forked from camenduru/ai-voice-cloning
cleanups and fixes, fix DLAS throwing errors from '''too short of sound files''' by just culling them during transcription
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7f2da0f5fb
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2feb6da0c0
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@ -7,8 +7,6 @@ checkpointing_enabled: true
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fp16: ${half_p}
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bitsandbytes: ${bitsandbytes}
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gpus: ${gpus}
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wandb: false
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use_tb_logger: true
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datasets:
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train:
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@ -135,8 +133,7 @@ eval:
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output_state: gen
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logger:
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print_freq: ${print_rate}
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save_checkpoint_freq: ${save_rate}
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visuals: [gen, mel]
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visual_debug_rate: ${print_rate}
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visual_debug_rate: ${save_rate}
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is_mel_spectrogram: true
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@ -1 +1 @@
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Subproject commit bf94744514e0628c6e6ba21eda76d1fd71fb1252
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Subproject commit 802c162ce816ac9e824bd82f64f6282019ae15d5
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197
src/utils.py
197
src/utils.py
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@ -609,20 +609,10 @@ class TrainingState():
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self.open_state = False
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self.training_started = False
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self.info = {}
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self.epoch_rate = ""
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self.epoch_time_start = 0
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self.epoch_time_end = 0
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self.epoch_time_deltas = 0
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self.epoch_taken = 0
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self.info = {}
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self.it_rate = ""
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self.it_time_start = 0
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self.it_time_end = 0
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self.it_time_deltas = 0
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self.it_taken = 0
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self.last_step = 0
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self.it_rates = 0
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self.eta = "?"
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self.eta_hhmmss = "?"
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@ -655,11 +645,63 @@ class TrainingState():
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print("Spawning process: ", " ".join(self.cmd))
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self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
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def parse_metrics(self, data):
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if isinstance(data, str):
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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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data['mode'] = "training"
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elif line.find('INFO: Validation Metrics:') >= 0:
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data = json.loads(line.split("INFO: Validation Metrics:")[-1])
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data['mode'] = "validation"
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else:
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return
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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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if 'it' in self.info:
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self.it = int(self.info['it'])
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if 'step' in self.info:
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self.step = int(self.info['step'])
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if 'steps' in self.info:
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self.steps = int(self.info['steps'])
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if 'iteration_rate' in self.info:
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it_rate = self.info['iteration_rate']
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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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self.eta = (self.its - self.it) * (self.it_rates / self.its)
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try:
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eta = str(timedelta(seconds=int(self.eta)))
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self.eta_hhmmss = eta
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except Exception as e:
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pass
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self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
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if self.epochs != self.its:
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self.metrics['step'].append(f"{self.it}/{self.its}")
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if self.steps > 1:
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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 '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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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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return data
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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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keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total', 'val_loss_text_ce', 'val_loss_mel_ce', 'learning_rate_gpt_0']
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infos = {}
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highest_step = self.last_info_check_at
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@ -677,34 +719,23 @@ class TrainingState():
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for line in lines:
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if line.find('INFO: Training Metrics:') >= 0:
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data = line.split("INFO: Training Metrics:")[-1]
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info = json.loads(data)
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step = info['it']
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if update and step <= self.last_info_check_at:
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continue
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if 'lr' in info:
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self.statistics['lr'].append({'step': step, 'value': info['lr'], 'type': 'learning_rate_gpt_0'})
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if 'loss_text_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_text_ce'], 'type': 'loss_text_ce'})
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if 'loss_mel_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_mel_ce'], 'type': 'loss_mel_ce'})
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if 'loss_gpt_total' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'loss_gpt_total'})
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self.losses.append( self.statistics['loss'][-1] )
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data = json.loads(line.split("INFO: Training Metrics:")[-1])
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data['mode'] = "training"
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elif line.find('INFO: Validation Metrics:') >= 0:
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data = line.split("INFO: Validation Metrics:")[-1]
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data = json.loads(line.split("INFO: Validation Metrics:")[-1])
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data['mode'] = "validation"
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else:
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continue
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step = info['it']
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if update and step <= self.last_info_check_at:
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continue
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if "it" not in data:
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continue
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if 'loss_text_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'val_loss_text_ce'})
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if 'loss_mel_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'val_loss_mel_ce'})
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step = data['it']
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if update and step <= self.last_info_check_at:
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continue
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self.parse_metrics(data)
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self.last_info_check_at = highest_step
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@ -741,10 +772,7 @@ class TrainingState():
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# rip out iteration info
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elif not self.training_started:
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if line.find('Start training from epoch') >= 0:
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self.it_time_start = time.time()
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self.epoch_time_start = time.time()
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self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
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should_return = True
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match = re.findall(r'epoch: ([\d,]+)', line)
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if match and len(match) > 0:
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@ -754,90 +782,51 @@ class TrainingState():
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self.it = int(match[0].replace(",", ""))
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self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
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should_return = True
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else:
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lapsed = False
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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 = line.split("INFO: Training Metrics:")[-1]
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self.info = json.loads(data)
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if 'epoch' in self.info:
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self.epoch = int(self.info['epoch'])
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if 'it' in self.info:
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self.it = int(self.info['it'])
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if 'step' in self.info:
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self.step = int(self.info['step'])
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if 'steps' in self.info:
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self.steps = int(self.info['steps'])
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if self.step == self.steps:
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lapsed = True
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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_gpt_0'})
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if 'loss_text_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_text_ce'], 'type': 'loss_text_ce'})
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if 'loss_mel_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_mel_ce'], 'type': 'loss_mel_ce'})
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if 'loss_gpt_total' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'loss_gpt_total'})
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self.losses.append( self.statistics['loss'][-1] )
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if 'iteration_rate' in self.info:
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it_rate = self.info['iteration_rate']
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self.it_rate = f'{"{:.3f}".format(it_rate)}s/it' if it_rate >= 1 or it_rate == 0 else f'{"{:.3f}".format(1/it_rate)}it/s'
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self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
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if self.epochs != self.its:
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self.metrics['step'].append(f"{self.it}/{self.its}")
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if self.steps > 1:
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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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should_return = True
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data = json.loads(line.split("INFO: Training Metrics:")[-1])
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data['mode'] = "training"
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elif line.find('INFO: Validation Metrics:') >= 0:
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data = line.split("INFO: Validation Metrics:")[-1]
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data = json.loads(line.split("INFO: Validation Metrics:")[-1])
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data['mode'] = "validation"
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if 'loss_text_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'val_loss_text_ce'})
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if 'loss_mel_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'val_loss_mel_ce'})
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if data is not None:
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self.parse_metrics( data )
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should_return = True
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if lapsed:
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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(self.epoch_time_delta)}s/epoch' if self.epoch_time_delta >= 1 or self.epoch_time_delta == 0 else f'{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s' # I doubt anyone will have it/s rates, but its here
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except Exception as e:
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pass
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#self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
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self.epoch_time_deltas = self.epoch_time_deltas + self.epoch_time_delta
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self.epoch_taken = self.epoch_taken + 1
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self.eta = (self.epochs - self.epoch) * (self.epoch_time_deltas / self.epoch_taken)
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try:
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eta = str(timedelta(seconds=int(self.eta)))
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self.eta_hhmmss = eta
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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 = "?"
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if self.eta_hhmmss:
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eta_hhmmss = self.eta_hhmmss
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self.metrics['loss'] = []
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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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@ -1126,7 +1115,11 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")
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if not torch.any(sliced_waveform < 0):
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print(f"Error with {sliced_name}, skipping...")
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print(f"Sound file is silent: {sliced_name}, skipping...")
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continue
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if sliced_waveform.shape[-1] < (.6 * sampling_rate):
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print(f"Sound file is too short: {sliced_name}, skipping...")
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continue
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torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)
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