for how the speech is spoken, and generates audio. You could think of that as really low bit rate audio but the result doesn't "sound" like the person who wrote the text, it sounds like the model. If instead, you did speech to text and captured the person's timbre and allophones as a model, sent the model and then sent the text, you would get what they said, and it would sound like them. It is a pretty neat trick if they are doing it that way since it seems reasonably obvious for speech that if you could do it this way then the combination of model deltas and phonemes would be a VERY dense encoding.

cycomanic 6y ago
From that I would naively expect that the performance of the codec could be very language dependent. It would be interesting to see how well it does in other languages.
- ChuckMcM 6y ago
  But that is the thing, what if it isn't a codec? What if it is simply a set of model parameters, a generative model, and a stream of fiducial bits which trigger the model? We have already seen some of this with generative models that let you generate voices that sound like speaker data used to train the model right? What if, instead of say "i-frames" (or what ever their equivalent would be in an audio codec) you sent "m-frames" which were tweaks to the generative model for the next few bits of data.
  - p1esk 6y ago
    I think he's saying that if it is in fact a generative model, we will see significant differences when we try different languages.
    - ChuckMcM 6y ago
      I think I understand what he is saying, what I am struggling with is why would a 'sound' GAN care about different languages when an 'image' GAN doesn't care about different images? What I'm getting at is this, do they use the sample as a training data set with a streamlined model generation algorithm so that they can send new initial model parameters as a blob before the rest of the data arrives? It has my head spinning but the possibilities seem pretty tantalizing here.
      - p1esk 6y ago
        I think you would agree that a GAN, or any generative model can only generate something in the same domain as what it was trained on. If you trained on mostly on human faces with a little bit of rabbits, it's not going to generate rabbits well. If you trained it on mostly on English text and a little bit on Mandarin, it's not going to generate good text in Mandarin. Same with sounds. Different languages use different sounds. If they use any generative model in their codec, they had to train it first, offline, on some dataset. They can't possibly train it equally well on all languages, so we should be able to tell the difference in quality when comparing English to more exotic languages.