auto-suggested voice chunk size is based on the total duration of the voice files divided by 10 seconds, added setting to adjust the auto-suggested division factor (a really oddly worded one), because I'm sure people will OOM blindly generating without adjusting this slider

src/utils.py

@@ -454,6 +454,22 @@ def hash_file(path, algo="md5", buffer_size=0):
 
 	return "{0}".format(hash.hexdigest())
 
+def update_baseline_for_latents_chunks( voice ):
+	path = f'{get_voice_dir()}/{voice}/'
+	if not os.path.isdir(path):
+		return 1
+
+	files = os.listdir(path)
+	total_duration = 0
+	for file in files:
+		if file[-4:] != ".wav":
+			continue
+		metadata = torchaudio.info(f'{path}/{file}')
+		duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
+		total_duration += duration
+
+	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)):
 	global tts
 	global args
@@ -1244,6 +1260,7 @@ def setup_args():
 		'prune-nonfinal-outputs': True,
 		'use-bigvgan-vocoder': True,
 		'concurrency-count': 2,
+		'autocalculate-voice-chunk-duration-size': 10,
 		'output-sample-rate': 44100,
 		'output-volume': 1,
 		
@@ -1282,6 +1299,7 @@ def setup_args():
 	parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
 	parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
 	parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
+	parser.add_argument("--autocalculate-voice-chunk-duration-size", type=float, default=default_arguments['autocalculate-voice-chunk-duration-size'], help="Number of seconds to suggest voice chunk size for (for example, 100 seconds of audio at 10 seconds per chunk will suggest 10 chunks)")
 	parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
 	parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
 	
@@ -1321,7 +1339,7 @@ def setup_args():
 	
 	return args
 
-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, prune_nonfinal_outputs, use_bigvgan_vocoder, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
+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, prune_nonfinal_outputs, use_bigvgan_vocoder, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
 	global args
 
 	args.listen = listen
@@ -1340,7 +1358,8 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
 	args.voice_fixer = voice_fixer
 	args.voice_fixer_use_cuda = voice_fixer_use_cuda
 	args.concurrency_count = concurrency_count
-	args.output_sample_rate = output_sample_rate
+	args.output_sample_rate = 44000
+	args.autocalculate_voice_chunk_duration_size = autocalculate_voice_chunk_duration_size
 	args.output_volume = output_volume
 	
 	args.autoregressive_model = autoregressive_model
@@ -1372,6 +1391,7 @@ def save_args_settings():
 		'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
 		'concurrency-count': args.concurrency_count,
 		'output-sample-rate': args.output_sample_rate,
+		'autocalculate-voice-chunk-duration-size': args.autocalculate_voice_chunk_duration_size,
 		'output-volume': args.output_volume,
 		
 		'autoregressive-model': args.autoregressive_model,
@@ -1481,7 +1501,7 @@ def load_tts( restart=False, model=None ):
 		tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
 	except Exception as e:
 		tts = TextToSpeech(minor_optimizations=not args.low_vram)
-		update_autoregressive_model(args.autoregressive_model)
+		load_autoregressive_model(args.autoregressive_model)
 
 	if not hasattr(tts, 'autoregressive_model_hash'):
 		tts.autoregressive_model_hash = hash_file(tts.autoregressive_model_path)

src/webui.py

@@ -385,19 +385,6 @@ def setup_gradio():
 						refresh_voices = gr.Button(value="Refresh Voice List")
 						recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
 
-					def update_baseline_for_latents_chunks( voice ):
-						path = f'{get_voice_dir()}/{voice}/'
-						if not os.path.isdir(path):
-							return 1
-
-						files = os.listdir(path)
-						count = 0
-						for file in files:
-							if file[-4:] == ".wav":
-								count += 1
-
-						return count if count > 0 else 1
-
 					voice.change(
 						fn=update_baseline_for_latents_chunks,
 						inputs=voice,
@@ -575,7 +562,7 @@ def setup_gradio():
 					exec_inputs = exec_inputs + [
 						gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
 						gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count),
-						gr.Number(label="Output Sample Rate", precision=0, value=args.output_sample_rate),
+						gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size),
 						gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
 					]
 					
@@ -594,6 +581,7 @@ def setup_gradio():
 							inputs=autoregressive_model_dropdown,
 							outputs=None
 						)
+						# kill_button = gr.Button(value="Close UI")
 
 					def update_model_list_proxy( val ):
 						autoregressive_models = get_autoregressive_models()
@@ -814,6 +802,18 @@ def setup_gradio():
 			outputs=save_yaml_output #console_output
 		)
 
+		"""
+		def kill_process():
+			ui.close()
+			exit()
+
+		kill_button.click(
+			kill_process,
+			inputs=None,
+			outputs=None
+		)
+		"""
+
 		if os.path.isfile('./config/generate.json'):
 			ui.load(import_generate_settings, inputs=None, outputs=input_settings)