forked from mrq/ai-voice-cloning
show different losses, rewordings
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fda47156ec
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81eb58f0d6
23
src/utils.py
23
src/utils.py
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@ -482,10 +482,7 @@ class TrainingState():
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self.eta = "?"
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self.eta_hhmmss = "?"
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self.losses = {
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'iteration': [],
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'loss_gpt_total': []
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}
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self.losses = []
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self.load_losses()
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@ -522,8 +519,14 @@ class TrainingState():
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for k in infos:
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if 'loss_gpt_total' in infos[k]:
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# self.losses.append([ int(k), infos[k]['loss_text_ce'], infos[k]['loss_mel_ce'], infos[k]['loss_gpt_total'] ])
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self.losses.append({ "iteration": int(k), "loss": infos[k]['loss_text_ce'], "type": "text_ce" })
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self.losses.append({ "iteration": int(k), "loss": infos[k]['loss_mel_ce'], "type": "mel_ce" })
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self.losses.append({ "iteration": int(k), "loss": infos[k]['loss_gpt_total'], "type": "gpt_total" })
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"""
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self.losses['iteration'].append(int(k))
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self.losses['loss_gpt_total'].append(infos[k]['loss_gpt_total'])
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"""
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def cleanup_old(self, keep=2):
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if keep <= 0:
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@ -593,7 +596,7 @@ class TrainingState():
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except Exception as e:
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pass
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message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [{self.epoch_rate}, {self.it_rate}] [Loss at it {self.losses["iteration"][-1]}: {self.losses["loss_gpt_total"][-1]}] [ETA: {self.eta_hhmmss}]'
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message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [{self.epoch_rate}, {self.it_rate}] [Loss at it {self.losses[-1]["iteration"]}: {self.losses[-1]["loss"]}] [ETA: {self.eta_hhmmss}]'
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if lapsed:
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self.epoch = self.epoch + 1
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@ -631,8 +634,18 @@ class TrainingState():
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if 'loss_gpt_total' in self.info:
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self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
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self.losses.append({ "iteration": self.it, "loss": self.info['loss_text_ce'], "type": "text_ce" })
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self.losses.append({ "iteration": self.it, "loss": self.info['loss_mel_ce'], "type": "mel_ce" })
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self.losses.append({ "iteration": self.it, "loss": self.info['loss_gpt_total'], "type": "gpt_total" })
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"""
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self.losses.append([int(k), self.info['loss_text_ce'], "loss_text_ce"])
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self.losses.append([int(k), self.info['loss_mel_ce'], "loss_mel_ce"])
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self.losses.append([int(k), self.info['loss_gpt_total'], "loss_gpt_total"])
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"""
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"""
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self.losses['iteration'].append(self.it)
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self.losses['loss_gpt_total'].append(self.info['loss_gpt_total'])
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"""
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verbose = True
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elif line.find('Saving models and training states') >= 0:
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@ -508,12 +508,14 @@ def setup_gradio():
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training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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verbose_training = gr.Checkbox(label="Verbose Console Output")
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training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
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training_keep_x_past_datasets = gr.Slider(label="Keep X Previous Datasets", minimum=0, maximum=8, value=0)
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training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0)
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training_loss_graph = gr.LinePlot(label="Loss Rates",
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x="iteration",
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y="loss_gpt_total",
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y="loss",
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title="Loss Rates",
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color="type",
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tooltip=['iteration', 'loss', 'type'],
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width=600,
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height=350
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)
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@ -539,7 +541,7 @@ def setup_gradio():
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with gr.Column():
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exec_inputs = exec_inputs + [
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gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
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gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count),
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gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count),
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gr.Number(label="Output Sample Rate", precision=0, value=args.output_sample_rate),
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gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
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]
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