Error: operands could not be broadcast together
Hi! I’m currently trying to train a SpeechT5 model on an ASR task. I have everything set up but it still get an error when i try to map my data: ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (2,2)  and requested shape (1,2). Im using the SpeechT5 Processor to prepare and pad my data for the model but i dont know why this error still occurs, i tried many thnigs to fix it… This is my code, any help would be really appreceated!feature_extractor = SpeechT5FeatureExtractor(sampling_rate=16000,num_mel_bins=128,feature_size=128,win_length=32,padding_value=3,return_attention_mask=True,do_normalize=False,hop_length=16,win_function=“hann_window”,fmin=50,fmax=7600,mel_floor=1e-10,)processor = SpeechT5Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)valid_dataset = valid_dataset.map(lambda example: {‘array’: [example[‘audio’][‘array’]], **example})print(valid_dataset)train_dataset = train_dataset.map(lambda example: {‘array’: [example[‘audio’][‘array’]], **example})print(train_dataset)def preprocess_function(examples):audio_arrays = examples[‘array’]text_list = examples['transcription']

input_data = processor(
    audio=audio_arrays,
    text_target=text_list,
    sampling_rate=16000,
    # return_tensors='pt'
)
return input_datavalid_dataset = valid_dataset.map(preprocess_function, batched=False)valid_dataset = valid_dataset.remove_columns([‘audio’, ‘transcription’, ‘array’, ‘attention_mask’, ‘decoder_attention_mask’])train_dataset = train_dataset.map(preprocess_function, batched=False)train_dataset = train_dataset.remove_columns([‘audio’, ‘transcription’, ‘array’, ‘attention_mask’, ‘decoder_attention_mask’])print(train_dataset)config = SpeechT5Config(return_dict=False,sampling_rate=16000,vocab_size=tokenizer.vocab_size,use_cache=True,activation_function=‘relu’,max_target_positions=2048,max_source_positions=160000,input_feat_per_channel=80,pad_token_id=3,eos_token_id=2,bos_token_id=1,)@dataclassclass DataCollatorForSeq2Seq:processor: processorpadding: Union[bool, str] = "longest"

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

    # different padding methods

    input_features = [{"input_values": feature["input_values"][0]} for feature in features]

    label_features = [{"input_ids": feature["labels"]} for feature in features]

    batch = self.processor.pad(input_values=input_features, padding=True, return_tensors="pt", return_attention_mask=True)

    labels_batch = self.processor.pad(labels=label_features, padding=True, return_tensors="pt", return_attention_mask=True)

    # 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

    return batchdata_collator = DataCollatorForSeq2Seq(processor=processor, padding=‘longest’)The error occurs in the feature_extractor.pad() function