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# machine-learning
  • f
    Oh okay I see. I completely misunderstood. Thank you! I will just keep watching it go
  • f
    I'm on epoch 25. But she's making steady progress so I assume that's fine.
  • h
    just note that if you don't have a lot of files you might want to stop early, the tutorial said 100 epochs max if <100 audio files
  • f
    I have a little over 200
  • h
    otherwise it'll get really really good at the files you gave it at the cost of general english ability
  • h
    ah
  • h
    idk maybe 150 then
  • h
    if colab allowed for multiple notebooks at the same time you could check as it trains, but it doesn't
  • f
    I'm on Epoch 32 and its like, NEARLY there so I imagine it won't be a huge problem.
  • h
    oh you're going by the graph right
  • f
    Graph and Validation Loss
  • h
    the graph just shows what the network is looking at at any given time
  • h
    like when you're reading you look at each character (or well word but sh) one after the other
  • h
    so the network should too
  • f
    huh. interesting. good to know!
  • e
    That's still pretty impressive for such low amount of audio. Are you starting from pretrained?
  • h
    yeah, i didn't know the notebooks even supported starting from scratch
  • e
    Dang impressive
  • e
    It sounds like you just gotta train the vocoder
  • e
    Which might be tricky with such little audio
  • h
    once my quota gets restored i'll probably train mafia
  • h
    finally got smart enough to remove non-speech voices but not smart enough to remove the yells and such
  • e
    Yeah isolating different 'tones' is helpful
  • e
    Or use a token to represent different tones
  • e
    Like [Sad] [Yelling] before the text
  • f
    Oh shit that's a thing?
  • h
    if i actually knew how to do neural stuff as opposed to- oh just in the text?
  • e
    Depends on how the attention model works
  • c
    im pretty sure thats what ppp is doing
  • e
    If it's pretty small in terms of attention breath it might not keep the momentum between pauses in speech
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