ugh

vall_e/data.py

@@ -1230,7 +1230,7 @@ if __name__ == "__main__":
 	parser = argparse.ArgumentParser("Save trained model to path.")
 	parser.add_argument("--action", type=str)
 	parser.add_argument("--tasks", type=str)
-	args = parser.parse_args()
+	args, unknown = parser.parse_known_args()
 
 	task = args.action
 

vall_e/emb/g2p.py

@@ -58,25 +58,4 @@ def encode(text: str, language="en-us", backend="auto") -> list[str]:
 		tokenized = tokenized.replace( f' {merge}', merge )
 
 	return tokenized.split(" ")
-	"""
+	"""
-
-
-@torch.no_grad()
-def main():
-	parser = argparse.ArgumentParser()
-	parser.add_argument("folder", type=Path)
-	parser.add_argument("--suffix", type=str, default=".txt")
-	args = parser.parse_args()
-
-	paths = list(args.folder.rglob(f"*{args.suffix}"))
-
-	for path in tqdm(paths):
-		phone_path = path.with_name(path.stem.split(".")[0] + ".phn.txt")
-		if phone_path.exists():
-			continue
-		phones = encode(open(path, "r", encoding="utf-8").read())
-		open(phone_path, "w", encoding="utf-8").write(" ".join(phones))
-
-
-if __name__ == "__main__":
-	main()

vall_e/emb/qnt.py

@@ -377,26 +377,4 @@ def merge_audio( *args, device="cpu", scale=[], levels=cfg.model.max_levels ):
 			decoded[i] = decoded[i] * scale[i]
 
 	combined = sum(decoded) / len(decoded)
-	return encode(combined, cfg.sample_rate, device="cpu", levels=levels)[0].t()
+	return encode(combined, cfg.sample_rate, device="cpu", levels=levels)[0].t()
-
-def main():
-	parser = argparse.ArgumentParser()
-	parser.add_argument("folder", type=Path)
-	parser.add_argument("--suffix", default=".wav")
-	parser.add_argument("--device", default="cuda")
-	parser.add_argument("--backend", default="encodec")
-	args = parser.parse_args()
-
-	device = args.device
-	paths = [*args.folder.rglob(f"*{args.suffix}")]
-
-	for path in tqdm(paths):
-		out_path = _replace_file_extension(path, ".qnt.pt")
-		if out_path.exists():
-			continue
-		qnt = encode_from_file(path, device=device)
-		torch.save(qnt.cpu(), out_path)
-
-
-if __name__ == "__main__":
-	main()

vall_e/export.py

@@ -63,7 +63,7 @@ def main():
 	parser = argparse.ArgumentParser("Save trained model to path.")
 	parser.add_argument("--module-only", action='store_true')
 	parser.add_argument("--hf", action='store_true', default=None) # convert to HF-style
-	args = parser.parse_args()
+	args, unknown = parser.parse_known_args()
 
 	if args.module_only:
 		cfg.trainer.load_module_only = True

vall_e/plot.py

@@ -104,7 +104,7 @@ if __name__ == "__main__":
 
 	parser.add_argument("--filename", default="log.txt")
 	parser.add_argument("--group-level", default=1)
-	args = parser.parse_args()
+	args, unknown = parser.parse_known_args()
 
 	path = cfg.rel_path / "logs"
 	paths = path.rglob(f"./*/{args.filename}")

vall_e/train.py

@@ -6,6 +6,7 @@ from .emb import qnt
 
 from .utils import setup_logging, to_device, trainer, flatten_dict, do_gc
 from .data import fold_inputs, unfold_outputs
+from .utils.distributed import is_global_leader
 
 import auraloss
 import json
@@ -205,7 +206,7 @@ def train():
 	# create log folder
 	setup_logging(cfg.log_dir)
 	# copy config yaml to backup
-	if cfg.yaml_path is not None:
+	if cfg.yaml_path is not None and is_global_leader():
 		shutil.copy( cfg.yaml_path, cfg.log_dir / "config.yaml" )
 
 	train_dl, subtrain_dl, val_dl = create_train_val_dataloader()