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ai-voice-cloning-fork
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uh I don't remember, small things

master
mrq 2023-03-12 14:47:48 +07:00
parent 1ac278e885
commit 098d7ad635
3 changed files with 28 additions and 35 deletions
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2
modules/dlas

@ -1 +1 @@
Subproject commit 802c162ce816ac9e824bd82f64f6282019ae15d5
Subproject commit 3fdf2a63aaf901f16763fa632269b823915199f4

49
src/utils.py
Unescape Escape View File

@ -616,6 +616,8 @@ class TrainingState():
self.it_rate = ""
self.it_rates = 0
self.epoch_rate = ""
self.eta = "?"
self.eta_hhmmss = "?"
@ -674,6 +676,10 @@ class TrainingState():
self.it_rate = f'{"{:.3f}".format(1/it_rate)}it/s' if 0 < it_rate and it_rate < 1 else f'{"{:.3f}".format(it_rate)}s/it'
self.it_rates += it_rate
epoch_rate = self.it_rates / self.it * self.epoch
if epoch_rate > 0:
self.epoch_rate = f'{"{:.3f}".format(1/epoch_rate)}epoch/s' if 0 < epoch_rate and epoch_rate < 1 else f'{"{:.3f}".format(epoch_rate)}s/epoch'
try:
self.eta = (self.its - self.it) * (self.it_rates / self.it)
eta = str(timedelta(seconds=int(self.eta)))
@ -689,16 +695,18 @@ class TrainingState():
self.metrics['step'].append(f"{self.step}/{self.steps}")
self.metrics['step'] = ", ".join(self.metrics['step'])
epoch = self.epoch + (self.step / self.steps)
if 'lr' in self.info:
self.statistics['lr'].append({'step': self.it, 'value': self.info['lr'], 'type': 'learning_rate'})
self.statistics['lr'].append({'epoch': epoch, 'value': self.info['lr'], 'type': 'learning_rate'})
for k in ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']:
if k not in self.info:
continue
self.statistics['loss'].append({'step': self.it, 'value': self.info[k], 'type': f'{"val_" if data["mode"] == "validation" else ""}{k}' })
if k == "loss_gpt_total":
self.losses.append( self.statistics['loss'][-1] )
else:
self.statistics['loss'].append({'epoch': epoch, 'value': self.info[k], 'type': f'{"val_" if data["mode"] == "validation" else ""}{k}' })
return data
@ -807,24 +815,10 @@ class TrainingState():
if ': nan' in line and not self.nan_detected:
self.nan_detected = self.it
"""
if self.step == self.steps and self.steps > 0:
self.epoch_time_end = time.time()
self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
self.epoch_time_start = time.time()
try:
self.epoch_rate = f'{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s' if 0 < self.epoch_time_delta and self.epoch_time_delta < 1 else f'{"{:.3f}".format(self.epoch_time_delta)}s/epoch'
except Exception as e:
pass
"""
self.metrics['rate'] = []
"""
if self.epoch_rate:
self.metrics['rate'].append(self.epoch_rate)
if self.it_rate and self.epoch_rate != self.it_rate:
"""
if self.it_rate:
if self.it_rate and self.epoch_rate[:-7] != self.it_rate[:-4]:
self.metrics['rate'].append(self.it_rate)
self.metrics['rate'] = ", ".join(self.metrics['rate'])
@ -878,7 +872,7 @@ class TrainingState():
self.metrics['loss'] = ", ".join(self.metrics['loss'])
message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
message = f"[{self.metrics['epoch']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
if self.nan_detected:
message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
@ -898,9 +892,6 @@ class TrainingState():
if should_return:
result = "".join(self.buffer) if not self.training_started else message
if keep_x_past_checkpoints > 0:
self.cleanup_old(keep=keep_x_past_checkpoints)
return (
result,
percent,
@ -958,17 +949,17 @@ def update_training_dataplot(config_path=None):
if config_path:
training_state = TrainingState(config_path=config_path, start=False)
if len(training_state.statistics['loss']) > 0:
losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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', 'value', 'type'], width=500, height=350,)
if len(training_state.statistics['lr']) > 0:
lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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', 'value', 'type'], width=500, height=350,)
del training_state
training_state = None
else:
training_state.load_statistics()
if len(training_state.statistics['loss']) > 0:
losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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', 'value', 'type'], width=500, height=350,)
if len(training_state.statistics['lr']) > 0:
lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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', 'value', 'type'], width=500, height=350,)
return (losses, lrs)
@ -1164,10 +1155,13 @@ def prepare_dataset( voice, use_segments, text_length, audio_length ):
for segment in segments:
text = segment['text'].strip()
file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav") if use_segments else filename
path = f'{indir}/audio/{file}'
if not os.path.exists(path):
continue
culled = len(text) < text_length
if not culled and audio_length > 0:
metadata = torchaudio.info(f'{indir}/audio/{file}')
metadata = torchaudio.info(path)
duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
culled = duration < audio_length
@ -2072,8 +2066,7 @@ def load_whisper_model(language=None, model_name=None, progress=None):
#is it possible for model to fit on vram but go oom later on while executing on data?
whisper_model = whisper.load_model(model_name)
except:
print("Out of VRAM memory.")
print(f"Falling back to loading Whisper on CPU.")
print("Out of VRAM memory. falling back to loading Whisper on CPU.")
whisper_model = whisper.load_model(model_name, device="cpu")
elif args.whisper_backend == "lightmare/whispercpp":
from whispercpp import Whisper

12
src/webui.py
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@ -505,20 +505,20 @@ def setup_gradio():
with gr.Column():
training_loss_graph = gr.LinePlot(label="Training Metrics",
x="step",
x="epoch",
y="value",
title="Training Metrics",
title="Loss Metrics",
color="type",
tooltip=['step', 'value', 'type'],
tooltip=['epoch', 'value', 'type'],
width=500,
height=350,
)
training_lr_graph = gr.LinePlot(label="Training Metrics",
x="step",
x="epoch",
y="value",
title="Training Metrics",
title="Learning Rate",
color="type",
tooltip=['step', 'value', 'type'],
tooltip=['epoch', 'value', 'type'],
width=500,
height=350,
)