Getting this 'AttributeError: 'list' object has no attribute 'get'' error when trying to fine tune wav2vec2 model
My trained data strcutre is:Dataset({features: [‘input_values’, ‘input_length’, ‘labels’],num_rows: 100})where input_values representation vector generate from audio and labels are my transcription vector representation.this my padding class where I getting error in accessing this line        (  input_values = feature.get(“input_values”)  # Safely get the value of “input_values” ) . This error basically occurs when I trained the modelprocessor: Wav2Vec2Processor
padding: Union[bool, str] = True
max_length: Optional[int] = None
max_length_labels: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
pad_to_multiple_of_labels: Optional[int] = None

def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
    # split inputs and labels since they have to be of different lengths and need

    input_features = []
    for feature in features:
      input_values = feature.get("input_values")  # Safely get the value of "input_values"
      if input_values is not None:
        input_features.append({"input_values": input_values})

    # inpy = [{"input_values": give(feature["labels"])} for feature in features]
    label_features = [{"input_ids": feature["labels"]} for feature in features]

    batch = self.processor.pad(
        input_features,
        padding=self.padding,
        max_length=self.max_length,
        pad_to_multiple_of=self.pad_to_multiple_of,
        return_tensors="pt",
    )
    with self.processor.as_target_processor():
        labels_batch = self.processor.pad(
            label_features,
            padding=self.padding,
            max_length=self.max_length_labels,
            pad_to_multiple_of=self.pad_to_multiple_of_labels,
            return_tensors="pt",
        )

    # replace padding with -100 to ignore loss correctly
    labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)

    batch["labels"] = labels
    # print("Batch input:", batch["input_values"])
    # print("Batch labels:", batch["labels"])

    return batchthis is my trainer classfrom transformers import Trainer trainer = Trainer(     model=model,     data_collator=data_collator,     args=training_args,     compute_metrics=compute_metrics,     train_dataset=common_voice_train,     eval_dataset=common_voice_train,     tokenizer=processor.feature_extractor, )