tweak

vall_e/__main__.py

@@ -13,6 +13,7 @@ def main():
 	parser.add_argument("text")
 	parser.add_argument("references", type=path_list, default=None)
 	parser.add_argument("--language", type=str, default="en")
+	parser.add_argument("--task", type=str, default="tts")
 	parser.add_argument("--out-path", type=Path, default=None)
 
 	parser.add_argument("--yaml", type=Path, default=None)
@@ -53,6 +54,7 @@ def main():
 		text=args.text,
 		references=args.references,
 		language=args.language,
+		task=args.task,
 		out_path=args.out_path,
 		input_prompt_length=args.input_prompt_length,
 		max_ar_steps=args.max_ar_steps, max_nar_levels=args.max_nar_levels,

vall_e/data.py

@@ -1053,11 +1053,13 @@ class Dataset(_Dataset):
 				task,
 			]
 
-		# Base TTS (<resp> => <text>)
+		# Base STT (<resp> => <text>)
 		elif task == "stt":
 			# easier to just keep it instead of wrangling around trying to remove it
 			# it might also help to provide a guidance prompt but who knows right now
-			proms = self.sample_prompts(spkr_name, ignore=path)
+			proms = [
+				task
+			]
 
 		# noise suppression (<text>? <resp+noise> => <resp>)
 		# speech removal (<text>?<resp+noise> => <noise>)

vall_e/inference.py

@@ -128,6 +128,7 @@ class TTS():
 		text,
 		references,
 		language="en",
+		task="tts",
 		#
 		max_ar_steps=6 * cfg.dataset.frames_per_second,
 		max_nar_levels=7,
@@ -181,6 +182,40 @@ class TTS():
 		
 		set_seed(seed)
 
+		if task == "stt":
+			resp = self.encode_audio( references )
+			lang = self.encode_lang( language )
+			
+			reps = to_device(reps, device=self.device, dtype=torch.int16)
+			lang = to_device(lang, device=self.device, dtype=torch.uint8)
+
+			with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
+				if model_ar is not None:
+					text_list = model_ar(
+						text_list=None, proms_list=[resp], lang_list=[lang], resps_list=[resp], max_steps=max_ar_steps,
+						sampling_temperature=ar_temp,
+						sampling_min_temperature=min_ar_temp,
+						sampling_top_p=top_p, sampling_top_k=top_k,
+						sampling_repetition_penalty=repetition_penalty, sampling_repetition_penalty_decay=repetition_penalty_decay,
+						sampling_length_penalty=length_penalty,
+						sampling_beam_width=beam_width,
+						sampling_mirostat_tau=mirostat_tau,
+						sampling_mirostat_eta=mirostat_eta,
+						sampling_dry_multiplier=dry_multiplier,
+						sampling_dry_base=dry_base,
+						sampling_dry_allowed_length=dry_allowed_length,
+
+						disable_tqdm=not tqdm,
+					)
+				else:
+					raise Exception("!")
+				
+				text_list = [ cfg.tokenizer.decode( text ) for text in text_list ]
+				print( text_list )
+
+			return text_list[0]
+
+
 		for line in lines:
 			if out_path is None:
 				output_dir = Path("./data/results/")

vall_e/models/ar_nar.py

@@ -357,11 +357,14 @@ def example_usage():
 	from ..emb.qnt import decode_to_file, unload_model, trim_random, repeat_extend_audio, concat_audio, merge_audio
 	from ..engines import Engine, Engines
 	from ..utils import wrapper as ml
+	from ..utils import setup_logging
 	
 	import numpy as np
 	import re
 
+	setup_logging()
 	device = "cuda"
+
 	
 	# mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it)
 	"""

vall_e/utils/utils.py

@@ -120,7 +120,7 @@ class TqdmLoggingHandler(logging.Handler):
 			self.handleError(record) 
 
 @global_leader_only
-def setup_logging(log_dir: str | Path | None = "log", log_level="info"):
+def setup_logging(log_dir: str | Path | None = None, log_level="info"):
 	handlers = []
 
 	#stdout_handler = StreamHandler()

vall_e/webui.py

@@ -13,7 +13,7 @@ from pathlib import Path
 
 from .inference import TTS, cfg
 from .train import train
-from .utils import get_devices
+from .utils import get_devices, setup_logging
 
 tts = None
 
@@ -338,6 +338,8 @@ with ui:
 		gr.Markdown(md)
 
 def start( lock=True ):
+	setup_logging()
+	
 	ui.queue(max_size=8)
 	ui.launch(share=args.share, server_name=args.listen_host, server_port=args.listen_port, prevent_thread_lock=not lock)