we do a little garbage collection

src/utils.py

@@ -15,6 +15,7 @@ import base64
 import re
 import urllib.request
 import signal
+import gc
 
 import tqdm
 import torch
@@ -40,6 +41,9 @@ webui = None
 voicefixer = None
 whisper_model = None
 
+def do_gc():
+	gc.collect()
+
 def get_args():
 	global args
 	return args
@@ -152,6 +156,8 @@ def generate(
 	if not tts:
 		raise Exception("TTS is uninitialized or still initializing...")
 
+	do_gc()
+
 	if voice != "microphone":
 		voices = [voice]
 	else:
@@ -307,6 +313,9 @@ def generate(
 			# save here in case some error happens mid-batch
 			torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, tts.output_sample_rate)
 
+	del gen
+	do_gc()
+
 	for k in audio_cache:
 		audio = audio_cache[k]['audio']
 
@@ -480,19 +489,44 @@ def setup_tortoise(restart=False):
 	global args
 	global tts
 
-	if args.voice_fixer and not restart:
+	do_gc()
+
+	if args.voice_fixer:
 		setup_voicefixer(restart=restart)
 
 	if restart:
 		del tts
 		tts = None
 
-	print("Initializating TorToiSe...")
+	print(f"Initializating TorToiSe... (using model: {args.autoregressive_model})")
 	tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
 	get_model_path('dvae.pth')
 	print("TorToiSe initialized, ready for generation.")
 	return tts
 
+def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
+	global tts
+	global args
+
+	if not tts:
+		raise Exception("TTS is uninitialized or still initializing...")
+
+	do_gc()
+
+	voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
+
+	if voice_samples is None:
+		return
+
+	conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
+
+	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')
+
+	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 ):
 	settings = {
 		"iterations": iterations if iterations else 500,
@@ -737,32 +771,12 @@ def update_autoregressive_model(path_name):
 		raise Exception("TTS is uninitialized or still initializing...")
 
 	print(f"Loading model: {path_name}")
-	if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(path_name):
-		args.autoregressive_model = path_name
-		save_args_settings()
-	# polyfill in case a user did NOT update the packages
-	else:
-		from tortoise.models.autoregressive import UnifiedVoice
-
-		previous_path = tts.autoregressive_model_path
-		tts.autoregressive_model_path = path_name if path_name and os.path.exists(path_name) else get_model_path('autoregressive.pth')
-
-		del tts.autoregressive
-		tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
-										  model_dim=1024,
-										  heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
-										  train_solo_embeddings=False).cpu().eval()
-		tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path))
-		tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache)
-		if tts.preloaded_tensors:
-			tts.autoregressive = tts.autoregressive.to(tts.device)
-
-		if previous_path != tts.autoregressive_model_path:
-			args.autoregressive_model = path_name
-			save_args_settings()
-	
+	tts.load_autoregressive_model(path_name)
 	print(f"Loaded model: {tts.autoregressive_model_path}")
 	
+	args.autoregressive_model = path_name
+	save_args_settings()
+
 	return path_name
 
 def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):

src/webui.py

@@ -86,27 +86,6 @@ def run_generation(
 		gr.update(value=stats, visible=True),
 	)
 
-def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
-	global tts
-	global args
-
-	if not tts:
-		raise Exception("TTS is uninitialized or still initializing...")
-
-	voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
-
-	if voice_samples is None:
-		return
-
-	conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
-
-	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')
-
-	return voice
-
 def update_presets(value):
 	PRESETS = {
 		'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},