god i finally found some time and focus: reworded print/save freq per epoch => print/save freq (in epochs), added import config button to reread the last used settings (will check for the output folder's configs first, then the generated ones) and auto-grab the last resume state (if available), some other cleanups i genuinely don't remember what I did when I spaced out for 20 minutes

src/utils.py

@@ -438,7 +438,7 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 
 # superfluous, but it cleans up some things
 class TrainingState():
-	def __init__(self, config_path, buffer_size=8):
+	def __init__(self, config_path):
 		self.cmd = ['train.bat', config_path] if os.name == "nt" else ['bash', './train.sh', config_path]
 
 		# parse config to get its iteration
@@ -465,7 +465,7 @@ class TrainingState():
 		self.training_started = False
 
 		self.info = {}
-		self.status = ""
+		self.status = "..."
 
 		self.epoch_rate = ""
 		self.epoch_time_start = 0
@@ -491,7 +491,7 @@ class TrainingState():
 				match = re.findall(r'iter: ([\d,]+)', line)
 				if match and len(match) > 0:
 					self.it = int(match[0].replace(",", ""))
-		elif progress is not None:
+		else:
 			if line.find('%|') > 0 and not self.open_state:
 				self.open_state = True
 			elif line.find('100%|') == 0 and self.open_state:
@@ -505,7 +505,12 @@ class TrainingState():
 				self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
 				self.eta_hhmmss = str(timedelta(seconds=int(self.eta)))
 
-				progress(self.epoch / float(self.epochs), f'[{self.epoch}/{self.epochs}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} Training... {self.status}')
+				percent = self.epoch / float(self.epochs)
+				message = f'[{self.epoch}/{self.epochs}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} {self.status}'
+				print(f'{"{:.3f}".format(percent*100)}% {message}')
+				if progress is not None:
+					progress(percent, message)
+				self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
 
 			if line.find('INFO: [epoch:') >= 0:
 				# easily rip out our stats...
@@ -516,12 +521,20 @@ class TrainingState():
 						
 				if 'loss_gpt_total' in self.info:
 					self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
+					print(self.status)
+					self.buffer.append(self.status)
 			elif line.find('Saving models and training states') >= 0:
 				self.checkpoint = self.checkpoint + 1
-				progress(self.checkpoint / float(self.checkpoints), f'[{self.checkpoint}/{self.checkpoints}] Saving checkpoint...')
-
+				percent = self.checkpoint / float(self.checkpoints)
+				message = f'[{self.checkpoint}/{self.checkpoints}] Saving checkpoint...'
+				print(f'{"{:.3f}".format(percent*100)}% {message}')
+				if progress is not None:
+					progress(percent, message)
+				self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
+
+		self.buffer = self.buffer[-buffer_size:]
 		if verbose or not self.training_started:
-			return "".join(self.buffer[-buffer_size:])
+			return "".join(self.buffer)
 
 def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
 	global training_state
@@ -535,25 +548,22 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
 	unload_whisper()
 	unload_voicefixer()
 
-	training_state = TrainingState(config_path=config_path, buffer_size=buffer_size)
+	training_state = TrainingState(config_path=config_path)
 
 	for line in iter(training_state.process.stdout.readline, ""):
-		print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
 		
 		res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress )
+		print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
 		if res:
 			yield res
 
 	training_state.process.stdout.close()
 	return_code = training_state.process.wait()
-	output = "".join(training_state.buffer[-buffer_size:])
 	training_state = None
 	
 	#if return_code:
 	#	raise subprocess.CalledProcessError(return_code, cmd)
 
-	return output
-
 def reconnect_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
 	global training_state
 	if not training_state or not training_state.process:
@@ -563,10 +573,6 @@ def reconnect_training(config_path, verbose=False, buffer_size=8, progress=gr.Pr
 		res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress )
 		if res:
 			yield res
-			
-	output = "".join(training_state.buffer[-buffer_size:])
-
-	return output
 
 def stop_training():
 	global training_process
@@ -644,7 +650,7 @@ EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
 def schedule_learning_rate( iterations ):
 	return [int(iterations * d) for d in EPOCH_SCHEDULE]
 
-def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate_schedule, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
+def optimize_training_settings( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
 	name = f"{voice}-finetune"
 	dataset_name = f"{voice}-train"
 	dataset_path = f"./training/{voice}/train.txt"
@@ -694,9 +700,9 @@ def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate
 	messages.append(f"For {epochs} epochs with {lines} lines in batches of {batch_size}, iterating for {iterations} steps ({int(iterations / epochs)} steps per epoch)")
 
 	return (
-		batch_size,
 		learning_rate,
 		learning_rate_schedule,
+		batch_size,
 		mega_batch_factor,
 		print_rate,
 		save_rate,
@@ -704,7 +710,7 @@ def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate
 		messages
 	)
 
-def save_training_settings( iterations=None, batch_size=None, learning_rate=None, learning_rate_schedule=None, mega_batch_factor=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None, half_p=None ):	
+def save_training_settings( iterations=None, learning_rate=None, learning_rate_schedule=None, batch_size=None, mega_batch_factor=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None, half_p=None ):	
 	settings = {
 		"iterations": iterations if iterations else 500,
 		"batch_size": batch_size if batch_size else 64,

src/webui.py

@@ -200,7 +200,65 @@ def optimize_training_settings_proxy( *args, **kwargs ):
 		"\n".join(tup[7])
 	)
 
-def save_training_settings_proxy( epochs, batch_size, learning_rate, learning_rate_schedule, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
+def import_training_settings_proxy( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
+	indir = f'./training/{voice}/'
+	outdir = f'./training/{voice}-finetune/'
+
+	in_config_path = f"{indir}/train.yaml"
+	out_configs = sorted([d[:-5] for d in os.listdir(outdir) if d[-5:] == ".yaml" ])
+	if len(out_configs) > 0:
+		out_config_path = f'{outdir}/{out_configs[-1]}.yaml'
+
+	config_path = out_config_path if out_config_path else in_config_path
+
+	messages = []
+	with open(config_path, 'r') as file:
+		config = yaml.safe_load(file)
+		messages.append(f"Importing from: {config_path}")
+
+	dataset_path = f"./training/{voice}/train.txt"
+	with open(dataset_path, 'r', encoding="utf-8") as f:
+		lines = len(f.readlines())
+		messages.append(f"Basing epoch size to {lines} lines")
+
+	batch_size = config['datasets']['train']['batch_size']
+	mega_batch_factor = config['train']['mega_batch_factor']
+	
+	iterations = config['train']['niter']
+	steps_per_iteration = int(lines / batch_size)
+	epochs = int(iterations / steps_per_iteration)
+
+
+	learning_rate = config['steps']['gpt_train']['optimizer_params']['lr']
+	learning_rate_schedule = [ int(x / steps_per_iteration) for x in config['train']['gen_lr_steps'] ]
+
+
+	print_rate = int(config['logger']['print_freq'] / steps_per_iteration)
+	save_rate = int(config['logger']['save_checkpoint_freq'] / steps_per_iteration)
+
+	statedir = f'{outdir}/training_state/' # NOOO STOP MIXING YOUR CASES
+	resumes = sorted([int(d[:-6]) for d in os.listdir(statedir) if d[-6:] == ".state" ])
+
+	if len(resumes) > 0:
+		resume_path = f'{statedir}/{resumes[-1]}.state'
+		messages.append(f"Latest resume found: {resume_path}")
+
+	messages = "\n".join(messages)
+
+	return (
+		epochs,
+		learning_rate,
+		learning_rate_schedule,
+		batch_size,
+		mega_batch_factor,
+		print_rate,
+		save_rate,
+		resume_path,
+		messages
+	)
+
+
+def save_training_settings_proxy( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
 	name = f"{voice}-finetune"
 	dataset_name = f"{voice}-train"
 	dataset_path = f"./training/{voice}/train.txt"
@@ -333,8 +391,9 @@ def setup_gradio():
 					repetition_penalty = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty")
 					cond_free_k = gr.Slider(value=2.0, minimum=0, maximum=4, label="Conditioning-Free K")
 				with gr.Column():
-					submit = gr.Button(value="Generate")
-					stop = gr.Button(value="Stop")
+					with gr.Row():
+						submit = gr.Button(value="Generate")
+						stop = gr.Button(value="Stop")
 
 					generation_results = gr.Dataframe(label="Results", headers=["Seed", "Time"], visible=False)
 					source_sample = gr.Audio(label="Source Sample", visible=False)
@@ -392,30 +451,45 @@ def setup_gradio():
 					with gr.Column():
 						training_settings = [
 							gr.Number(label="Epochs", value=500, precision=0),
-							gr.Number(label="Batch Size", value=128, precision=0),
-							gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
-							gr.Textbox(label="Learning Rate Schedule", placeholder=str(EPOCH_SCHEDULE)),
-							gr.Number(label="Mega Batch Factor", value=4, precision=0),
-							gr.Number(label="Print Frequency per Epoch", value=5, precision=0),
-							gr.Number(label="Save Frequency per Epoch", value=5, precision=0),
+						]
+						with gr.Row():
+							training_settings = training_settings + [
+								gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
+								gr.Textbox(label="Learning Rate Schedule", placeholder=str(EPOCH_SCHEDULE)),
+							]
+						with gr.Row():
+							training_settings = training_settings + [
+								gr.Number(label="Batch Size", value=128, precision=0),
+								gr.Number(label="Mega Batch Factor", value=4, precision=0),
+							]
+						with gr.Row():
+							training_settings = training_settings + [
+								gr.Number(label="Print Frequency (in epochs)", value=5, precision=0),
+								gr.Number(label="Save Frequency (in epochs)", value=5, precision=0),
+							]
+						training_settings = training_settings + [
 							gr.Textbox(label="Resume State Path", placeholder="./training/${voice}-finetune/training_state/${last_state}.state"),
 							gr.Checkbox(label="Half Precision", value=False),
 						]
 						dataset_list = gr.Dropdown( get_dataset_list(), label="Dataset", type="value" )
 						training_settings = training_settings + [ dataset_list ]
-						refresh_dataset_list = gr.Button(value="Refresh Dataset List")
+						with gr.Row():
+							refresh_dataset_list = gr.Button(value="Refresh Dataset List")
+							import_dataset_button = gr.Button(value="Import Dataset")
 					with gr.Column():
 						save_yaml_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
-						optimize_yaml_button = gr.Button(value="Validate Training Configuration")
-						save_yaml_button = gr.Button(value="Save Training Configuration")
+						with gr.Row():
+							optimize_yaml_button = gr.Button(value="Validate Training Configuration")
+							save_yaml_button = gr.Button(value="Save Training Configuration")
 			with gr.Tab("Run Training"):
 				with gr.Row():
 					with gr.Column():
 						training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_list())
 						refresh_configs = gr.Button(value="Refresh Configurations")
-						start_training_button = gr.Button(value="Train")
-						stop_training_button = gr.Button(value="Stop")
-						reconnect_training_button = gr.Button(value="Reconnect")
+						with gr.Row():
+							start_training_button = gr.Button(value="Train")
+							stop_training_button = gr.Button(value="Stop")
+							reconnect_training_button = gr.Button(value="Reconnect")
 					with gr.Column():
 						training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
 						verbose_training = gr.Checkbox(label="Verbose Console Output")
@@ -641,6 +715,10 @@ def setup_gradio():
 			inputs=training_settings,
 			outputs=training_settings[1:8] + [save_yaml_output] #console_output
 		)
+		import_dataset_button.click(import_training_settings_proxy,
+			inputs=training_settings,
+			outputs=training_settings[:8] + [save_yaml_output] #console_output
+		)
 		save_yaml_button.click(save_training_settings_proxy,
 			inputs=training_settings,
 			outputs=save_yaml_output #console_output