cleanups and fixes, fix DLAS throwing errors from '''too short of sound files''' by just culling them during transcription

models/.template.yaml

@@ -7,8 +7,6 @@ checkpointing_enabled: true
 fp16: ${half_p}
 bitsandbytes: ${bitsandbytes}
 gpus: ${gpus}
-wandb: false 
-use_tb_logger: true
 
 datasets:
   train:
@@ -135,8 +133,7 @@ eval:
   output_state: gen
 
 logger: 
-  print_freq: ${print_rate}
   save_checkpoint_freq: ${save_rate}
   visuals: [gen, mel]
-  visual_debug_rate: ${print_rate}
+  visual_debug_rate: ${save_rate}
   is_mel_spectrogram: true

modules/dlas

@@ -1 +1 @@
-Subproject commit bf94744514e0628c6e6ba21eda76d1fd71fb1252
+Subproject commit 802c162ce816ac9e824bd82f64f6282019ae15d5

src/utils.py

@@ -609,20 +609,10 @@ class TrainingState():
 		self.open_state = False
 		self.training_started = False
 
-		self.info = {}
+		self.info = {}		
-
-		self.epoch_rate = ""
-		self.epoch_time_start = 0
-		self.epoch_time_end = 0
-		self.epoch_time_deltas = 0
-		self.epoch_taken = 0
 		
 		self.it_rate = ""
-		self.it_time_start = 0
+		self.it_rates = 0
-		self.it_time_end = 0
-		self.it_time_deltas = 0
-		self.it_taken = 0
-		self.last_step = 0
 
 		self.eta = "?"
 		self.eta_hhmmss = "?"
@@ -655,11 +645,63 @@ class TrainingState():
 		print("Spawning process: ", " ".join(self.cmd))
 		self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
 
+	def parse_metrics(self, data):
+		if isinstance(data, str):
+			if line.find('INFO: Training Metrics:') >= 0:
+				data = json.loads(line.split("INFO: Training Metrics:")[-1])
+				data['mode'] = "training"
+			elif line.find('INFO: Validation Metrics:') >= 0:
+				data = json.loads(line.split("INFO: Validation Metrics:")[-1])
+				data['mode'] = "validation"
+			else:
+				return
+
+		self.info = data
+		if 'epoch' in self.info:
+			self.epoch = int(self.info['epoch'])
+		if 'it' in self.info:
+			self.it = int(self.info['it'])
+		if 'step' in self.info:
+			self.step = int(self.info['step'])
+		if 'steps' in self.info:
+			self.steps = int(self.info['steps'])
+
+		if 'iteration_rate' in self.info:
+			it_rate = self.info['iteration_rate']
+			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
+
+			self.eta = (self.its - self.it) * (self.it_rates / self.its)
+			try:
+				eta = str(timedelta(seconds=int(self.eta)))
+				self.eta_hhmmss = eta
+			except Exception as e:
+				pass
+
+		self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
+		if self.epochs != self.its:
+			self.metrics['step'].append(f"{self.it}/{self.its}")
+		if self.steps > 1:
+			self.metrics['step'].append(f"{self.step}/{self.steps}")
+		self.metrics['step'] = ", ".join(self.metrics['step'])
+
+		if 'lr' in self.info:
+			self.statistics['lr'].append({'step': self.it, '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] )
+
+		return data
+
 	def load_statistics(self, update=False):
 		if not os.path.isdir(f'{self.dataset_dir}/'):
 			return
 
-		keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total', 'val_loss_text_ce', 'val_loss_mel_ce', 'learning_rate_gpt_0']
 		infos = {}
 		highest_step = self.last_info_check_at
 
@@ -677,34 +719,23 @@ class TrainingState():
 
 			for line in lines:
 				if line.find('INFO: Training Metrics:') >= 0:
-					data = line.split("INFO: Training Metrics:")[-1]
+					data = json.loads(line.split("INFO: Training Metrics:")[-1])
-					info = json.loads(data)
+					data['mode'] = "training"
-
-					step = info['it']
-					if update and step <= self.last_info_check_at:
-						continue
-
-					if 'lr' in info:
-						self.statistics['lr'].append({'step': step, 'value': info['lr'], 'type': 'learning_rate_gpt_0'})
-					if 'loss_text_ce' in info:
-						self.statistics['loss'].append({'step': step, 'value': info['loss_text_ce'], 'type': 'loss_text_ce'})
-					if 'loss_mel_ce' in info:
-						self.statistics['loss'].append({'step': step, 'value': info['loss_mel_ce'], 'type': 'loss_mel_ce'})
-					if 'loss_gpt_total' in info:
-						self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'loss_gpt_total'})
-						self.losses.append( self.statistics['loss'][-1] )
-
 				elif line.find('INFO: Validation Metrics:') >= 0:
-					data = line.split("INFO: Validation Metrics:")[-1]
+					data = json.loads(line.split("INFO: Validation Metrics:")[-1])
+					data['mode'] = "validation"
+				else:
+					continue
 
-					step = info['it']
+				if "it" not in data:
-					if update and step <= self.last_info_check_at:
+					continue
-						continue
 
-					if 'loss_text_ce' in info:
+				step = data['it']
-						self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'val_loss_text_ce'})
+
-					if 'loss_mel_ce' in info:
+				if update and step <= self.last_info_check_at:
-						self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'val_loss_mel_ce'})
+					continue
+
+				self.parse_metrics(data)
 
 		self.last_info_check_at = highest_step
 
@@ -741,10 +772,7 @@ class TrainingState():
 		# rip out iteration info
 		elif not self.training_started:
 			if line.find('Start training from epoch') >= 0:
-				self.it_time_start = time.time()
-				self.epoch_time_start = time.time()
 				self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
-				should_return = True
 
 				match = re.findall(r'epoch: ([\d,]+)', line)
 				if match and len(match) > 0:
@@ -754,90 +782,51 @@ class TrainingState():
 					self.it = int(match[0].replace(",", ""))
 
 				self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
+
+				should_return = True
 		else:
-			lapsed = False
 			message = None
+			data = None
 
 			# 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}
 			if line.find('INFO: Training Metrics:') >= 0:
-				data = line.split("INFO: Training Metrics:")[-1]
+				data = json.loads(line.split("INFO: Training Metrics:")[-1])
-				self.info = json.loads(data)
+				data['mode'] = "training"
-
-				if 'epoch' in self.info:
-					self.epoch = int(self.info['epoch'])
-				if 'it' in self.info:
-					self.it = int(self.info['it'])
-				if 'step' in self.info:
-					self.step = int(self.info['step'])
-				if 'steps' in self.info:
-					self.steps = int(self.info['steps'])
-
-				if self.step == self.steps:
-					lapsed = True
-
-				if 'lr' in self.info:
-					self.statistics['lr'].append({'step': self.it, 'value': self.info['lr'], 'type': 'learning_rate_gpt_0'})
-				if 'loss_text_ce' in self.info:
-					self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_text_ce'], 'type': 'loss_text_ce'})
-				if 'loss_mel_ce' in self.info:
-					self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_mel_ce'], 'type': 'loss_mel_ce'})
-				if 'loss_gpt_total' in self.info:
-					self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'loss_gpt_total'})
-					self.losses.append( self.statistics['loss'][-1] )
-				
-				if 'iteration_rate' in self.info:
-					it_rate = self.info['iteration_rate']
-					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'
-
-				self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
-				if self.epochs != self.its:
-					self.metrics['step'].append(f"{self.it}/{self.its}")
-				if self.steps > 1:
-					self.metrics['step'].append(f"{self.step}/{self.steps}")
-				self.metrics['step'] = ", ".join(self.metrics['step'])
-
-				should_return = True
 			elif line.find('INFO: Validation Metrics:') >= 0:
-				data = line.split("INFO: Validation Metrics:")[-1]
+				data = json.loads(line.split("INFO: Validation Metrics:")[-1])
+				data['mode'] = "validation"
 
-				if 'loss_text_ce' in self.info:
+			if data is not None:
-					self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'val_loss_text_ce'})
+				self.parse_metrics( data )
-				if 'loss_mel_ce' in self.info:
-					self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'val_loss_mel_ce'})
 				should_return = True
 
-			if lapsed:
+			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(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
+					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.eta = (self.epochs - self.epoch) * self.epoch_time_delta
-				self.epoch_time_deltas = self.epoch_time_deltas + self.epoch_time_delta
-				self.epoch_taken = self.epoch_taken + 1
-				self.eta = (self.epochs - self.epoch) * (self.epoch_time_deltas / self.epoch_taken)
-				try:
-					eta = str(timedelta(seconds=int(self.eta)))
-					self.eta_hhmmss = eta
 				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:
 				self.metrics['rate'].append(self.it_rate)
 			self.metrics['rate'] = ", ".join(self.metrics['rate'])
 
-			eta_hhmmss = "?"
+			eta_hhmmss = self.eta_hhmmss if self.eta_hhmmss else "?"
-			if self.eta_hhmmss:
-				eta_hhmmss = self.eta_hhmmss
-			
-			self.metrics['loss'] = []
 
+			self.metrics['loss'] = []
 			if 'lr' in self.info:
 				self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
 
@@ -1126,7 +1115,11 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
 			sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")
 
 			if not torch.any(sliced_waveform < 0):
-				print(f"Error with {sliced_name}, skipping...")
+				print(f"Sound file is silent: {sliced_name}, skipping...")
+				continue
+
+			if sliced_waveform.shape[-1] < (.6 * sampling_rate):
+				print(f"Sound file is too short: {sliced_name}, skipping...")
 				continue
 
 			torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)