added more safeties and parameters to training yaml generator, I think I tested it extensively enough

models/.template.yaml

@@ -114,19 +114,19 @@ networks:
       #only_alignment_head: False  # uv3/4
 
 path:
-  pretrain_model_gpt: './models/tortoise/autoregressive.pth' # CHANGEME: copy this from tortoise cache
+  ${pretrain_model_gpt} 
   strict_load: true
-  #resume_state: ./models/tortoise/train_imgnet_vqvae_stage1/training_state/0.state   # <-- Set this to resume from a previous training state.
+  ${resume_state}
 
 # afaik all units here are measured in **steps** (i.e. one batch of batch_size is 1 unit)
 train: # CHANGEME: ALL OF THESE PARAMETERS SHOULD BE EXPERIMENTED WITH
   niter: ${iterations}
   warmup_iter: -1
-  mega_batch_factor: 4    # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
+  mega_batch_factor: ${mega_batch_factor} # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
-  val_freq: 500
+  val_freq: ${iterations}
 
   default_lr_scheme: MultiStepLR
-  gen_lr_steps: [500, 1000, 1400, 1800] #[50000, 100000, 140000, 180000]
+  gen_lr_steps: ${gen_lr_steps} #[50000, 100000, 140000, 180000]
   lr_gamma: 0.5
 
 eval:

src/utils.py

@@ -580,11 +580,13 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 
 	return voice
 
-def save_training_settings( iterations=None, batch_size=None, learning_rate=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None ):
+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,
 		"batch_size": batch_size if batch_size else 64,
 		"learning_rate": learning_rate if learning_rate else 1e-5,
+		"gen_lr_steps": learning_rate_schedule if learning_rate_schedule else [ 200, 300, 400, 500 ],
+		"mega_batch_factor": mega_batch_factor if mega_batch_factor else 4,
 		"print_rate": print_rate if print_rate else 50,
 		"save_rate": save_rate if save_rate else 50,
 		"name": name if name else "finetune",
@@ -592,19 +594,25 @@ def save_training_settings( iterations=None, batch_size=None, learning_rate=None
 		"dataset_path": dataset_path if dataset_path else "./training/finetune/train.txt",
 		"validation_name": validation_name if validation_name else "finetune",
 		"validation_path": validation_path if validation_path else "./training/finetune/train.txt",
+
+		'resume_state': f"resume_state: '{resume_path}'" if resume_path else f"# resume_state: './training/{name if name else 'finetune'}/training_state/#.state'",
+		'pretrain_model_gpt': "pretrain_model_gpt: './models/tortoise/autoregressive.pth'" if not resume_path else "# pretrain_model_gpt: './models/tortoise/autoregressive.pth'"
 	}
 
 	if not output_name:
 		output_name = f'{settings["name"]}.yaml'
 
-	outfile = f'./training/{output_name}'
 
 	with open(f'./models/.template.yaml', 'r', encoding="utf-8") as f:
 		yaml = f.read()
 
+	# i could just load and edit the YAML directly, but this is easier, as I don't need to bother with path traversals
 	for k in settings:
+		if settings[k] is None:
+			continue
 		yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
 
+	outfile = f'./training/{output_name}'
 	with open(outfile, 'w', encoding="utf-8") as f:
 		f.write(yaml)
 

src/webui.py

@@ -47,28 +47,28 @@ def run_generation(
 ):
 	try:
 		sample, outputs, stats = generate(
-			text,
+			text=text,
-			delimiter,
+			delimiter=delimiter,
-			emotion,
+			emotion=emotion,
-			prompt,
+			prompt=prompt,
-			voice,
+			voice=voice,
-			mic_audio,
+			mic_audio=mic_audio,
-			voice_latents_chunks,
+			voice_latents_chunks=voice_latents_chunks,
-			seed,
+			seed=seed,
-			candidates,
+			candidates=candidates,
-			num_autoregressive_samples,
+			num_autoregressive_samples=num_autoregressive_samples,
-			diffusion_iterations,
+			diffusion_iterations=diffusion_iterations,
-			temperature,
+			temperature=temperature,
-			diffusion_sampler,
+			diffusion_sampler=diffusion_sampler,
-			breathing_room,
+			breathing_room=breathing_room,
-			cvvp_weight,
+			cvvp_weight=cvvp_weight,
-			top_p,
+			top_p=top_p,
-			diffusion_temperature,
+			diffusion_temperature=diffusion_temperature,
-			length_penalty,
+			length_penalty=length_penalty,
-			repetition_penalty,
+			repetition_penalty=repetition_penalty,
-			cond_free_k,
+			cond_free_k=cond_free_k,
-			experimental_checkboxes,
+			experimental_checkboxes=experimental_checkboxes,
-			progress
+			progress=progress
 		)
 	except Exception as e:
 		message = str(e)
@@ -180,7 +180,7 @@ 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, print_rate, save_rate, voice ):
+def save_training_settings_proxy( iterations, 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"
@@ -190,13 +190,44 @@ def save_training_settings_proxy( iterations, batch_size, learning_rate, print_r
 	with open(dataset_path, 'r', encoding="utf-8") as f:
 		lines = len(f.readlines())
 
+	messages = []
+
 	if batch_size > lines:
-		print("Batch size is larger than your dataset, clamping...")
 		batch_size = lines
+		messages.append(f"Batch size is larger than your dataset, clamping batch size to: {batch_size}")
 	
-	out_name = f"{voice}/train.yaml"
+	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}")
 
-	return save_training_settings(iterations, batch_size, learning_rate, print_rate, save_rate, name, dataset_name, dataset_path, validation_name, validation_path, out_name )
+	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,
+		mega_batch_factor=mega_batch_factor,
+		print_rate=print_rate,
+		save_rate=save_rate,
+		name=name,
+		dataset_name=dataset_name,
+		dataset_path=dataset_path,
+		validation_name=validation_name,
+		validation_path=validation_path,
+		output_name=f"{voice}/train.yaml",
+		resume_path=resume_path,
+	))
+	return "\n".join(messages)
 
 def update_voices():
 	return (
@@ -326,8 +357,11 @@ def setup_gradio():
 							gr.Slider(label="Iterations", minimum=0, maximum=5000, value=500),
 							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.Slider(label="Mega Batch Factor", minimum=1, maximum=16, value=4),
 							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 + [