doing something completely unrelated had me realize it's 1000x easier to just base things in terms of epochs, and calculate iteratsions from there

src/utils.py

@@ -580,6 +580,63 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 
 	return voice
 
+def calc_iterations( epochs, lines, batch_size ):
+	iterations = int(epochs * lines / float(batch_size))
+	return iterations
+
+def schedule_learning_rate( iterations ):
+	schedule = [ 9, 18, 25, 33 ]
+	return [int(iterations * d) for d in schedule]
+
+def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate_schedule, mega_batch_factor, print_rate, save_rate, resume_path, voice ):
+	name = f"{voice}-finetune"
+	dataset_name = f"{voice}-train"
+	dataset_path = f"./training/{voice}/train.txt"
+	validation_name = f"{voice}-val"
+	validation_path = f"./training/{voice}/train.txt"
+
+	with open(dataset_path, 'r', encoding="utf-8") as f:
+		lines = len(f.readlines())
+
+	messages = []
+
+	if batch_size > lines:
+		batch_size = lines
+		messages.append(f"Batch size is larger than your dataset, clamping batch size to: {batch_size}")	
+
+	if batch_size / mega_batch_factor < 2:
+		mega_batch_factor = int(batch_size / 2)
+		messages.append(f"Mega batch factor is too large for the given batch size, clamping mega batch factor to: {mega_batch_factor}")
+
+	iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
+	messages.append(f"For {epochs} epochs with {lines} lines, iterating for {iterations} steps")
+
+	if iterations < print_rate:
+		print_rate = iterations
+		messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {print_rate}")
+	
+	if iterations < save_rate:
+		save_rate = iterations
+		messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {save_rate}")
+
+	if resume_path and not os.path.exists(resume_path):
+		resume_path = None
+		messages.append("Resume path specified, but does not exist. Disabling...")
+
+	learning_rate_schedule = schedule_learning_rate( iterations / epochs ) # faster learning schedule compared to just passing lines / batch_size due to truncating
+	messages.append(f"Suggesting best learning rate schedule for iterations: {learning_rate_schedule}")
+
+	return (
+		batch_size,
+		learning_rate,
+		learning_rate_schedule,
+		mega_batch_factor,
+		print_rate,
+		save_rate,
+		resume_path,
+		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 ):
 	settings = {
 		"iterations": iterations if iterations else 500,

src/webui.py

@@ -180,7 +180,21 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
 def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ):
 	return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, progress=progress )
 
-def save_training_settings_proxy( iterations, batch_size, learning_rate, learning_rate_schedule, mega_batch_factor, print_rate, save_rate, resume_path, voice ):
+def optimize_training_settings_proxy( *args, **kwargs ):
+	tup = optimize_training_settings(*args, **kwargs)
+
+	return (
+		gr.update(value=tup[0]),
+		gr.update(value=tup[1]),
+		gr.update(value=tup[2]),
+		gr.update(value=tup[3]),
+		gr.update(value=tup[4]),
+		gr.update(value=tup[5]),
+		gr.update(value=tup[6]),
+		"\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, voice ):
 	name = f"{voice}-finetune"
 	dataset_name = f"{voice}-train"
 	dataset_path = f"./training/{voice}/train.txt"
@@ -192,28 +206,11 @@ def save_training_settings_proxy( iterations, batch_size, learning_rate, learnin
 
 	messages = []
 
-	if batch_size > lines:
+	iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
-		batch_size = lines
+	messages.append(f"For {epochs} epochs with {lines} lines, iterating for {iterations} steps")
-		messages.append(f"Batch size is larger than your dataset, clamping batch size to: {batch_size}")
-	
 
-	if batch_size / mega_batch_factor < 2:
+	messages.append(save_training_settings(
-		mega_batch_factor = int(batch_size / 2)
+		iterations=iterations,
-		messages.append(f"Mega batch factor is too large for the given batch size, clamping mega batch factor to: {mega_batch_factor}")
-
-	if iterations < print_rate:
-		print_rate = iterations
-		messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {print_rate}")
-	
-	if iterations < save_rate:
-		save_rate = iterations
-		messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {save_rate}")
-
-	if resume_path and not os.path.exists(resume_path):
-		messages.append("Resume path specified, but does not exist. Disabling...")
-		resume_path = None
-
-	messages.append(save_training_settings(iterations,
 		batch_size=batch_size,
 		learning_rate=learning_rate,
 		learning_rate_schedule=learning_rate_schedule,
@@ -355,19 +352,17 @@ def setup_gradio():
 				with gr.Row():
 					with gr.Column():
 						training_settings = [
-							gr.Slider(label="Iterations", minimum=0, maximum=5000, value=500),
+							gr.Slider(label="Epochs", minimum=0, maximum=500, value=10),
 							gr.Slider(label="Batch Size", minimum=2, maximum=128, value=64),
 							gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
-							gr.Textbox(label="Learning Rate Schedule", placeholder="[ 200, 300, 400, 500 ]"),
+							gr.Textbox(label="Learning Rate Schedule", placeholder="[ 100, 200, 300, 400 ]"),
 							gr.Slider(label="Mega Batch Factor", minimum=1, maximum=16, value=4, step=1),
 							gr.Number(label="Print Frequency", value=50),
 							gr.Number(label="Save Frequency", value=50),
 							gr.Textbox(label="Resume State Path", placeholder="./training/${voice}-finetune/training_state/${last_state}.state"),
 						]
 						dataset_list = gr.Dropdown( get_dataset_list(), label="Dataset", type="value" )
-						training_settings = training_settings + [
+						training_settings = training_settings + [ dataset_list ]
-							dataset_list
-						]
 						refresh_dataset_list = gr.Button(value="Refresh Dataset List")
 						"""
 						training_settings = training_settings + [
@@ -380,6 +375,7 @@ def setup_gradio():
 						"""
 					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.Tab("Run Training"):
 				with gr.Row():
@@ -591,6 +587,10 @@ def setup_gradio():
 			inputs=None,
 			outputs=dataset_list,
 		)
+		optimize_yaml_button.click(optimize_training_settings_proxy,
+			inputs=training_settings,
+			outputs=training_settings[1:8] + [save_yaml_output] #console_output
+		)
 		save_yaml_button.click(save_training_settings_proxy,
 			inputs=training_settings,
 			outputs=save_yaml_output #console_output