Trainer() issue: AttributeError:` 'str' object `has no attribute 'dtype'
System Infotransformersversion: 4.30.0.dev0Platform: Linux-5.4.204-ql-generic-12.0-19-x86_64-with-glibc2.31Python version: 3.11.3Huggingface_hub version: 0.14.1Safetensors version: 0.3.1PyTorch version (GPU?): 2.0.1 (True)Versions of relevant libraries:[pip3] numpy==1.23.0[pip3] torch==2.0.1[pip3] torchaudio==2.0.2[pip3] torchvision==0.15.2[conda] blas                      1.0                         mkl[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch[conda] mkl                       2023.1.0         h6d00ec8_46342[conda] mkl-service               2.4.0           py311h5eee18b_1[conda] mkl_fft                   1.3.6           py311ha02d727_1[conda] mkl_random                1.2.2           py311ha02d727_1[conda] numpy                     1.23.0                   pypi_0    pypi[conda] pytorch                   2.0.1           py3.11_cuda11.8_cudnn8.7.0_0    pytorch[conda] pytorch-cuda              11.8                 h7e8668a_5    pytorch[conda] pytorch-mutex             1.0                        cuda    pytorch[conda] torchaudio                2.0.2               py311_cu118    pytorch[conda] torchtriton               2.0.0                     py311    pytorch[conda] torchvision               0.15.2              py311_cu118    pytorchWho can help?@sgugger@sanchit-gandhiInformationThe official example scriptsMy own modified scriptsTasksAn officially supported task in theexamplesfolder (such as GLUE/SQuAD, …)My own task or dataset (give details below)ReproductionERROR:train_result =trainer.train(resume_from_checkpoint=checkpoint)…python3.11/site-packages/transformers/feature_extraction_sequence_utils.py", line 220, in padif value.dtype is np.dtype(np.float64):^^^^^^^^^^^AttributeError:'str' objecthas no attribute ‘dtype’I am not sure which element of the dataset is read as ‘str’1. OFFICIAL SCRIPT:transformers/examples/pytorch/audio-classification/run_audio_classification.py2. LOADED DATASET:DatasetDict({train: Dataset({features: ['audio','label'],num_rows: 1280})validation: Dataset({features: [‘audio’, ‘label’],num_rows: 160})test: Dataset({features: [‘audio’, ‘label’],num_rows: 160})3. logger.info(raw_datasets[‘train’][0]){‘audio’: {'path': ‘/transformers/examples/pytorch/audio-classification/s/data/s/s/train/audio1.wav’,'array': array([0.02072144, 0.02767944, 0.03274536, …, 0.00079346, 0.00088501,0.00149536]), ‘sampling_rate’: 16000},'label': ‘happy’}@mariosaskoany idea about this?Expected behaviorload the dataset to model for training  in  train_result =trainer.train(resume_from_checkpoint=checkpoint)