 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.*  *Note: The feature extractor depends on [torchaudio](https://github.com/pytorch/audio) and the tokenizer depends on [sentencepiece](https://github.com/google/sentencepiece) so be sure to install those packages before running the examples.*  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  model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")  ds = load_dataset(     "patrickvonplaten/librispeech_asr_dummy",     "clean",     split="validation" )  input_features = processor(     ds[0]["audio"]["array"],     sampling_rate=16_000,     return_tensors="pt" ).input_features  