 How to use  As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model.  *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio)  to extract the filter bank features. Make sure to install the `torchaudio` package before running this example.*  You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly  with `pip install torchaudio sentencepiece`.   ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf  model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-librispeech-asr") processor = Speech2Textprocessor.from_pretrained("facebook/s2t-medium-librispeech-asr")  def map_to_array(batch):     speech, _ = sf.read(batch["file"])     batch["speech"] = speech     return batch  ds = load_dataset(     "patrickvonplaten/librispeech_asr_dummy",     "clean",     split="validation" ) ds = ds.map(map_to_array)  input_features = processor(     ds["speech"][0],     sampling_rate=16_000,     return_tensors="pt" ).input_features  