added (yet another) experimental voice latent calculation mode (when chunk size is 0 and theres a dataset generated, itll leverage it by padding to a common size then computing them, should help avoid splitting mid-phoneme)

src/utils.py

@@ -31,7 +31,7 @@ import pandas as pd
 from datetime import datetime
 from datetime import timedelta
 
-from tortoise.api import TextToSpeech, MODELS, get_model_path
+from tortoise.api import TextToSpeech, MODELS, get_model_path, pad_or_truncate
 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
@@ -89,6 +89,8 @@ def generate(
 		if tts_loading:
 			raise Exception("TTS is still initializing...")
 		load_tts()
+	if hasattr(tts, "loading") and tts.loading:
+		raise Exception("TTS is still initializing...")
 
 	do_gc()
 
@@ -121,17 +123,8 @@ def generate(
 				voice_samples, conditioning_latents = load_voice(voice)
 
 		if voice_samples and len(voice_samples) > 0:
+			conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=voice_latents_chunks)
 			sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
-
-			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)
-				
-			if voice != "microphone":
-				if hasattr(tts, 'autoregressive_model_hash'):
-					torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
-				else:
-					torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
 			voice_samples = None
 		else:
 			if conditioning_latents is not None:
@@ -551,6 +544,10 @@ def update_baseline_for_latents_chunks( voice ):
 	if not os.path.isdir(path):
 		return 1
 
+	dataset_file = f'./training/{voice}/train.txt'
+	if os.path.exists(dataset_file):
+		return 0 # 0 will leverage using the LJspeech dataset for computing latents
+
 	files = os.listdir(path)
 	
 	total = 0
@@ -565,11 +562,13 @@ def update_baseline_for_latents_chunks( voice ):
 		total_duration += duration
 		total = total + 1
 
+
+	# brain too fried to figure out a better way
 	if args.autocalculate_voice_chunk_duration_size == 0:
 		return int(total_duration / total) if total > 0 else 1
 	return int(total_duration / args.autocalculate_voice_chunk_duration_size) if total_duration > 0 else 1
 
-def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
+def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, progress=None):
 	global tts
 	global args
 	
@@ -581,12 +580,42 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 			raise Exception("TTS is still initializing...")
 		load_tts()
 
-	voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
+	if hasattr(tts, "loading") and tts.loading:
+		raise Exception("TTS is still initializing...")
+
+	if voice:
+		load_from_dataset = voice_latents_chunks == 0
+
+		if load_from_dataset:
+			dataset_path = f'./training/{voice}/train.txt'
+			if not os.path.exists(dataset_path):
+				load_from_dataset = False
+			else:
+				with open(dataset_path, 'r', encoding="utf-8") as f:
+					lines = f.readlines()
+
+				print("Leveraging LJSpeech dataset for computing latents")
+
+				voice_samples = []
+				max_length = 0
+				for line in lines:
+					filename = f'./training/{voice}/{line.split("|")[0]}'
+					
+					waveform = load_audio(filename, 22050)
+					max_length = max(max_length, waveform.shape[-1])
+					voice_samples.append(waveform)
+
+				for i in range(len(voice_samples)):
+					voice_samples[i] = pad_or_truncate(voice_samples[i], max_length)
+
+				voice_latents_chunks = len(voice_samples)
+		if not load_from_dataset:
+			voice_samples, _ = 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)
+	conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents, progress=progress)
 
 	if len(conditioning_latents) == 4:
 		conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
@@ -596,7 +625,7 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 	else:
 		torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
 
-	return voice
+	return conditioning_latents
 
 # superfluous, but it cleans up some things
 class TrainingState():
@@ -1847,6 +1876,10 @@ def update_autoregressive_model(autoregressive_model_path):
 		if tts_loading:
 			raise Exception("TTS is still initializing...")
 		return
+	
+	if hasattr(tts, "loading") and tts.loading:
+		raise Exception("TTS is still initializing...")
+
 
 	print(f"Loading model: {autoregressive_model_path}")
 	tts.load_autoregressive_model(autoregressive_model_path)
@@ -1867,6 +1900,9 @@ def update_vocoder_model(vocoder_model):
 			raise Exception("TTS is still initializing...")
 		return
 
+	if hasattr(tts, "loading") and tts.loading:
+		raise Exception("TTS is still initializing...")
+
 	print(f"Loading model: {vocoder_model}")
 	tts.load_vocoder_model(vocoder_model)
 	print(f"Loaded model: {tts.vocoder_model}")

src/webui.py

@@ -163,6 +163,11 @@ def history_view_results( voice ):
 		gr.Dropdown.update(choices=sorted(files))
 	)
 
+def compute_latents_proxy(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
+	compute_latents( voice=voice, voice_latents_chunks=voice_latents_chunks, progress=progress )
+	return voice
+
+
 def import_voices_proxy(files, name, progress=gr.Progress(track_tqdm=True)):
 	import_voices(files, name, progress)
 	return gr.update()
@@ -387,7 +392,7 @@ def setup_gradio():
 					prompt = gr.Textbox(lines=1, label="Custom Emotion")
 					voice = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
 					mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
-					voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=128, value=1, step=1)
+					voice_latents_chunks = gr.Number(label="Voice Chunks", precision=0, value=0)
 					with gr.Row():
 						refresh_voices = gr.Button(value="Refresh Voice List")
 						recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
@@ -704,7 +709,7 @@ def setup_gradio():
 			],
 		)
 
-		recompute_voice_latents.click(compute_latents,
+		recompute_voice_latents.click(compute_latents_proxy,
 			inputs=[
 				voice,
 				voice_latents_chunks,