signal. It's a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively. The
generate() method can be used for inference.
The [Speech2TextFeatureExtractor] class is responsible for extracting the log-mel filter-bank
features. The [Speech2TextProcessor] wraps [Speech2TextFeatureExtractor] and
[Speech2TextTokenizer] into a single instance to both extract the input features and decode the
predicted token ids.
The feature extractor depends on torchaudio and the tokenizer depends on sentencepiece so be sure to
install those packages before running the examples. You could either install those as extra speech dependencies with
pip install transformers"[speech, sentencepiece]" or install the packages seperately with pip install torchaudio sentencepiece. Also torchaudio requires the development version of the libsndfile package which can be installed via a system package manager. On Ubuntu it can
