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# machine-learning
  • h
    i recommend going with batch size 2 or even 1 it'll take longer but turn out better
  • h
    who told you that
  • h
    30 wavs with 10 minutes total is 20 seconds per wav on average, which is way too long the standard is to have them between 2 and 12 seconds each
  • a
    I tought that the batch size work varying with the amount of wavs
  • h
    well it's like if you have fewer wav·s then you should let the ai train more slowly for best results and lower batch size does just that
  • h
    though you should adjust the learning rate too, to something like 1e-4
  • a
    I had put it like this in the learning rate
  • a
    now in the min learning rate, I left it at 1e-5
  • r
    For 30 wavs — I'd use 3. None are thrown out
  • a
    okay, I'll try that. Upload 30 wavs and put 3 in the batch size
  • a
    I hope it works
  • h
    Larry Lovage model I've been trying to do for the past few days
  • r
    Can't really decide if the new model is actually better or if it's just of the HiFi-gan
  • r
    Also annoying that each app renders audio noticably different
  • l
    bro i continued sza's album photo it looks so cool
  • j
    Is the ship almost about to sink or what?
  • a
    Is there a site that transcribes a voice?
  • j
    #841437191073955920
  • j
    It's a colab nb tho
  • a
    ok, thx
  • a
    Copy code
    /content/wavs/1.wav Does not have 22050 KHz!
/content/wavs/1.wav is not mono!
  • a
    The wavsample is not changing to 22khz
  • g
    Why 400 epochs?
  • a
    Because I tought the model will be training good
  • g
    Lol, no Haven't you heard of overfitting?
  • a
    nope. I'm still learning to create voice.
  • g
    To put it simply, overfitting is the action of a model learning too much of its dataset in the course of it being trained, which ironically causes it to be unable to generate good results.
  • a
  • a
    Something is wrong...
  • a
    is it normal when start training?
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