up (#14154)

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -187,6 +187,13 @@ class DataTrainingArguments:
             "so that the cached datasets can consequently be loaded in distributed training"
         },
     )
+    use_auth_token: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "If :obj:`True`, will use the token generated when running"
+            ":obj:`transformers-cli logiin as HTTP bearer authorization for remote files."
+        },
+    )
 
 
 @dataclass
@@ -408,7 +415,9 @@ def main():
     # one local process can concurrently download model & vocab.
 
     # load config
-    config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
+    config = AutoConfig.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+    )
 
     # tokenizer is defined by `tokenizer_class` if present in config else by `model_type`
     config_for_tokenizer = config if config.tokenizer_class is not None else None
@@ -422,9 +431,10 @@ def main():
         unk_token="[UNK]",
         pad_token="[PAD]",
         word_delimiter_token="|",
+        use_auth_token=data_args.use_auth_token,
     )
     feature_extractor = AutoFeatureExtractor.from_pretrained(
-        model_args.model_name_or_path, cache_dir=model_args.cache_dir
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
     )
     processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
 
@@ -447,7 +457,10 @@ def main():
 
     # create model
     model = AutoModelForCTC.from_pretrained(
-        model_args.model_name_or_path, cache_dir=model_args.cache_dir, config=config
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        config=config,
+        use_auth_token=data_args.use_auth_token,
     )
 
     # freeze encoder