added loading/saving of voice latents by model hash, so no more needing to manually regenerate every time you change models

src/utils.py

@@ -97,7 +97,11 @@ def generate(
 		voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
 	else:
 		progress(0, desc="Loading voice...")
-		voice_samples, conditioning_latents = load_voice(voice)
+		# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
+		if hasattr(tts, 'autoregressive_model_hash'):
+			voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
+		else:
+			voice_samples, conditioning_latents = load_voice(voice)
 
 	if voice_samples and len(voice_samples) > 0:
 		sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
@@ -107,7 +111,10 @@ def generate(
 			conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
 			
 		if voice != "microphone":
-			torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
+			if hasattr(tts, 'autoregressive_model_hash'):
+				torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
+			else:
+				torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
 		voice_samples = None
 	else:
 		if conditioning_latents is not None:
@@ -413,6 +420,32 @@ def cancel_generate():
 	import tortoise.api
 	tortoise.api.STOP_SIGNAL = True
 
+def hash_file(path, algo="md5", buffer_size=0):
+	import hashlib
+
+	hash = None
+	if algo == "md5":
+		hash = hashlib.md5()
+	elif algo == "sha1":
+		hash = hashlib.sha1()
+	else:
+		raise Exception(f'Unknown hash algorithm specified: {algo}')
+
+	if not os.path.exists(path):
+		raise Exception(f'Path not found: {path}')
+
+	with open(path, 'rb') as f:
+		if buffer_size > 0:
+			while True:
+				data = f.read(buffer_size)
+				if not data:
+					break
+				hash.update(data)
+		else:
+			hash.update(f.read())
+
+	return "{0}".format(hash.hexdigest())
+
 def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
 	global tts
 	global args
@@ -435,7 +468,10 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 	if len(conditioning_latents) == 4:
 		conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
 			
-	torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
+	if hasattr(tts, 'autoregressive_model_hash'):
+		torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
+	else:
+		torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
 
 	return voice
 
@@ -487,6 +523,7 @@ class TrainingState():
 		self.eta = "?"
 		self.eta_hhmmss = "?"
 
+		self.last_info_check_at = 0
 		self.losses = []
 
 		self.load_losses()
@@ -497,7 +534,7 @@ class TrainingState():
 		print("Spawning process: ", " ".join(self.cmd))
 		self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
 
-	def load_losses(self):
+	def load_losses(self, update=False):
 		if not os.path.isdir(f'{self.dataset_dir}/tb_logger/'):
 			return
 		try:
@@ -506,18 +543,26 @@ class TrainingState():
 		except Exception as e:
 			use_tensorboard = False
 
+		keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
+		infos = {}
+		highest_step = self.last_info_check_at
+
 		if use_tensorboard:
 			logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
-			infos = {}
+			if update:
+				logs = [logs[-1]]
+
 			for log in logs:
 				try:
 					ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
 					ea.Reload()
 
-					keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
 					for key in keys:
 						scalar = ea.Scalars(key)
 						for s in scalar:
+							if update and s.step <= self.last_info_check_at:
+								continue
+							highest_step = max( highest_step, s.step )
 							self.losses.append( { "step": s.step, "value": s.value, "type": key } )
 				except Exception as e:
 					print("Failed to parse event log:", log)
@@ -525,7 +570,9 @@ class TrainingState():
 
 		else:
 			logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
-			infos = {}
+			if update:
+				logs = [logs[-1]]
+
 			for log in logs:
 				with open(log, 'r', encoding="utf-8") as f:
 					lines = f.readlines()
@@ -546,9 +593,13 @@ class TrainingState():
 
 			for k in infos:
 				if 'loss_gpt_total' in infos[k]:
-					self.losses.append({ "step": int(k), "value": infos[k]['loss_text_ce'], "type": "loss_text_ce" })
+					for key in keys:
-					self.losses.append({ "step": int(k), "value": infos[k]['loss_mel_ce'], "type": "loss_mel_ce" })
+						if update and int(k) <= self.last_info_check_at:
-					self.losses.append({ "step": int(k), "value": infos[k]['loss_gpt_total'], "type": "loss_gpt_total" })
+							continue
+						highest_step = max( highest_step, s.step )
+						self.losses.append({ "step": int(k), "value": infos[k][key], "type": key })
+
+		self.last_info_check_at = highest_step
 
 	def cleanup_old(self, keep=2):
 		if keep <= 0:
@@ -581,7 +632,8 @@ class TrainingState():
 			if line.find('Start training from epoch') >= 0:
 				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:
 					self.epoch = int(match[0].replace(",", ""))
@@ -662,12 +714,15 @@ class TrainingState():
 							
 				if 'loss_gpt_total' in self.info:
 					self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
-
+					"""
 					self.losses.append({ "step": self.it, "value": self.info['loss_text_ce'], "type": "loss_text_ce" })
 					self.losses.append({ "step": self.it, "value": self.info['loss_mel_ce'], "type": "loss_mel_ce" })
 					self.losses.append({ "step": self.it, "value": self.info['loss_gpt_total'], "type": "loss_gpt_total" })
-
+					"""
 					should_return = True
+
+				self.load_losses(update=True)
+
 			elif line.find('Saving models and training states') >= 0:
 				self.checkpoint = self.checkpoint + 1
 
@@ -1035,32 +1090,6 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
 		res = res + ["random", "microphone"]
 	return res
 
-def hash_file(path, algo="md5", buffer_size=0):
-	import hashlib
-
-	hash = None
-	if algo == "md5":
-		hash = hashlib.md5()
-	elif algo == "sha1":
-		hash = hashlib.sha1()
-	else:
-		raise Exception(f'Unknown hash algorithm specified: {algo}')
-
-	if not os.path.exists(path):
-		raise Exception(f'Path not found: {path}')
-
-	with open(path, 'rb') as f:
-		if buffer_size > 0:
-			while True:
-				data = f.read(buffer_size)
-				if not data:
-					break
-				hash.update(data)
-		else:
-			hash.update(f.read())
-
-	return "{0}".format(hash.hexdigest())
-
 def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
 	os.makedirs(dir, exist_ok=True)
 	base = [get_model_path('autoregressive.pth')]