add weights_only=False to torch.load (#1984)

docs/source/for-dummies/model-export.rst

@@ -41,7 +41,7 @@ To give you an idea of what ``tdnn/exp/pretrained.pt`` contains, we can use the
 .. code-block:: python3
 
     >>> import torch
-    >>> m = torch.load("tdnn/exp/pretrained.pt")
+    >>> m = torch.load("tdnn/exp/pretrained.pt", weights_only=False)
     >>> list(m.keys())
     ['model']
     >>> list(m["model"].keys())

egs/aidatatang_200zh/ASR/local/prepare_lang.py

@@ -28,7 +28,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/pretrained.py

@@ -224,7 +224,7 @@ def main():
     logging.info("Creating model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell/ASR/conformer_ctc/decode.py

@@ -503,7 +503,7 @@ def main():
     else:
         H = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 

egs/aishell/ASR/conformer_ctc/pretrained.py

@@ -249,7 +249,7 @@ def main():
         use_feat_batchnorm=params.use_feat_batchnorm,
     )
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -315,7 +315,7 @@ def main():
         hyps = [[token_sym_table[i] for i in ids] for ids in token_ids]
     elif params.method in ["1best", "attention-decoder"]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu", weights_only=False))
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder

egs/aishell/ASR/conformer_mmi/decode.py

@@ -516,7 +516,7 @@ def main():
     else:
         H = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 

egs/aishell/ASR/local/prepare_lang.py

@@ -28,7 +28,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/aishell/ASR/pruned_transducer_stateless2/pretrained.py

@@ -227,7 +227,7 @@ def main():
     logging.info("About to create model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell/ASR/pruned_transducer_stateless3/pretrained.py

@@ -228,7 +228,7 @@ def main():
     logging.info("About to create model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell/ASR/pruned_transducer_stateless7_bbpe/decode.py

@@ -773,7 +773,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/aishell/ASR/pruned_transducer_stateless7_bbpe/pretrained.py

@@ -237,7 +237,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell/ASR/tdnn_lstm_ctc/decode.py

@@ -337,7 +337,7 @@ def main():
 
     logging.info(f"device: {device}")
 
-    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
+    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu", weights_only=False))
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 

egs/aishell/ASR/tdnn_lstm_ctc/pretrained.py

@@ -139,13 +139,13 @@ def main():
         subsampling_factor=params.subsampling_factor,
     )
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"])
     model.to(device)
     model.eval()
 
     logging.info(f"Loading HLG from {params.HLG}")
-    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu", weights_only=False))
     HLG = HLG.to(device)
     if not hasattr(HLG, "lm_scores"):
         # For whole-lattice-rescoring and attention-decoder

egs/aishell/ASR/transducer_stateless/pretrained.py

@@ -245,7 +245,7 @@ def main():
     logging.info("Creating model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell/ASR/transducer_stateless_modified-2/pretrained.py

@@ -225,7 +225,7 @@ def main():
     logging.info("About to create model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell/ASR/transducer_stateless_modified/pretrained.py

@@ -225,7 +225,7 @@ def main():
     logging.info("About to create model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"])
     model.to(device)
     model.eval()

egs/aishell/ASR/whisper/decode.py

@@ -89,10 +89,10 @@ def average_checkpoints(
     """
     n = len(filenames)
 
-    if "model" in torch.load(filenames[0], map_location=device):
-        avg = torch.load(filenames[0], map_location=device)["model"]
+    if "model" in torch.load(filenames[0], map_location=device, weights_only=False):
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)["model"]
     else:
-        avg = torch.load(filenames[0], map_location=device)
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)
 
     # Identify shared parameters. Two parameters are said to be shared
     # if they have the same data_ptr
@@ -107,10 +107,10 @@ def average_checkpoints(
     uniqued_names = list(uniqued.values())
 
     for i in range(1, n):
-        if "model" in torch.load(filenames[i], map_location=device):
-            state_dict = torch.load(filenames[i], map_location=device)["model"]
+        if "model" in torch.load(filenames[i], map_location=device, weights_only=False):
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)["model"]
         else:
-            state_dict = torch.load(filenames[i], map_location=device)
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)
         for k in uniqued_names:
             avg[k] += state_dict[k]
 
@@ -440,7 +440,7 @@ def main():
             start = params.epoch - params.avg
             assert start >= 1, start
             checkpoint = torch.load(
-                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
             )
             if "model" not in checkpoint:
                 # deepspeed converted checkpoint only contains model state_dict
@@ -469,7 +469,7 @@ def main():
             torch.save(model.state_dict(), filename)
         else:
             checkpoint = torch.load(
-                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
             )
             if "model" not in checkpoint:
                 model.load_state_dict(checkpoint, strict=True)

egs/aishell/ASR/zipformer/decode.py

@@ -761,7 +761,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/aishell/ASR/zipformer/decode_bbpe.py

@@ -783,7 +783,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/aishell/ASR/zipformer/pretrained_bbpe.py

@@ -298,7 +298,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell2/ASR/pruned_transducer_stateless5/decode.py

@@ -728,7 +728,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/aishell2/ASR/pruned_transducer_stateless5/pretrained.py

@@ -226,7 +226,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/aishell4/ASR/local/prepare_lang.py

@@ -28,7 +28,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/aishell4/ASR/pruned_transducer_stateless5/pretrained.py

@@ -238,7 +238,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/alimeeting/ASR/local/prepare_lang.py

@@ -28,7 +28,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/alimeeting/ASR/pruned_transducer_stateless2/pretrained.py

@@ -224,7 +224,7 @@ def main():
     logging.info("Creating model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/ami/ASR/pruned_transducer_stateless7/decode.py

@@ -672,7 +672,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/ami/SURT/dprnn_zipformer/train.py

@@ -1263,7 +1263,7 @@ def run(rank, world_size, args):
         logging.info(
             f"Initializing model with checkpoint from {params.model_init_ckpt}"
         )
-        init_ckpt = torch.load(params.model_init_ckpt, map_location=device)
+        init_ckpt = torch.load(params.model_init_ckpt, map_location=device, weights_only=False)
         model.load_state_dict(init_ckpt["model"], strict=False)
 
     if world_size > 1:

egs/ami/SURT/dprnn_zipformer/train_adapt.py

@@ -1254,7 +1254,7 @@ def run(rank, world_size, args):
         logging.info(
             f"Initializing model with checkpoint from {params.model_init_ckpt}"
         )
-        init_ckpt = torch.load(params.model_init_ckpt, map_location=device)
+        init_ckpt = torch.load(params.model_init_ckpt, map_location=device, weights_only=False)
         model.load_state_dict(init_ckpt["model"], strict=False)
 
     if world_size > 1:

egs/audioset/AT/zipformer/pretrained.py

@@ -141,7 +141,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/baker_zh/TTS/matcha/infer.py

@@ -115,7 +115,7 @@ def load_vocoder(checkpoint_path: Path) -> nn.Module:
 
     hifigan = HiFiGAN(h).to("cpu")
     hifigan.load_state_dict(
-        torch.load(checkpoint_path, map_location="cpu")["generator"]
+        torch.load(checkpoint_path, map_location="cpu", weights_only=False)["generator"]
     )
     _ = hifigan.eval()
     hifigan.remove_weight_norm()

egs/commonvoice/ASR/local/compile_hlg.py

@@ -73,11 +73,11 @@ def compile_HLG(lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
     max_token_id = max(lexicon.tokens)
     logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
     H = k2.ctc_topo(max_token_id)
-    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
 
     if Path(f"{lang_dir}/lm/{lm}.pt").is_file():
         logging.info(f"Loading pre-compiled {lm}")
-        d = torch.load(f"{lang_dir}/lm/{lm}.pt")
+        d = torch.load(f"{lang_dir}/lm/{lm}.pt", weights_only=False)
         G = k2.Fsa.from_dict(d)
     else:
         logging.info(f"Loading {lm}.fst.txt")

egs/commonvoice/ASR/local/compile_lg.py

@@ -68,11 +68,11 @@ def compile_LG(lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
       An FSA representing LG.
     """
     lexicon = Lexicon(lang_dir)
-    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
 
     if Path(f"{lang_dir}/lm/{lm}.pt").is_file():
         logging.info(f"Loading pre-compiled {lm}")
-        d = torch.load(f"{lang_dir}/lm/{lm}.pt")
+        d = torch.load(f"{lang_dir}/lm/{lm}.pt", weights_only=False)
         G = k2.Fsa.from_dict(d)
     else:
         logging.info(f"Loading {lm}.fst.txt")

egs/commonvoice/ASR/pruned_transducer_stateless7/decode.py

@@ -910,7 +910,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/commonvoice/ASR/pruned_transducer_stateless7/pretrained.py

@@ -247,7 +247,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/decode.py

@@ -767,7 +767,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/finetune.py

@@ -627,7 +627,7 @@ def load_model_params(
 
     """
     logging.info(f"Loading checkpoint from {ckpt}")
-    checkpoint = torch.load(ckpt, map_location="cpu")
+    checkpoint = torch.load(ckpt, map_location="cpu", weights_only=False)
 
     # if module list is empty, load the whole model from ckpt
     if not init_modules:

egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/generate_model_from_checkpoint.py

@@ -25,7 +25,7 @@ Usage:
     --exp-dir ./pruned_transducer_stateless7/exp
 
 It will generate a file `epoch-28-avg-15-use-averaged-model.pt` in the given `exp_dir`.
-You can later load it by `torch.load("epoch-28-avg-15-use-averaged-model.pt")`.
+You can later load it by `torch.load("epoch-28-avg-15-use-averaged-model.pt", weights_only=False)`.
 
 (2) use the averaged model with checkpoint exp_dir/checkpoint-iter.pt
 ./pruned_transducer_stateless7/generate_model_from_checkpoint.py \
@@ -35,7 +35,7 @@ You can later load it by `torch.load("epoch-28-avg-15-use-averaged-model.pt")`.
     --exp-dir ./pruned_transducer_stateless7/exp
 
 It will generate a file `iter-22000-avg-5-use-averaged-model.pt` in the given `exp_dir`.
-You can later load it by `torch.load("iter-22000-avg-5-use-averaged-model.pt")`.
+You can later load it by `torch.load("iter-22000-avg-5-use-averaged-model.pt", weights_only=False)`.
 
 (3) use the original model with checkpoint exp_dir/epoch-xxx.pt
 ./pruned_transducer_stateless7/generate_model_from_checkpoint.py \
@@ -45,7 +45,7 @@ You can later load it by `torch.load("iter-22000-avg-5-use-averaged-model.pt")`.
     --exp-dir ./pruned_transducer_stateless7/exp
 
 It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`.
-You can later load it by `torch.load("epoch-28-avg-15.pt")`.
+You can later load it by `torch.load("epoch-28-avg-15.pt", weights_only=False)`.
 
 (4) use the original model with checkpoint exp_dir/checkpoint-iter.pt
 ./pruned_transducer_stateless7/generate_model_from_checkpoint.py \
@@ -55,7 +55,7 @@ You can later load it by `torch.load("epoch-28-avg-15.pt")`.
     --exp-dir ./pruned_transducer_stateless7/exp
 
 It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`.
-You can later load it by `torch.load("iter-22000-avg-5.pt")`.
+You can later load it by `torch.load("iter-22000-avg-5.pt", weights_only=False)`.
 """
 
 

egs/commonvoice/ASR/zipformer/decode.py

@@ -987,7 +987,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/commonvoice/ASR/zipformer/decode_char.py

@@ -756,7 +756,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/csj/ASR/pruned_transducer_stateless7_streaming/decode.py

@@ -791,7 +791,7 @@ def main():
 
     if params.decoding_graph:
         decoding_graph = k2.Fsa.from_dict(
-            torch.load(params.decoding_graph, map_location=device)
+            torch.load(params.decoding_graph, map_location=device, weights_only=False)
         )
     elif "fast_beam_search" in params.decoding_method:
         if params.decoding_method == "fast_beam_search_nbest_LG":
@@ -800,7 +800,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/csj/ASR/pruned_transducer_stateless7_streaming/pretrained.py

@@ -239,7 +239,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/csj/ASR/pruned_transducer_stateless7_streaming/streaming_decode.py

@@ -561,7 +561,7 @@ def main():
     decoding_graph = None
     if params.decoding_graph:
         decoding_graph = k2.Fsa.from_dict(
-            torch.load(params.decoding_graph, map_location=device)
+            torch.load(params.decoding_graph, map_location=device, weights_only=False)
         )
     elif params.decoding_method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)

egs/fluent_speech_commands/SLU/local/compile_hlg.py

@@ -47,7 +47,7 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
     max_token_id = max(lexicon.tokens)
     logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
     H = k2.ctc_topo(max_token_id)
-    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
 
     logging.info("Loading G.fst.txt")
     with open(lang_dir / "G.fst.txt") as f:

egs/fluent_speech_commands/SLU/local/prepare_lang.py

@@ -14,7 +14,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/gigaspeech/ASR/conformer_ctc/decode.py

@@ -589,7 +589,7 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 
@@ -628,7 +628,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False)
             G = k2.Fsa.from_dict(d)
 
         if params.method in ["whole-lattice-rescoring", "attention-decoder"]:

egs/gigaspeech/ASR/zipformer/ctc_decode.py

@@ -668,7 +668,7 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 
@@ -707,7 +707,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False)
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method == "whole-lattice-rescoring":

egs/gigaspeech/ASR/zipformer/decode.py

@@ -1000,7 +1000,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/gigaspeech/KWS/zipformer/decode-asr.py

@@ -1001,7 +1001,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/gigaspeech/KWS/zipformer/finetune.py

@@ -183,7 +183,7 @@ def load_model_params(
 
     """
     logging.info(f"Loading checkpoint from {ckpt}")
-    checkpoint = torch.load(ckpt, map_location="cpu")
+    checkpoint = torch.load(ckpt, map_location="cpu", weights_only=False)
 
     # if module list is empty, load the whole model from ckpt
     if not init_modules:

egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/decode.py

@@ -938,7 +938,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/ksponspeech/ASR/zipformer/ctc_decode.py

@@ -666,7 +666,7 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 
@@ -705,7 +705,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False)
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method == "whole-lattice-rescoring":

egs/ksponspeech/ASR/zipformer/decode.py

@@ -989,7 +989,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/libricss/SURT/dprnn_zipformer/pretrained.py

@@ -177,7 +177,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/libricss/SURT/dprnn_zipformer/train.py

@@ -1286,7 +1286,7 @@ def run(rank, world_size, args):
         logging.info(
             f"Initializing model with checkpoint from {params.model_init_ckpt}"
         )
-        init_ckpt = torch.load(params.model_init_ckpt, map_location=device)
+        init_ckpt = torch.load(params.model_init_ckpt, map_location=device, weights_only=False)
         model.load_state_dict(init_ckpt["model"], strict=False)
 
     if world_size > 1:

egs/libricss/SURT/dprnn_zipformer/train_adapt.py

@@ -1175,7 +1175,7 @@ def run(rank, world_size, args):
         logging.info(
             f"Initializing model with checkpoint from {params.model_init_ckpt}"
         )
-        init_ckpt = torch.load(params.model_init_ckpt, map_location=device)
+        init_ckpt = torch.load(params.model_init_ckpt, map_location=device, weights_only=False)
         model.load_state_dict(init_ckpt["model"], strict=True)
 
     if world_size > 1:

egs/libriheavy/ASR/zipformer_prompt_asr/pretrained.py

@@ -252,7 +252,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/librilight/SSL/zipformer/decode.py

@@ -960,7 +960,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/librilight/SSL/zipformer/finetune.py

@@ -750,7 +750,7 @@ def _to_int_tuple(s: str):
 def get_encoder_model(params: AttributeDict) -> nn.Module:
     if hasattr(params, "pretrained_dir"):
         logging.info(f"Loading {params.pretrained_dir}")
-        pretrained = torch.load(params.pretrained_dir)
+        pretrained = torch.load(params.pretrained_dir, weights_only=False)
         encoder = HubertModel(params)
         encoder.load_state_dict(pretrained["model"])
     else:

egs/librispeech/ASR/local/prepare_lang.py

@@ -28,7 +28,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py

@@ -264,7 +264,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/librispeech/ASR/transducer_stateless/pretrained.py

@@ -234,7 +234,7 @@ def main():
     logging.info("Creating model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/librispeech/ASR/transducer_stateless2/pretrained.py

@@ -234,7 +234,7 @@ def main():
     logging.info("Creating model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/librispeech/ASR/transducer_stateless_multi_datasets/pretrained.py

@@ -234,7 +234,7 @@ def main():
     logging.info("Creating model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/librispeech/SSL/hubert/decode.py

@@ -962,7 +962,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/librispeech/SSL/hubert/decode_ce.py

@@ -962,7 +962,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/librispeech/SSL/hubert/finetune.py

@@ -451,7 +451,7 @@ def _to_int_tuple(s: str):
 def get_encoder_model(params: AttributeDict) -> nn.Module:
     if hasattr(params, "pretrained_dir"):
         logging.info(f"Loading {params.pretrained_dir}")
-        pretrained = torch.load(params.pretrained_dir)
+        pretrained = torch.load(params.pretrained_dir, weights_only=False)
         encoder = HubertModel(params)
         encoder.load_state_dict(pretrained["model"])
     else:

egs/librispeech/SSL/hubert/finetune_ce.py

@@ -451,7 +451,7 @@ def _to_int_tuple(s: str):
 def get_encoder_model(params: AttributeDict) -> nn.Module:
     if hasattr(params, "pretrained_dir"):
         logging.info(f"Loading {params.pretrained_dir}")
-        pretrained = torch.load(params.pretrained_dir)
+        pretrained = torch.load(params.pretrained_dir, weights_only=False)
         encoder = HubertModel(params)
         encoder.load_state_dict(pretrained["model"])
     else:

egs/librispeech/SSL/local/convert_checkpoint_from_fairseq.py

@@ -12,7 +12,7 @@ args = parser.parse_args()
 src = args.src
 tgt = args.tgt
 
-old_checkpoint = torch.load(src)
+old_checkpoint = torch.load(src, weights_only=False)
 new_checkpoint = OrderedDict()
 new_checkpoint["model"] = old_checkpoint["model"]
 torch.save(new_checkpoint, tgt)

egs/librispeech/SSL/local/prepare_lang.py

@@ -28,7 +28,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/librispeech/SSL/zipformer/decode.py

@@ -960,7 +960,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/librispeech/SSL/zipformer/finetune.py

@@ -750,7 +750,7 @@ def _to_int_tuple(s: str):
 def get_encoder_model(params: AttributeDict) -> nn.Module:
     if hasattr(params, "pretrained_dir"):
         logging.info(f"Loading {params.pretrained_dir}")
-        pretrained = torch.load(params.pretrained_dir)
+        pretrained = torch.load(params.pretrained_dir, weights_only=False)
         encoder = HubertModel(params)
         encoder.load_state_dict(pretrained["model"])
     else:

egs/librispeech/WSASR/conformer_ctc2/decode.py

@@ -578,7 +578,7 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 

egs/librispeech/WSASR/conformer_ctc2/decode_phone.py

@@ -457,7 +457,7 @@ def main():
 
     params.num_classes = num_classes
 
-    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
+    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu", weights_only=False))
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 

egs/librispeech/WSASR/local/compile_hlg.py

@@ -78,11 +78,11 @@ def compile_HLG(lm_dir: str, lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
     max_token_id = max(lexicon.tokens)
     logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
     H = k2.ctc_topo(max_token_id)
-    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
 
     if Path(f"{lm_dir}/{lm}.pt").is_file():
         logging.info(f"Loading pre-compiled {lm}")
-        d = torch.load(f"{lm_dir}/{lm}.pt")
+        d = torch.load(f"{lm_dir}/{lm}.pt", weights_only=False)
         G = k2.Fsa.from_dict(d)
     else:
         logging.info(f"Loading {lm}.fst.txt")

egs/librispeech/WSASR/local/prepare_lang.py

@@ -28,7 +28,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/librispeech/WSASR/local/prepare_otc_lang.py

@@ -29,7 +29,7 @@ consisting of words and tokens (i.e., phones) and does the following:
 
 4. Generate L.pt, in k2 format. It can be loaded by
 
-        d = torch.load("L.pt")
+        d = torch.load("L.pt", weights_only=False)
         lexicon = k2.Fsa.from_dict(d)
 
 5. Generate L_disambig.pt, in k2 format.

egs/libritts/ASR/zipformer/ctc_decode.py

@@ -802,7 +802,7 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 
@@ -842,7 +842,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False)
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method in [

egs/libritts/ASR/zipformer/decode.py

@@ -1014,7 +1014,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/ljspeech/TTS/matcha/hifigan/xutils.py

@@ -41,7 +41,7 @@ def get_padding(kernel_size, dilation=1):
 def load_checkpoint(filepath, device):
     assert os.path.isfile(filepath)
     print(f"Loading '{filepath}'")
-    checkpoint_dict = torch.load(filepath, map_location=device)
+    checkpoint_dict = torch.load(filepath, map_location=device, weights_only=False)
     print("Complete.")
     return checkpoint_dict
 

egs/ljspeech/TTS/matcha/infer.py

@@ -103,7 +103,7 @@ def load_vocoder(checkpoint_path: Path) -> nn.Module:
 
     hifigan = HiFiGAN(h).to("cpu")
     hifigan.load_state_dict(
-        torch.load(checkpoint_path, map_location="cpu")["generator"]
+        torch.load(checkpoint_path, map_location="cpu", weights_only=False)["generator"]
     )
     _ = hifigan.eval()
     hifigan.remove_weight_norm()

egs/mdcc/ASR/zipformer/decode.py

@@ -756,7 +756,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/mgb2/ASR/conformer_ctc/decode.py

@@ -575,7 +575,7 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False)
         )
         assert HLG.requires_grad is False
 
@@ -614,7 +614,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False)
             G = k2.Fsa.from_dict(d)
 
         if params.method in ["whole-lattice-rescoring", "attention-decoder"]:

egs/mgb2/ASR/conformer_ctc/pretrained.py

@@ -275,7 +275,7 @@ def main():
         use_feat_batchnorm=params.use_feat_batchnorm,
     )
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -347,7 +347,7 @@ def main():
         "attention-decoder",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu", weights_only=False))
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -358,7 +358,7 @@ def main():
             "attention-decoder",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu", weights_only=False))
             # Add epsilon self-loops to G as we will compose
             # it with the whole lattice later
             G = G.to(device)

egs/mgb2/ASR/pruned_transducer_stateless5/pretrained.py

@@ -236,7 +236,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/multi_ja_en/ASR/zipformer/decode.py

@@ -733,7 +733,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/multi_zh-hans/ASR/whisper/decode.py

@@ -90,10 +90,10 @@ def average_checkpoints(
     """
     n = len(filenames)
 
-    if "model" in torch.load(filenames[0], map_location=device):
-        avg = torch.load(filenames[0], map_location=device)["model"]
+    if "model" in torch.load(filenames[0], map_location=device, weights_only=False):
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)["model"]
     else:
-        avg = torch.load(filenames[0], map_location=device)
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)
 
     # Identify shared parameters. Two parameters are said to be shared
     # if they have the same data_ptr
@@ -108,10 +108,10 @@ def average_checkpoints(
     uniqued_names = list(uniqued.values())
 
     for i in range(1, n):
-        if "model" in torch.load(filenames[i], map_location=device):
-            state_dict = torch.load(filenames[i], map_location=device)["model"]
+        if "model" in torch.load(filenames[i], map_location=device, weights_only=False):
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)["model"]
         else:
-            state_dict = torch.load(filenames[i], map_location=device)
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)
         for k in uniqued_names:
             avg[k] += state_dict[k]
 
@@ -484,7 +484,7 @@ def main():
             start = params.epoch - params.avg
             assert start >= 1, start
             checkpoint = torch.load(
-                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
             )
             if "model" not in checkpoint:
                 # deepspeed converted checkpoint only contains model state_dict
@@ -513,7 +513,7 @@ def main():
             torch.save(model.state_dict(), filename)
         else:
             checkpoint = torch.load(
-                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
             )
             if "model" not in checkpoint:
                 model.load_state_dict(checkpoint, strict=True)

egs/multi_zh-hans/ASR/whisper/train.py

@@ -809,7 +809,7 @@ def run(rank, world_size, args):
     del model.alignment_heads
 
     if params.pretrained_model_path:
-        checkpoint = torch.load(params.pretrained_model_path, map_location="cpu")
+        checkpoint = torch.load(params.pretrained_model_path, map_location="cpu", weights_only=False)
         if "model" not in checkpoint:
             model.load_state_dict(checkpoint, strict=True)
         else:

egs/multi_zh-hans/ASR/zipformer/decode.py

@@ -784,7 +784,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/multi_zh-hans/ASR/zipformer/generate_averaged_model.py

@@ -24,7 +24,7 @@ Usage:
     --exp-dir ./zipformer/exp
 
 It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`.
-You can later load it by `torch.load("epoch-28-avg-15.pt")`.
+You can later load it by `torch.load("epoch-28-avg-15.pt", weights_only=False)`.
 
 (2) use the checkpoint exp_dir/checkpoint-iter.pt
 ./zipformer/generate_averaged_model.py \
@@ -33,7 +33,7 @@ You can later load it by `torch.load("epoch-28-avg-15.pt")`.
     --exp-dir ./zipformer/exp
 
 It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`.
-You can later load it by `torch.load("iter-22000-avg-5.pt")`.
+You can later load it by `torch.load("iter-22000-avg-5.pt", weights_only=False)`.
 """
 
 

egs/multi_zh-hans/ASR/zipformer/pretrained.py

@@ -291,7 +291,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/multi_zh_en/ASR/zipformer/decode.py

@@ -792,7 +792,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/multi_zh_en/ASR/zipformer/generate_averaged_model.py

@@ -24,7 +24,7 @@ Usage:
     --exp-dir ./zipformer/exp
 
 It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`.
-You can later load it by `torch.load("epoch-28-avg-15.pt")`.
+You can later load it by `torch.load("epoch-28-avg-15.pt", weights_only=False)`.
 
 (2) use the checkpoint exp_dir/checkpoint-iter.pt
 ./zipformer/generate_averaged_model.py \
@@ -33,7 +33,7 @@ You can later load it by `torch.load("epoch-28-avg-15.pt")`.
     --exp-dir ./zipformer/exp
 
 It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`.
-You can later load it by `torch.load("iter-22000-avg-5.pt")`.
+You can later load it by `torch.load("iter-22000-avg-5.pt", weights_only=False)`.
 """
 
 

egs/multi_zh_en/ASR/zipformer/pretrained.py

@@ -294,7 +294,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()

egs/ptb/LM/local/sort_lm_training_data.py

@@ -64,7 +64,7 @@ def main():
     if out_lm_data.is_file():
         logging.warning(f"{out_lm_data} exists - skipping")
         return
-    data = torch.load(in_lm_data)
+    data = torch.load(in_lm_data, weights_only=False)
     words2bpe = data["words"]
     sentences = data["sentences"]
     sentence_lengths = data["sentence_lengths"]

egs/ptb/LM/local/test_prepare_lm_training_data.py

@@ -37,7 +37,7 @@ def main():
     sp = spm.SentencePieceProcessor()
     sp.load(str(bpe_model))
 
-    data = torch.load(lm_training_data)
+    data = torch.load(lm_training_data, weights_only=False)
     words2bpe = data["words"]
     sentences = data["sentences"]
 

egs/reazonspeech/ASR/zipformer/decode.py

@@ -1008,7 +1008,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:

egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py

@@ -95,10 +95,10 @@ def average_checkpoints(
     """
     n = len(filenames)
 
-    if "model" in torch.load(filenames[0], map_location=device):
-        avg = torch.load(filenames[0], map_location=device)["model"]
+    if "model" in torch.load(filenames[0], map_location=device, weights_only=False):
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)["model"]
     else:
-        avg = torch.load(filenames[0], map_location=device)
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)
 
     # Identify shared parameters. Two parameters are said to be shared
     # if they have the same data_ptr
@@ -113,10 +113,10 @@ def average_checkpoints(
     uniqued_names = list(uniqued.values())
 
     for i in range(1, n):
-        if "model" in torch.load(filenames[i], map_location=device):
-            state_dict = torch.load(filenames[i], map_location=device)["model"]
+        if "model" in torch.load(filenames[i], map_location=device, weights_only=False):
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)["model"]
         else:
-            state_dict = torch.load(filenames[i], map_location=device)
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)
         for k in uniqued_names:
             avg[k] += state_dict[k]
 
@@ -548,7 +548,7 @@ def main():
         # torch.save(avg_checkpoint, filename)
     else:
         checkpoint = torch.load(
-            f"{params.exp_dir}/epoch-{params.epoch}/pytorch_model.bin",
+            f"{params.exp_dir}/epoch-{params.epoch}/pytorch_model.bin", weights_only=False,
             map_location="cpu",
         )
         model.load_state_dict(checkpoint, strict=False)

egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py

@@ -652,7 +652,7 @@ def run(rank, world_size, args):
     )
 
     if params.pretrained_model_path:
-        checkpoint = torch.load(params.pretrained_model_path, map_location="cpu")
+        checkpoint = torch.load(params.pretrained_model_path, map_location="cpu", weights_only=False)
         missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False)
 
     num_param = sum([p.numel() for p in model.parameters()])
@@ -704,7 +704,7 @@ def run(rank, world_size, args):
 
     sampler_state_dict = None
     if params.sampler_state_dict_path:
-        sampler_state_dict = torch.load(params.sampler_state_dict_path)
+        sampler_state_dict = torch.load(params.sampler_state_dict_path, weights_only=False)
         sampler_state_dict["max_duration"] = params.max_duration
 
     train_dl = data_module.train_dataloaders(

egs/speechio/ASR/whisper/decode.py

@@ -91,10 +91,10 @@ def average_checkpoints(
     """
     n = len(filenames)
 
-    if "model" in torch.load(filenames[0], map_location=device):
-        avg = torch.load(filenames[0], map_location=device)["model"]
+    if "model" in torch.load(filenames[0], map_location=device, weights_only=False):
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)["model"]
     else:
-        avg = torch.load(filenames[0], map_location=device)
+        avg = torch.load(filenames[0], map_location=device, weights_only=False)
 
     # Identify shared parameters. Two parameters are said to be shared
     # if they have the same data_ptr
@@ -109,10 +109,10 @@ def average_checkpoints(
     uniqued_names = list(uniqued.values())
 
     for i in range(1, n):
-        if "model" in torch.load(filenames[i], map_location=device):
-            state_dict = torch.load(filenames[i], map_location=device)["model"]
+        if "model" in torch.load(filenames[i], map_location=device, weights_only=False):
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)["model"]
         else:
-            state_dict = torch.load(filenames[i], map_location=device)
+            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)
         for k in uniqued_names:
             avg[k] += state_dict[k]
 
@@ -447,7 +447,7 @@ def main():
             start = params.epoch - params.avg
             assert start >= 1, start
             checkpoint = torch.load(
-                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
             )
             if "model" not in checkpoint:
                 # deepspeed converted checkpoint only contains model state_dict
@@ -476,7 +476,7 @@ def main():
             torch.save(model.state_dict(), filename)
         else:
             checkpoint = torch.load(
-                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
             )
             if "model" not in checkpoint:
                 model.load_state_dict(checkpoint, strict=True)