)

Given emissions of shape *(batch, time, num_tokens)*, the decoder will compute and return a List of batch Lists, each consisting of the nbest hypotheses corresponding to the emissions. Each hypothesis can be further broken down into tokens, words (if a lexicon is provided), score, and timesteps components.

```python
emissions = acoustic_model(waveforms)  # (B, T, N)
batch_hypotheses = decoder(emissions)  # List[List[CTCHypothesis]]

# transcript for a lexicon decoder
transcripts = [" ".join(hypo[0].words) for hypo in batch_hypotheses]

# transcript for a lexicon free decoder, splitting by sil token
batch_tokens = [decoder.idxs_to_tokens(hypo[0].tokens) for hypo in batch_hypotheses]
transcripts = ["".join(tokens) for tokens in batch_tokens]
