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|
| import itertools as it |
| from typing import Any, Dict, List |
|
|
| import torch |
| from fairseq.data.dictionary import Dictionary |
| from fairseq.models.fairseq_model import FairseqModel |
|
|
|
|
| class BaseDecoder: |
| def __init__(self, tgt_dict: Dictionary) -> None: |
| self.tgt_dict = tgt_dict |
| self.vocab_size = len(tgt_dict) |
|
|
| self.blank = ( |
| tgt_dict.index("<ctc_blank>") |
| if "<ctc_blank>" in tgt_dict.indices |
| else tgt_dict.bos() |
| ) |
| if "<sep>" in tgt_dict.indices: |
| self.silence = tgt_dict.index("<sep>") |
| elif "|" in tgt_dict.indices: |
| self.silence = tgt_dict.index("|") |
| else: |
| self.silence = tgt_dict.eos() |
|
|
| def generate( |
| self, models: List[FairseqModel], sample: Dict[str, Any], **unused |
| ) -> List[List[Dict[str, torch.LongTensor]]]: |
| encoder_input = { |
| k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" |
| } |
| emissions = self.get_emissions(models, encoder_input) |
| return self.decode(emissions) |
|
|
| def get_emissions( |
| self, |
| models: List[FairseqModel], |
| encoder_input: Dict[str, Any], |
| ) -> torch.FloatTensor: |
| model = models[0] |
| encoder_out = model(**encoder_input) |
| if hasattr(model, "get_logits"): |
| emissions = model.get_logits(encoder_out) |
| else: |
| emissions = model.get_normalized_probs(encoder_out, log_probs=True) |
| return emissions.transpose(0, 1).float().cpu().contiguous() |
|
|
| def get_tokens(self, idxs: torch.IntTensor) -> torch.LongTensor: |
| idxs = (g[0] for g in it.groupby(idxs)) |
| idxs = filter(lambda x: x != self.blank, idxs) |
| return torch.LongTensor(list(idxs)) |
|
|
| def decode( |
| self, |
| emissions: torch.FloatTensor, |
| ) -> List[List[Dict[str, torch.LongTensor]]]: |
| raise NotImplementedError |
|
|
