implicitly load either normal pickled weights or safetensors on loading the model

vall_e/config.py

@@ -718,6 +718,8 @@ class Config(BaseConfig):
 
 	weights_format: str = "pth" # "pth" | "sft"
 
+	supported_weights_formats: list[str] = field(default_factory=lambda: ["sft", "safetensors", "pt", "pth"])
+
 	@property
 	def model(self):
 		for i, model in enumerate(self.models):

vall_e/engines/__init__.py

@@ -12,7 +12,7 @@ from .base import Engines, TrainFeeder, default_feeder, Engine as LocalEngine
 
 from ..models import get_models, get_model
 from ..utils import wrapper as ml
-from ..utils.io import torch_save, torch_load
+from ..utils.io import torch_save, torch_load, pick_path
 from ..models.lora import apply_lora, lora_load_state_dict
 
 import torch
@@ -43,7 +43,7 @@ def load_engines(training=True):
 
 		checkpoint_path = cfg.ckpt_dir / name / "latest"
 		# automatically load from state dict if one is provided, but no DeepSpeed checkpoint is present
-		load_path = cfg.ckpt_dir / name / f"fp32.{cfg.weights_format}"
+		load_path = pick_path( cfg.ckpt_dir / name / f"fp32.{cfg.weights_format}", *[ f'.{format}' for format in cfg.supported_weights_formats] )
 
 		# actually use the lora-specific checkpoint if available
 		if cfg.lora is not None:			
@@ -52,7 +52,7 @@ def load_engines(training=True):
 		# to handle the issue of training with deepspeed, but inferencing with local
 		if checkpoint_path.exists() and backend == "local":
 			tag = open(checkpoint_path).read()
-			checkpoint_path = checkpoint_path.parent / tag / f"state.{cfg.weights_format}"
+			checkpoint_path = pick_path( checkpoint_path.parent / tag / f"state.{cfg.weights_format}", *[ f'.{format}' for format in cfg.supported_weights_formats] )
 
 		if not loads_state_dict and not checkpoint_path.exists() and load_path.exists():
 			print("Checkpoint missing, but weights found:", load_path)
@@ -197,7 +197,7 @@ def load_engines(training=True):
 
 			# load lora weights if exists
 			if cfg.lora is not None:
-				lora_path = cfg.ckpt_dir / cfg.lora.full_name / f"lora.{cfg.weights_format}"
+				lora_path = pick_path( cfg.ckpt_dir / cfg.lora.full_name / f"lora.{cfg.weights_format}", *[ f'.{format}' for format in cfg.supported_weights_formats] )
 				if lora_path.exists():
 					print( "Loaded LoRA state dict:", lora_path )
 

vall_e/utils/io.py

@@ -8,6 +8,16 @@ from safetensors.torch import save_file as sft_save
 def coerce_path( path ):
 	return path if isinstance( path, Path ) else Path(path)
 
+def pick_path( path, *suffixes ):
+	suffixes = [*suffixes]
+
+	for suffix in suffixes:
+		p = path.with_suffix( suffix )
+		if p.exists():
+			return p
+
+	return path
+
 def is_dict_of( d, t ):
 	if not isinstance( d, dict ):
 		return False

vall_e/webui.py

@@ -33,7 +33,10 @@ def gradio_wrapper(inputs):
 		def wrapped_function(*args, **kwargs):
 			for i, key in enumerate(inputs):
 				kwargs[key] = args[i]
-			return fun(**kwargs)
+			try:
+				return fun(**kwargs)
+			except Exception as e:
+				raise gr.Error(str(e))
 		return wrapped_function
 	return decorated
 
@@ -95,6 +98,9 @@ def init_tts(yaml=None, restart=False):
 
 @gradio_wrapper(inputs=layout["inference"]["inputs"].keys())
 def do_inference( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
+	if not cfg.yaml_path:
+		raise Exception("No YAML loaded.")
+
 	if kwargs.pop("dynamic-sampling", False):
 		kwargs['min-ar-temp'] = 0.85 if kwargs['ar-temp'] > 0.85 else 0.0
 		kwargs['min-nar-temp'] = 0.85 if kwargs['nar-temp'] > 0.85 else 0.0 # should probably disable it for the NAR
@@ -131,7 +137,7 @@ def do_inference( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
 
 	"""
 	if not args.references:
-		raise ValueError("No reference audio provided.")
+		raise Exception("No reference audio provided.")
 	"""
 
 	tts = init_tts()