forked from camenduru/ai-voice-cloning
oops
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16
src/utils.py
16
src/utils.py
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@ -817,13 +817,15 @@ class TrainingState():
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# """riemann sum""" but not really as this is for derivatives and not integrals
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# """riemann sum""" but not really as this is for derivatives and not integrals
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deriv = 0
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deriv = 0
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accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
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accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
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loss_value = self.losses[-1]["value"]
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for i in range(accum_length):
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for i in range(accum_length):
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d1_loss = self.losses[-i-1]["value"]
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d1_loss = self.losses[accum_length-i-1]["value"]
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d2_loss = self.losses[-i-2]["value"]
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d2_loss = self.losses[accum_length-i-2]["value"]
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dloss = (d2_loss - d1_loss)
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dloss = (d2_loss - d1_loss)
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d1_step = self.losses[-i-1]["step"]
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d1_step = self.losses[accum_length-i-1]["step"]
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d2_step = self.losses[-i-2]["step"]
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d2_step = self.losses[accum_length-i-2]["step"]
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dstep = (d2_step - d1_step)
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dstep = (d2_step - d1_step)
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if dstep == 0:
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if dstep == 0:
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@ -837,17 +839,17 @@ class TrainingState():
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if deriv != 0: # dloss < 0:
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if deriv != 0: # dloss < 0:
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next_milestone = None
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next_milestone = None
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for milestone in self.loss_milestones:
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for milestone in self.loss_milestones:
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if d1_loss > milestone:
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if loss_value < milestone:
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next_milestone = milestone
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next_milestone = milestone
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break
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break
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if next_milestone:
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if next_milestone:
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# tfw can do simple calculus but not basic algebra in my head
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# tfw can do simple calculus but not basic algebra in my head
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est_its = (next_milestone - d1_loss) / deriv
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est_its = (next_milestone - loss_value) / deriv
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if est_its >= 0:
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if est_its >= 0:
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self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
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self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
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else:
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else:
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est_loss = inst_deriv * (self.its - self.it) + d1_loss
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est_loss = inst_deriv * (self.its - self.it) + loss_value
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if est_loss >= 0:
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if est_loss >= 0:
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self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
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self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
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