test

examples/industrial_data_pretraining/lcbnet/demo2.sh

@@ -7,8 +7,7 @@ python -m funasr.bin.inference \
 ++init_param=${file_dir}/model.pb \
 ++tokenizer_conf.token_list=${file_dir}/tokens.txt \
 ++frontend_conf.cmvn_file=${file_dir}/am.mvn \
-++input=${file_dir}/wav.scp \
-++input=${file_dir}/ocr_text \
+++input=[${file_dir}/wav.scp,${file_dir}/ocr_text] \
 +data_type='["sound", "text"]' \
 ++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
 ++output_dir="./outputs/debug" \

funasr/auto/auto_model.py

@@ -172,14 +172,11 @@ class AutoModel:
 
         # build model
         model_class = tables.model_classes.get(kwargs["model"])
-        pdb.set_trace()
         model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
-        pdb.set_trace()
         model.to(device)
         
         # init_param
         init_param = kwargs.get("init_param", None)
-        pdb.set_trace()
         if init_param is not None:
             logging.info(f"Loading pretrained params from {init_param}")
             load_pretrained_model(

funasr/train_utils/load_pretrained_model.py

@@ -96,19 +96,17 @@ def load_pretrained_model(
 	
 	obj = model
 	dst_state = obj.state_dict()
-	# import pdb;
-	# pdb.set_trace()
 	print(f"ckpt: {path}")
-	pdb.set_trace()
+
 	if oss_bucket is None:
 		src_state = torch.load(path, map_location=map_location)
 	else:
 		buffer = BytesIO(oss_bucket.get_object(path).read())
 		src_state = torch.load(buffer, map_location=map_location)
-	pdb.set_trace()
+
 	if "state_dict" in src_state:
 		src_state = src_state["state_dict"]
-	pdb.set_trace()
+
 	for k in dst_state.keys():
 		if not k.startswith("module.") and "module." + k in src_state.keys():
 			k_ddp = "module." + k
@@ -118,7 +116,6 @@ def load_pretrained_model(
 			dst_state[k] = src_state[k_ddp]
 		else:
 			print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}")
-	pdb.set_trace()
 	flag = obj.load_state_dict(dst_state, strict=True)
 	# print(flag)