cleanup, "injected" dvae.pth to download through tortoise's model loader, so I don't need to keep copying it

src/train.py

@@ -4,12 +4,27 @@ import argparse
 import os
 import sys
 
+# this is some massive kludge that only works if it's called from a shell and not an import/PIP package
+# it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell
+
 sys.path.insert(0, './dlas/codes/')
+# this is also because DLAS is not written as a package in mind
+# it'll gripe when it wants to import from train.py
 sys.path.insert(0, './dlas/')
 
+# for PIP, replace it with:
+# sys.path.insert(0, os.path.dirname(os.path.realpath(dlas.__file__)))
+# sys.path.insert(0, f"{os.path.dirname(os.path.realpath(dlas.__file__))}/../")
+
+# don't even really bother trying to get DLAS PIP'd
+# without kludge, it'll have to be accessible as `codes` and not `dlas`
+
 from codes import train as tr
 from utils import util, options as option
 
+# this is effectively just copy pasted and cleaned up from the __main__ section of training.py
+# I'll clean it up better
+
 parser = argparse.ArgumentParser()
 parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml')
 parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')

src/utils.py

@@ -24,18 +24,20 @@ import gradio.utils
 
 from datetime import datetime
 
-from tortoise.api import TextToSpeech
+from tortoise.api import TextToSpeech, MODELS, get_model_path
 from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
 from tortoise.utils.text import split_and_recombine_text
 from tortoise.utils.device import get_device_name, set_device_name
 
 import whisper
 
+MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
+
 args = None
 tts = None
 webui = None
 voicefixer = None
-dlas = None
+whisper_model = None
 
 def get_args():
 	global args
@@ -53,7 +55,7 @@ def setup_args():
 		'sample-batch-size': None,
 		'embed-output-metadata': True,
 		'latents-lean-and-mean': True,
-		'voice-fixer': False, # I'm tired of long initialization of Colab notebooks
+		'voice-fixer': True,
 		'voice-fixer-use-cuda': True,
 		'force-cpu-for-conditioning-latents': False,
 		'device-override': None,
@@ -420,22 +422,46 @@ def generate(
 		stats,
 	)
 
+def run_training(config_path):
+	global tts
+	del tts
+	tts = None
+
+	import subprocess
+	subprocess.run(["python", "./src/train.py", "-opt", config_path], env=os.environ.copy(), shell=True, stdout=subprocess.PIPE)
+	"""
+	from train import train
+	train(config)
+	"""
+
+def setup_voicefixer(restart=False):
+	global voicefixer
+	if restart:
+		del voicefixer
+		voicefixer = None
+
+	try:
+		print("Initializating voice-fixer")
+		from voicefixer import VoiceFixer
+		voicefixer = VoiceFixer()
+		print("initialized voice-fixer")
+	except Exception as e:
+		print(f"Error occurred while tring to initialize voicefixer: {e}")
+
 def setup_tortoise(restart=False):
 	global args
 	global tts
-	global voicefixer
 
 	if args.voice_fixer and not restart:
-		try:
-			from voicefixer import VoiceFixer
-			print("Initializating voice-fixer")
-			voicefixer = VoiceFixer()
-			print("initialized voice-fixer")
-		except Exception as e:
-			print(f"Error occurred while tring to initialize voicefixer: {e}")
+		setup_voicefixer(restart=restart)
+
+	if restart:
+		del tts
+		tts = None
 
 	print("Initializating TorToiSe...")
 	tts = TextToSpeech(minor_optimizations=not args.low_vram)
+	get_model_path('dvae.pth')
 	print("TorToiSe initialized, ready for generation.")
 	return tts
 
@@ -461,7 +487,6 @@ def save_training_settings( batch_size=None, learning_rate=None, print_rate=None
 	with open(f'./training/{settings["name"]}.yaml', 'w', encoding="utf-8") as f:
 		f.write(yaml)
 
-whisper_model = None
 def prepare_dataset( files, outdir, language=None ):
 	global whisper_model
 	if whisper_model is None:
@@ -641,9 +666,7 @@ def check_for_updates():
 	return False
 
 def reload_tts():
-	global tts
-	del tts
-	tts = setup_tortoise(restart=True)
+	setup_tortoise(restart=True)
 
 def cancel_generate():
 	tortoise.api.STOP_SIGNAL = True

src/webui.py

@@ -123,6 +123,76 @@ def update_presets(value):
 	else:
 		return (gr.update(), gr.update())
 
+def get_training_configs():
+	configs = []
+	for i, file in enumerate(sorted(os.listdir(f"./training/"))):
+		if file[-5:] != ".yaml" or file[0] == ".":
+			continue
+		configs.append(f"./training/{file}")
+
+	return configs
+
+def update_training_configs():
+	return gr.update(choices=get_training_configs())
+
+def history_view_results( voice ):
+	results = []
+	files = []
+	outdir = f"./results/{voice}/"
+	for i, file in enumerate(sorted(os.listdir(outdir))):
+		if file[-4:] != ".wav":
+			continue
+
+		metadata, _ = read_generate_settings(f"{outdir}/{file}", read_latents=False)
+		if metadata is None:
+			continue
+			
+		values = []
+		for k in headers:
+			v = file
+			if k != "Name":
+				v = metadata[headers[k]]
+			values.append(v)
+
+
+		files.append(file)
+		results.append(values)
+
+	return (
+		results,
+		gr.Dropdown.update(choices=sorted(files))
+	)
+
+def read_generate_settings_proxy(file, saveAs='.temp'):
+	j, latents = read_generate_settings(file)
+
+	if latents:
+		outdir = f'{get_voice_dir()}/{saveAs}/'
+		os.makedirs(outdir, exist_ok=True)
+		with open(f'{outdir}/cond_latents.pth', 'wb') as f:
+			f.write(latents)
+		
+		latents = f'{outdir}/cond_latents.pth'
+
+	return (
+		j,
+		gr.update(value=latents, visible=latents is not None),
+		None if j is None else j['voice']
+	)
+
+def prepare_dataset_proxy( voice, language ):
+	return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language )
+	
+def update_voices():
+	return (
+		gr.Dropdown.update(choices=get_voice_list()),
+		gr.Dropdown.update(choices=get_voice_list()),
+		gr.Dropdown.update(choices=get_voice_list("./results/")),
+	)
+
+def history_copy_settings( voice, file ):
+	return import_generate_settings( f"./results/{voice}/{file}" )
+
 def setup_gradio():
 	global args
 	global ui
@@ -279,34 +349,6 @@ def setup_gradio():
 				with gr.Column():
 					history_audio = gr.Audio()
 					history_copy_settings_button = gr.Button(value="Copy Settings")
-				
-				def history_view_results( voice ):
-					results = []
-					files = []
-					outdir = f"./results/{voice}/"
-					for i, file in enumerate(sorted(os.listdir(outdir))):
-						if file[-4:] != ".wav":
-							continue
-
-						metadata, _ = read_generate_settings(f"{outdir}/{file}", read_latents=False)
-						if metadata is None:
-							continue
-							
-						values = []
-						for k in headers:
-							v = file
-							if k != "Name":
-								v = metadata[headers[k]]
-							values.append(v)
-
-
-						files.append(file)
-						results.append(values)
-
-					return (
-						results,
-						gr.Dropdown.update(choices=sorted(files))
-					)
 
 				history_view_results_button.click(
 					fn=history_view_results,
@@ -335,23 +377,6 @@ def setup_gradio():
 					metadata_out = gr.JSON(label="Audio Metadata")
 					latents_out = gr.File(type="binary", label="Voice Latents")
 
-					def read_generate_settings_proxy(file, saveAs='.temp'):
-						j, latents = read_generate_settings(file)
-
-						if latents:
-							outdir = f'{get_voice_dir()}/{saveAs}/'
-							os.makedirs(outdir, exist_ok=True)
-							with open(f'{outdir}/cond_latents.pth', 'wb') as f:
-								f.write(latents)
-							
-							latents = f'{outdir}/cond_latents.pth'
-
-						return (
-							j,
-							gr.update(value=latents, visible=latents is not None),
-							None if j is None else j['voice']
-						)
-
 					audio_in.upload(
 						fn=read_generate_settings_proxy,
 						inputs=audio_in,
@@ -382,9 +407,6 @@ def setup_gradio():
 					with gr.Column():
 						prepare_dataset_button = gr.Button(value="Prepare")
 
-						def prepare_dataset_proxy( voice, language ):
-							return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language )
-
 						prepare_dataset_button.click(
 							prepare_dataset_proxy,
 							inputs=dataset_settings,
@@ -416,34 +438,12 @@ def setup_gradio():
 			with gr.Tab("Train"):
 				with gr.Row():
 					with gr.Column():
-						def get_training_configs():
-							configs = []
-							for i, file in enumerate(sorted(os.listdir(f"./training/"))):
-								if file[-5:] != ".yaml" or file[0] == ".":
-									continue
-								configs.append(f"./training/{file}")
-
-							return configs
-						def update_training_configs():
-							return gr.update(choices=get_training_configs())
-
 						training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_configs())
 						refresh_configs = gr.Button(value="Refresh Configurations")
-						train = gr.Button(value="Train")
-
-					def run_training_proxy( config ):
-						global tts
-						del tts
-
-						import subprocess
-						subprocess.run(["python", "./src/train.py", "-opt", config], env=os.environ.copy(), shell=True)
-						"""
-						from train import train
-						train(config)
-						"""
+						train = gr.Button(value="Train")						
 
 					refresh_configs.click(update_training_configs,inputs=None,outputs=training_configs)
-					train.click(run_training_proxy,
+					train.click(run_training,
 						inputs=training_configs,
 						outputs=None
 					)
@@ -506,17 +506,6 @@ def setup_gradio():
 			experimental_checkboxes,
 		]
 
-		# YUCK
-		def update_voices():
-			return (
-				gr.Dropdown.update(choices=get_voice_list()),
-				gr.Dropdown.update(choices=get_voice_list()),
-				gr.Dropdown.update(choices=get_voice_list("./results/")),
-			)
-
-		def history_copy_settings( voice, file ):
-			return import_generate_settings( f"./results/{voice}/{file}" )
-
 		refresh_voices.click(update_voices,
 			inputs=None,
 			outputs=[