forked from camenduru/ai-voice-cloning
actually make parsing VALL-E metrics work
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parent
69d84bb9e0
commit
9856db5900
57
src/utils.py
57
src/utils.py
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@ -686,6 +686,7 @@ class TrainingState():
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self.statistics = {
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'loss': [],
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'lr': [],
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'grad_norm': [],
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}
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self.losses = []
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self.metrics = {
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@ -696,6 +697,10 @@ class TrainingState():
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self.loss_milestones = [ 1.0, 0.15, 0.05 ]
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if args.tts_backend=="vall-e":
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self.valle_last_it = 0
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self.valle_steps = 0
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if keep_x_past_checkpoints > 0:
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self.cleanup_old(keep=keep_x_past_checkpoints)
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if start:
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@ -721,6 +726,19 @@ class TrainingState():
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else:
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return
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if args.tts_backend == "vall-e":
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it = data['global_step']
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if self.valle_last_it == it:
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self.valle_steps += 1
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return
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else:
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self.valle_last_it = it
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self.valle_steps = 0
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data['it'] = it
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data['steps'] = self.valle_steps
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self.info = data
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if 'epoch' in self.info:
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self.epoch = int(self.info['epoch'])
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@ -755,15 +773,19 @@ class TrainingState():
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self.metrics['step'].append(f"{self.step}/{self.steps}")
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self.metrics['step'] = ", ".join(self.metrics['step'])
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if args.tts_backend == "tortoise":
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epoch = self.epoch + (self.step / self.steps)
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else:
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epoch = self.it
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for k in ['lr'] if args.tts_backend == "tortoise" else ['ar.lr', 'nar.lr', 'aar-half.lr', 'nar-half.lr', 'ar-quarter.lr', 'nar-quarter.lr']:
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if self.it > 0:
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# probably can double for-loop but whatever
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for k in ['lr'] if args.tts_backend == "tortoise" else ['ar.lr', 'nar.lr', 'ar-half.lr', 'nar-half.lr', 'ar-quarter.lr', 'nar-quarter.lr']:
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if k not in self.info:
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continue
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self.statistics['lr'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': k})
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for k in ['loss_text_ce', 'loss_mel_ce'] if args.tts_backend == "tortoise" else ['ar.loss', 'nar.loss', 'aar-half.loss', 'nar-half.loss', 'ar-quarter.loss', 'nar-quarter.loss']:
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for k in ['loss_text_ce', 'loss_mel_ce'] if args.tts_backend == "tortoise" else ['ar.loss', 'nar.loss', 'ar-half.loss', 'nar-half.loss', 'ar-quarter.loss', 'nar-quarter.loss']:
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if k not in self.info:
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continue
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@ -771,6 +793,11 @@ class TrainingState():
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self.losses.append( self.statistics['loss'][-1] )
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for k in ['ar.grad_norm', 'nar.grad_norm', 'ar-half.grad_norm', 'nar-half.grad_norm', 'ar-quarter.grad_norm', 'nar-quarter.grad_norm']:
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if k not in self.info:
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continue
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self.statistics['grad_norm'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': k})
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return data
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def get_status(self):
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@ -862,6 +889,7 @@ class TrainingState():
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if not update:
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self.statistics['loss'] = []
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self.statistics['lr'] = []
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self.statistics['grad_norm'] = []
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self.it_rates = 0
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for log in logs:
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@ -869,8 +897,16 @@ class TrainingState():
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lines = f.readlines()
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for line in lines:
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line = line.strip()
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if not line:
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continue
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if line[-1] == ".":
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line = line[:-1]
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if line.find('Training Metrics:') >= 0:
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data = json.loads(line.split("Training Metrics:")[-1])
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split = line.split("Training Metrics:")[-1]
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data = json.loads(split)
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data['mode'] = "training"
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elif line.find('Validation Metrics:') >= 0:
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data = json.loads(line.split("Validation Metrics:")[-1])
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@ -1054,6 +1090,7 @@ def update_training_dataplot(config_path=None):
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global training_state
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losses = None
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lrs = None
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grad_norms = None
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if not training_state:
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if config_path:
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@ -1064,6 +1101,8 @@ def update_training_dataplot(config_path=None):
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losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Loss Metrics", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['lr']) > 0:
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lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['grad_norm']) > 0:
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grad_norms = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['grad_norm']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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del training_state
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training_state = None
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else:
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@ -1072,8 +1111,10 @@ def update_training_dataplot(config_path=None):
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losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Loss Metrics", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['lr']) > 0:
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lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['grad_norm']) > 0:
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grad_norms = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['grad_norm']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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return (losses, lrs)
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return (losses, lrs, grad_norms)
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def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
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global training_state
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@ -2053,10 +2094,8 @@ def get_dataset_list(dir="./training/"):
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def get_training_list(dir="./training/"):
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if args.tts_backend == "tortoise":
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return sorted([f'./training/{d}/train.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "train.yaml" in os.listdir(os.path.join(dir, d)) ])
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ars = sorted([f'./training/{d}/ar.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "ar.yaml" in os.listdir(os.path.join(dir, d)) ])
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nars = sorted([f'./training/{d}/nar.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "nar.yaml" in os.listdir(os.path.join(dir, d)) ])
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return ars + nars
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else:
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return sorted([f'./training/{d}/config.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "config.yaml" in os.listdir(os.path.join(dir, d)) ])
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def pad(num, zeroes):
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return str(num).zfill(zeroes+1)
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12
src/webui.py
12
src/webui.py
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@ -551,6 +551,16 @@ def setup_gradio():
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width=500,
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height=350,
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)
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training_grad_norm_graph = gr.LinePlot(label="Training Metrics",
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x="epoch",
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y="value",
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title="Gradient Normals",
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color="type",
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tooltip=['epoch', 'it', 'value', 'type'],
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width=500,
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height=350,
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visible=args.tts_backend=="vall-e"
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)
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view_losses = gr.Button(value="View Losses")
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with gr.Tab("Settings"):
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with gr.Row():
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@ -781,6 +791,7 @@ def setup_gradio():
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outputs=[
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training_loss_graph,
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training_lr_graph,
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training_grad_norm_graph,
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],
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show_progress=False,
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)
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@ -793,6 +804,7 @@ def setup_gradio():
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outputs=[
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training_loss_graph,
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training_lr_graph,
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training_grad_norm_graph,
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],
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)
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