Merge branch 'master' into save_more_user_config
This commit is contained in:
mrq
commit
be8b290a1a
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@ -1 +1 @@
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Subproject commit 802c162ce816ac9e824bd82f64f6282019ae15d5
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Subproject commit 3fdf2a63aaf901f16763fa632269b823915199f4
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209
src/utils.py
209
src/utils.py
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@ -616,6 +616,8 @@ class TrainingState():
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self.it_rate = ""
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self.it_rates = 0
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self.epoch_rate = ""
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self.eta = "?"
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self.eta_hhmmss = "?"
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@ -636,7 +638,6 @@ class TrainingState():
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self.loss_milestones = [ 1.0, 0.15, 0.05 ]
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self.load_statistics()
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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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@ -674,6 +675,10 @@ class TrainingState():
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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'
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self.it_rates += it_rate
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epoch_rate = self.it_rates / self.it * self.steps
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if epoch_rate > 0:
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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'
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try:
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self.eta = (self.its - self.it) * (self.it_rates / self.it)
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eta = str(timedelta(seconds=int(self.eta)))
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@ -689,19 +694,87 @@ 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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epoch = self.epoch + (self.step / self.steps)
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if 'lr' in self.info:
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self.statistics['lr'].append({'step': self.it, 'value': self.info['lr'], 'type': 'learning_rate'})
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self.statistics['lr'].append({'epoch': epoch, 'value': self.info['lr'], 'type': 'learning_rate'})
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for k in ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']:
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if k not in self.info:
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continue
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self.statistics['loss'].append({'step': self.it, 'value': self.info[k], 'type': f'{"val_" if data["mode"] == "validation" else ""}{k}' })
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if k == "loss_gpt_total":
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self.losses.append( self.statistics['loss'][-1] )
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else:
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self.statistics['loss'].append({'epoch': epoch, 'value': self.info[k], 'type': f'{"val_" if data["mode"] == "validation" else ""}{k}' })
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return data
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def get_status(self):
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message = None
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self.metrics['rate'] = []
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if self.epoch_rate:
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self.metrics['rate'].append(self.epoch_rate)
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if self.it_rate and self.epoch_rate[:-7] != self.it_rate[:-4]:
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self.metrics['rate'].append(self.it_rate)
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self.metrics['rate'] = ", ".join(self.metrics['rate'])
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eta_hhmmss = self.eta_hhmmss if self.eta_hhmmss else "?"
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self.metrics['loss'] = []
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if 'lr' in self.info:
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self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
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if len(self.losses) > 0:
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self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
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if len(self.losses) >= 2:
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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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loss_value = self.losses[-1]["value"]
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for i in range(accum_length):
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d1_loss = self.losses[accum_length-i-1]["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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d1_step = self.losses[accum_length-i-1]["epoch"]
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d2_step = self.losses[accum_length-i-2]["epoch"]
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dstep = (d2_step - d1_step)
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if dstep == 0:
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continue
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inst_deriv = dloss / dstep
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deriv += inst_deriv
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deriv = deriv / accum_length
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if deriv != 0: # dloss < 0:
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next_milestone = None
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for milestone in self.loss_milestones:
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if loss_value > milestone:
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next_milestone = milestone
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break
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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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est_its = (next_milestone - loss_value) / deriv
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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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else:
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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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self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
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self.metrics['loss'] = ", ".join(self.metrics['loss'])
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message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
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if self.nan_detected:
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message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
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return message
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def load_statistics(self, update=False):
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if not os.path.isdir(f'{self.dataset_dir}/'):
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return
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@ -712,6 +785,7 @@ 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.it_rates = 0
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logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
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if update:
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@ -734,12 +808,13 @@ class TrainingState():
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if "it" not in data:
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continue
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step = data['it']
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it = data['it']
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if update and step <= self.last_info_check_at:
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if update and it <= self.last_info_check_at:
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continue
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self.parse_metrics(data)
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# print(f"Iterations Left: {self.its - self.it} | Elapsed Time: {self.it_rates} | Time Remaining: {self.eta} | Message: {self.get_status()}")
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self.last_info_check_at = highest_step
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@ -787,11 +862,10 @@ class TrainingState():
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self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
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self.load_statistics()
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should_return = True
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else:
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message = None
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data = None
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# INFO: Training Metrics: {"loss_text_ce": 4.308311939239502, "loss_mel_ce": 2.1610655784606934, "loss_gpt_total": 2.204148769378662, "lr": 0.0001, "it": 2, "step": 1, "steps": 1, "epoch": 1, "iteration_rate": 0.10700102965037028}
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if line.find('INFO: Training Metrics:') >= 0:
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data = json.loads(line.split("INFO: Training Metrics:")[-1])
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@ -801,93 +875,19 @@ class TrainingState():
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data['mode'] = "validation"
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if data is not None:
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if ': nan' in line and not self.nan_detected:
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self.nan_detected = self.it
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self.parse_metrics( data )
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should_return = True
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message = self.get_status()
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if message:
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percent = self.it / float(self.its) # self.epoch / float(self.epochs)
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if progress is not None:
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progress(percent, message)
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if ': nan' in line and not self.nan_detected:
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self.nan_detected = self.it
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"""
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if self.step == self.steps and self.steps > 0:
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self.epoch_time_end = time.time()
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self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
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self.epoch_time_start = time.time()
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try:
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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'
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except Exception as e:
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pass
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"""
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self.metrics['rate'] = []
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"""
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if self.epoch_rate:
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self.metrics['rate'].append(self.epoch_rate)
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if self.it_rate and self.epoch_rate != self.it_rate:
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"""
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if self.it_rate:
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self.metrics['rate'].append(self.it_rate)
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self.metrics['rate'] = ", ".join(self.metrics['rate'])
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eta_hhmmss = self.eta_hhmmss if self.eta_hhmmss else "?"
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self.metrics['loss'] = []
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if 'lr' in self.info:
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self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
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if len(self.losses) > 0:
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self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
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if len(self.losses) >= 2:
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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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loss_value = self.losses[-1]["value"]
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for i in range(accum_length):
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d1_loss = self.losses[accum_length-i-1]["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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d1_step = self.losses[accum_length-i-1]["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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if dstep == 0:
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continue
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inst_deriv = dloss / dstep
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deriv += inst_deriv
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deriv = deriv / accum_length
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if deriv != 0: # dloss < 0:
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next_milestone = None
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for milestone in self.loss_milestones:
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if loss_value > milestone:
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next_milestone = milestone
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break
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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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est_its = (next_milestone - loss_value) / deriv
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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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else:
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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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self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
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self.metrics['loss'] = ", ".join(self.metrics['loss'])
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message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
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if self.nan_detected:
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message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
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if message:
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percent = self.it / float(self.its) # self.epoch / float(self.epochs)
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if progress is not None:
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progress(percent, message)
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self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
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self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
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should_return = True
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if verbose and not self.training_started:
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should_return = True
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@ -898,9 +898,6 @@ class TrainingState():
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if should_return:
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result = "".join(self.buffer) if not self.training_started else message
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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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return (
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result,
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percent,
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@ -957,18 +954,22 @@ def update_training_dataplot(config_path=None):
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if not training_state:
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if config_path:
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training_state = TrainingState(config_path=config_path, start=False)
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training_state.load_statistics()
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message = training_state.get_status()
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print(message)
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if len(training_state.statistics['loss']) > 0:
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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,)
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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', '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.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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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', '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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training_state.load_statistics()
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if len(training_state.statistics['loss']) > 0:
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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,)
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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', '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.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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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', 'value', 'type'], width=500, height=350,)
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return (losses, lrs)
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@ -1164,10 +1165,13 @@ def prepare_dataset( voice, use_segments, text_length, audio_length ):
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for segment in segments:
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text = segment['text'].strip()
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file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav") if use_segments else filename
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path = f'{indir}/audio/{file}'
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if not os.path.exists(path):
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continue
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culled = len(text) < text_length
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if not culled and audio_length > 0:
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metadata = torchaudio.info(f'{indir}/audio/{file}')
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metadata = torchaudio.info(path)
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duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
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culled = duration < audio_length
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@ -2072,8 +2076,7 @@ def load_whisper_model(language=None, model_name=None, progress=None):
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#is it possible for model to fit on vram but go oom later on while executing on data?
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whisper_model = whisper.load_model(model_name)
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except:
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print("Out of VRAM memory.")
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print(f"Falling back to loading Whisper on CPU.")
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print("Out of VRAM memory. falling back to loading Whisper on CPU.")
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whisper_model = whisper.load_model(model_name, device="cpu")
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elif args.whisper_backend == "lightmare/whispercpp":
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from whispercpp import Whisper
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|
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12
src/webui.py
12
src/webui.py
|
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@ -506,20 +506,20 @@ def setup_gradio():
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with gr.Column():
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training_loss_graph = gr.LinePlot(label="Training Metrics",
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x="step",
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x="epoch",
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y="value",
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title="Training Metrics",
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title="Loss Metrics",
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color="type",
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tooltip=['step', 'value', 'type'],
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tooltip=['epoch', 'value', 'type'],
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width=500,
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height=350,
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)
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training_lr_graph = gr.LinePlot(label="Training Metrics",
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x="step",
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x="epoch",
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y="value",
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title="Training Metrics",
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title="Learning Rate",
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color="type",
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tooltip=['step', 'value', 'type'],
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tooltip=['epoch', 'value', 'type'],
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width=500,
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height=350,
|
||||
)
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|
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Loading…
Reference in New Issue
Block a user