"""Beam search module."""
|
|
from itertools import chain
|
import logging
|
from typing import Any
|
from typing import Dict
|
from typing import List
|
from typing import NamedTuple
|
from typing import Tuple
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from typing import Union
|
|
import torch
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|
from funasr.modules.e2e_asr_common import end_detect
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from funasr.modules.scorers.scorer_interface import PartialScorerInterface
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from funasr.modules.scorers.scorer_interface import ScorerInterface
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|
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class Hypothesis(NamedTuple):
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"""Hypothesis data type."""
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yseq: torch.Tensor
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score: Union[float, torch.Tensor] = 0
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scores: Dict[str, Union[float, torch.Tensor]] = dict()
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states: Dict[str, Any] = dict()
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|
def asdict(self) -> dict:
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"""Convert data to JSON-friendly dict."""
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return self._replace(
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yseq=self.yseq.tolist(),
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score=float(self.score),
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scores={k: float(v) for k, v in self.scores.items()},
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)._asdict()
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class BeamSearch(torch.nn.Module):
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"""Beam search implementation."""
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def __init__(
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self,
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scorers: Dict[str, ScorerInterface],
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weights: Dict[str, float],
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beam_size: int,
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vocab_size: int,
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sos: int,
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eos: int,
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token_list: List[str] = None,
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pre_beam_ratio: float = 1.5,
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pre_beam_score_key: str = None,
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):
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"""Initialize beam search.
|
|
Args:
|
scorers (dict[str, ScorerInterface]): Dict of decoder modules
|
e.g., Decoder, CTCPrefixScorer, LM
|
The scorer will be ignored if it is `None`
|
weights (dict[str, float]): Dict of weights for each scorers
|
The scorer will be ignored if its weight is 0
|
beam_size (int): The number of hypotheses kept during search
|
vocab_size (int): The number of vocabulary
|
sos (int): Start of sequence id
|
eos (int): End of sequence id
|
token_list (list[str]): List of tokens for debug log
|
pre_beam_score_key (str): key of scores to perform pre-beam search
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pre_beam_ratio (float): beam size in the pre-beam search
|
will be `int(pre_beam_ratio * beam_size)`
|
|
"""
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super().__init__()
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# set scorers
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self.weights = weights
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self.scorers = dict()
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self.full_scorers = dict()
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self.part_scorers = dict()
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# this module dict is required for recursive cast
|
# `self.to(device, dtype)` in `recog.py`
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self.nn_dict = torch.nn.ModuleDict()
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for k, v in scorers.items():
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w = weights.get(k, 0)
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if w == 0 or v is None:
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continue
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assert isinstance(
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v, ScorerInterface
|
), f"{k} ({type(v)}) does not implement ScorerInterface"
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self.scorers[k] = v
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if isinstance(v, PartialScorerInterface):
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self.part_scorers[k] = v
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else:
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self.full_scorers[k] = v
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if isinstance(v, torch.nn.Module):
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self.nn_dict[k] = v
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# set configurations
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self.sos = sos
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self.eos = eos
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self.token_list = token_list
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self.pre_beam_size = int(pre_beam_ratio * beam_size)
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self.beam_size = beam_size
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self.n_vocab = vocab_size
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if (
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pre_beam_score_key is not None
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and pre_beam_score_key != "full"
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and pre_beam_score_key not in self.full_scorers
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):
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raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}")
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self.pre_beam_score_key = pre_beam_score_key
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self.do_pre_beam = (
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self.pre_beam_score_key is not None
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and self.pre_beam_size < self.n_vocab
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and len(self.part_scorers) > 0
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)
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def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]:
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"""Get an initial hypothesis data.
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Args:
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x (torch.Tensor): The encoder output feature
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Returns:
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Hypothesis: The initial hypothesis.
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"""
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init_states = dict()
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init_scores = dict()
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for k, d in self.scorers.items():
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init_states[k] = d.init_state(x)
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init_scores[k] = 0.0
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return [
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Hypothesis(
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score=0.0,
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scores=init_scores,
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states=init_states,
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yseq=torch.tensor([self.sos], device=x.device),
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)
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]
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@staticmethod
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def append_token(xs: torch.Tensor, x: int) -> torch.Tensor:
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"""Append new token to prefix tokens.
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Args:
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xs (torch.Tensor): The prefix token
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x (int): The new token to append
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Returns:
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torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device
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"""
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x = torch.tensor([x], dtype=xs.dtype, device=xs.device)
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return torch.cat((xs, x))
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def score_full(
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self, hyp: Hypothesis, x: torch.Tensor
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) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
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"""Score new hypothesis by `self.full_scorers`.
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Args:
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hyp (Hypothesis): Hypothesis with prefix tokens to score
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x (torch.Tensor): Corresponding input feature
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Returns:
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Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
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score dict of `hyp` that has string keys of `self.full_scorers`
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and tensor score values of shape: `(self.n_vocab,)`,
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and state dict that has string keys
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and state values of `self.full_scorers`
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|
"""
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scores = dict()
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states = dict()
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for k, d in self.full_scorers.items():
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scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x)
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return scores, states
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def score_partial(
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self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor
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) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
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"""Score new hypothesis by `self.part_scorers`.
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Args:
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hyp (Hypothesis): Hypothesis with prefix tokens to score
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ids (torch.Tensor): 1D tensor of new partial tokens to score
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x (torch.Tensor): Corresponding input feature
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Returns:
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Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
|
score dict of `hyp` that has string keys of `self.part_scorers`
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and tensor score values of shape: `(len(ids),)`,
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and state dict that has string keys
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and state values of `self.part_scorers`
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"""
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scores = dict()
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states = dict()
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for k, d in self.part_scorers.items():
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scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x)
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return scores, states
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def beam(
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self, weighted_scores: torch.Tensor, ids: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute topk full token ids and partial token ids.
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Args:
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weighted_scores (torch.Tensor): The weighted sum scores for each tokens.
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Its shape is `(self.n_vocab,)`.
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ids (torch.Tensor): The partial token ids to compute topk
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Returns:
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Tuple[torch.Tensor, torch.Tensor]:
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The topk full token ids and partial token ids.
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Their shapes are `(self.beam_size,)`
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"""
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# no pre beam performed
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if weighted_scores.size(0) == ids.size(0):
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top_ids = weighted_scores.topk(self.beam_size)[1]
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return top_ids, top_ids
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# mask pruned in pre-beam not to select in topk
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tmp = weighted_scores[ids]
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weighted_scores[:] = -float("inf")
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weighted_scores[ids] = tmp
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top_ids = weighted_scores.topk(self.beam_size)[1]
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local_ids = weighted_scores[ids].topk(self.beam_size)[1]
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return top_ids, local_ids
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@staticmethod
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def merge_scores(
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prev_scores: Dict[str, float],
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next_full_scores: Dict[str, torch.Tensor],
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full_idx: int,
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next_part_scores: Dict[str, torch.Tensor],
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part_idx: int,
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) -> Dict[str, torch.Tensor]:
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"""Merge scores for new hypothesis.
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Args:
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prev_scores (Dict[str, float]):
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The previous hypothesis scores by `self.scorers`
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next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers`
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full_idx (int): The next token id for `next_full_scores`
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next_part_scores (Dict[str, torch.Tensor]):
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scores of partial tokens by `self.part_scorers`
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part_idx (int): The new token id for `next_part_scores`
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Returns:
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Dict[str, torch.Tensor]: The new score dict.
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Its keys are names of `self.full_scorers` and `self.part_scorers`.
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Its values are scalar tensors by the scorers.
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"""
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new_scores = dict()
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for k, v in next_full_scores.items():
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new_scores[k] = prev_scores[k] + v[full_idx]
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for k, v in next_part_scores.items():
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new_scores[k] = prev_scores[k] + v[part_idx]
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return new_scores
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def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any:
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"""Merge states for new hypothesis.
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Args:
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states: states of `self.full_scorers`
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part_states: states of `self.part_scorers`
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part_idx (int): The new token id for `part_scores`
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Returns:
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Dict[str, torch.Tensor]: The new score dict.
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Its keys are names of `self.full_scorers` and `self.part_scorers`.
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Its values are states of the scorers.
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"""
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new_states = dict()
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for k, v in states.items():
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new_states[k] = v
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for k, d in self.part_scorers.items():
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new_states[k] = d.select_state(part_states[k], part_idx)
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return new_states
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def search(
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self, running_hyps: List[Hypothesis], x: torch.Tensor
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) -> List[Hypothesis]:
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"""Search new tokens for running hypotheses and encoded speech x.
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Args:
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running_hyps (List[Hypothesis]): Running hypotheses on beam
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x (torch.Tensor): Encoded speech feature (T, D)
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Returns:
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List[Hypotheses]: Best sorted hypotheses
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"""
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best_hyps = []
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part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam
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for hyp in running_hyps:
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# scoring
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weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device)
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scores, states = self.score_full(hyp, x)
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for k in self.full_scorers:
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weighted_scores += self.weights[k] * scores[k]
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# partial scoring
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if self.do_pre_beam:
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pre_beam_scores = (
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weighted_scores
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if self.pre_beam_score_key == "full"
|
else scores[self.pre_beam_score_key]
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)
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part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1]
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part_scores, part_states = self.score_partial(hyp, part_ids, x)
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for k in self.part_scorers:
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weighted_scores[part_ids] += self.weights[k] * part_scores[k]
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# add previous hyp score
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weighted_scores += hyp.score
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# update hyps
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for j, part_j in zip(*self.beam(weighted_scores, part_ids)):
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# will be (2 x beam at most)
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best_hyps.append(
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Hypothesis(
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score=weighted_scores[j],
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yseq=self.append_token(hyp.yseq, j),
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scores=self.merge_scores(
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hyp.scores, scores, j, part_scores, part_j
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),
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states=self.merge_states(states, part_states, part_j),
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)
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)
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# sort and prune 2 x beam -> beam
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best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[
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: min(len(best_hyps), self.beam_size)
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]
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return best_hyps
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def forward(
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self, x: torch.Tensor, maxlenratio: float = 0.0, minlenratio: float = 0.0
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) -> List[Hypothesis]:
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"""Perform beam search.
|
|
Args:
|
x (torch.Tensor): Encoded speech feature (T, D)
|
maxlenratio (float): Input length ratio to obtain max output length.
|
If maxlenratio=0.0 (default), it uses a end-detect function
|
to automatically find maximum hypothesis lengths
|
If maxlenratio<0.0, its absolute value is interpreted
|
as a constant max output length.
|
minlenratio (float): Input length ratio to obtain min output length.
|
|
Returns:
|
list[Hypothesis]: N-best decoding results
|
|
"""
|
# set length bounds
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if maxlenratio == 0:
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maxlen = x.shape[0]
|
elif maxlenratio < 0:
|
maxlen = -1 * int(maxlenratio)
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else:
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maxlen = max(1, int(maxlenratio * x.size(0)))
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minlen = int(minlenratio * x.size(0))
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logging.info("decoder input length: " + str(x.shape[0]))
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logging.info("max output length: " + str(maxlen))
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logging.info("min output length: " + str(minlen))
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# main loop of prefix search
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running_hyps = self.init_hyp(x)
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ended_hyps = []
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for i in range(maxlen):
|
logging.debug("position " + str(i))
|
best = self.search(running_hyps, x)
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# post process of one iteration
|
running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
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# end detection
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if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i):
|
logging.info(f"end detected at {i}")
|
break
|
if len(running_hyps) == 0:
|
logging.info("no hypothesis. Finish decoding.")
|
break
|
else:
|
logging.debug(f"remained hypotheses: {len(running_hyps)}")
|
|
nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True)
|
# check the number of hypotheses reaching to eos
|
if len(nbest_hyps) == 0:
|
logging.warning(
|
"there is no N-best results, perform recognition "
|
"again with smaller minlenratio."
|
)
|
return (
|
[]
|
if minlenratio < 0.1
|
else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1))
|
)
|
|
# report the best result
|
best = nbest_hyps[0]
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for k, v in best.scores.items():
|
logging.info(
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f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}"
|
)
|
logging.info(f"total log probability: {best.score:.2f}")
|
logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}")
|
logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}")
|
if self.token_list is not None:
|
logging.info(
|
"best hypo: "
|
+ "".join([self.token_list[x] for x in best.yseq[1:-1]])
|
+ "\n"
|
)
|
return nbest_hyps
|
|
def post_process(
|
self,
|
i: int,
|
maxlen: int,
|
maxlenratio: float,
|
running_hyps: List[Hypothesis],
|
ended_hyps: List[Hypothesis],
|
) -> List[Hypothesis]:
|
"""Perform post-processing of beam search iterations.
|
|
Args:
|
i (int): The length of hypothesis tokens.
|
maxlen (int): The maximum length of tokens in beam search.
|
maxlenratio (int): The maximum length ratio in beam search.
|
running_hyps (List[Hypothesis]): The running hypotheses in beam search.
|
ended_hyps (List[Hypothesis]): The ended hypotheses in beam search.
|
|
Returns:
|
List[Hypothesis]: The new running hypotheses.
|
|
"""
|
logging.debug(f"the number of running hypotheses: {len(running_hyps)}")
|
if self.token_list is not None:
|
logging.debug(
|
"best hypo: "
|
+ "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]])
|
)
|
# add eos in the final loop to avoid that there are no ended hyps
|
if i == maxlen - 1:
|
logging.info("adding <eos> in the last position in the loop")
|
running_hyps = [
|
h._replace(yseq=self.append_token(h.yseq, self.eos))
|
for h in running_hyps
|
]
|
|
# add ended hypotheses to a final list, and removed them from current hypotheses
|
# (this will be a problem, number of hyps < beam)
|
remained_hyps = []
|
for hyp in running_hyps:
|
if hyp.yseq[-1] == self.eos:
|
# e.g., Word LM needs to add final <eos> score
|
for k, d in chain(self.full_scorers.items(), self.part_scorers.items()):
|
s = d.final_score(hyp.states[k])
|
hyp.scores[k] += s
|
hyp = hyp._replace(score=hyp.score + self.weights[k] * s)
|
ended_hyps.append(hyp)
|
else:
|
remained_hyps.append(hyp)
|
return remained_hyps
|
|
|
def beam_search(
|
x: torch.Tensor,
|
sos: int,
|
eos: int,
|
beam_size: int,
|
vocab_size: int,
|
scorers: Dict[str, ScorerInterface],
|
weights: Dict[str, float],
|
token_list: List[str] = None,
|
maxlenratio: float = 0.0,
|
minlenratio: float = 0.0,
|
pre_beam_ratio: float = 1.5,
|
pre_beam_score_key: str = "full",
|
) -> list:
|
"""Perform beam search with scorers.
|
|
Args:
|
x (torch.Tensor): Encoded speech feature (T, D)
|
sos (int): Start of sequence id
|
eos (int): End of sequence id
|
beam_size (int): The number of hypotheses kept during search
|
vocab_size (int): The number of vocabulary
|
scorers (dict[str, ScorerInterface]): Dict of decoder modules
|
e.g., Decoder, CTCPrefixScorer, LM
|
The scorer will be ignored if it is `None`
|
weights (dict[str, float]): Dict of weights for each scorers
|
The scorer will be ignored if its weight is 0
|
token_list (list[str]): List of tokens for debug log
|
maxlenratio (float): Input length ratio to obtain max output length.
|
If maxlenratio=0.0 (default), it uses a end-detect function
|
to automatically find maximum hypothesis lengths
|
minlenratio (float): Input length ratio to obtain min output length.
|
pre_beam_score_key (str): key of scores to perform pre-beam search
|
pre_beam_ratio (float): beam size in the pre-beam search
|
will be `int(pre_beam_ratio * beam_size)`
|
|
Returns:
|
list: N-best decoding results
|
|
"""
|
ret = BeamSearch(
|
scorers,
|
weights,
|
beam_size=beam_size,
|
vocab_size=vocab_size,
|
pre_beam_ratio=pre_beam_ratio,
|
pre_beam_score_key=pre_beam_score_key,
|
sos=sos,
|
eos=eos,
|
token_list=token_list,
|
).forward(x=x, maxlenratio=maxlenratio, minlenratio=minlenratio)
|
return [h.asdict() for h in ret]
|
|
class BeamSearchScama(torch.nn.Module):
|
"""Beam search implementation."""
|
|
def __init__(
|
self,
|
scorers: Dict[str, ScorerInterface],
|
weights: Dict[str, float],
|
beam_size: int,
|
vocab_size: int,
|
sos: int,
|
eos: int,
|
token_list: List[str] = None,
|
pre_beam_ratio: float = 1.5,
|
pre_beam_score_key: str = None,
|
):
|
"""Initialize beam search.
|
|
Args:
|
scorers (dict[str, ScorerInterface]): Dict of decoder modules
|
e.g., Decoder, CTCPrefixScorer, LM
|
The scorer will be ignored if it is `None`
|
weights (dict[str, float]): Dict of weights for each scorers
|
The scorer will be ignored if its weight is 0
|
beam_size (int): The number of hypotheses kept during search
|
vocab_size (int): The number of vocabulary
|
sos (int): Start of sequence id
|
eos (int): End of sequence id
|
token_list (list[str]): List of tokens for debug log
|
pre_beam_score_key (str): key of scores to perform pre-beam search
|
pre_beam_ratio (float): beam size in the pre-beam search
|
will be `int(pre_beam_ratio * beam_size)`
|
|
"""
|
super().__init__()
|
# set scorers
|
self.weights = weights
|
self.scorers = dict()
|
self.full_scorers = dict()
|
self.part_scorers = dict()
|
# this module dict is required for recursive cast
|
# `self.to(device, dtype)` in `recog.py`
|
self.nn_dict = torch.nn.ModuleDict()
|
for k, v in scorers.items():
|
w = weights.get(k, 0)
|
if w == 0 or v is None:
|
continue
|
assert isinstance(
|
v, ScorerInterface
|
), f"{k} ({type(v)}) does not implement ScorerInterface"
|
self.scorers[k] = v
|
if isinstance(v, PartialScorerInterface):
|
self.part_scorers[k] = v
|
else:
|
self.full_scorers[k] = v
|
if isinstance(v, torch.nn.Module):
|
self.nn_dict[k] = v
|
|
# set configurations
|
self.sos = sos
|
self.eos = eos
|
self.token_list = token_list
|
self.pre_beam_size = int(pre_beam_ratio * beam_size)
|
self.beam_size = beam_size
|
self.n_vocab = vocab_size
|
if (
|
pre_beam_score_key is not None
|
and pre_beam_score_key != "full"
|
and pre_beam_score_key not in self.full_scorers
|
):
|
raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}")
|
self.pre_beam_score_key = pre_beam_score_key
|
self.do_pre_beam = (
|
self.pre_beam_score_key is not None
|
and self.pre_beam_size < self.n_vocab
|
and len(self.part_scorers) > 0
|
)
|
|
def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]:
|
"""Get an initial hypothesis data.
|
|
Args:
|
x (torch.Tensor): The encoder output feature
|
|
Returns:
|
Hypothesis: The initial hypothesis.
|
|
"""
|
init_states = dict()
|
init_scores = dict()
|
for k, d in self.scorers.items():
|
init_states[k] = d.init_state(x)
|
init_scores[k] = 0.0
|
return [
|
Hypothesis(
|
score=0.0,
|
scores=init_scores,
|
states=init_states,
|
yseq=torch.tensor([self.sos], device=x.device),
|
)
|
]
|
|
@staticmethod
|
def append_token(xs: torch.Tensor, x: int) -> torch.Tensor:
|
"""Append new token to prefix tokens.
|
|
Args:
|
xs (torch.Tensor): The prefix token
|
x (int): The new token to append
|
|
Returns:
|
torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device
|
|
"""
|
x = torch.tensor([x], dtype=xs.dtype, device=xs.device)
|
return torch.cat((xs, x))
|
|
def score_full(
|
self, hyp: Hypothesis,
|
x: torch.Tensor,
|
x_mask: torch.Tensor = None,
|
pre_acoustic_embeds: torch.Tensor = None,
|
) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
|
"""Score new hypothesis by `self.full_scorers`.
|
|
Args:
|
hyp (Hypothesis): Hypothesis with prefix tokens to score
|
x (torch.Tensor): Corresponding input feature
|
|
Returns:
|
Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
|
score dict of `hyp` that has string keys of `self.full_scorers`
|
and tensor score values of shape: `(self.n_vocab,)`,
|
and state dict that has string keys
|
and state values of `self.full_scorers`
|
|
"""
|
scores = dict()
|
states = dict()
|
for k, d in self.full_scorers.items():
|
scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x, x_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds)
|
return scores, states
|
|
def score_partial(
|
self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor
|
) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
|
"""Score new hypothesis by `self.part_scorers`.
|
|
Args:
|
hyp (Hypothesis): Hypothesis with prefix tokens to score
|
ids (torch.Tensor): 1D tensor of new partial tokens to score
|
x (torch.Tensor): Corresponding input feature
|
|
Returns:
|
Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
|
score dict of `hyp` that has string keys of `self.part_scorers`
|
and tensor score values of shape: `(len(ids),)`,
|
and state dict that has string keys
|
and state values of `self.part_scorers`
|
|
"""
|
scores = dict()
|
states = dict()
|
for k, d in self.part_scorers.items():
|
scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x)
|
return scores, states
|
|
def beam(
|
self, weighted_scores: torch.Tensor, ids: torch.Tensor
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Compute topk full token ids and partial token ids.
|
|
Args:
|
weighted_scores (torch.Tensor): The weighted sum scores for each tokens.
|
Its shape is `(self.n_vocab,)`.
|
ids (torch.Tensor): The partial token ids to compute topk
|
|
Returns:
|
Tuple[torch.Tensor, torch.Tensor]:
|
The topk full token ids and partial token ids.
|
Their shapes are `(self.beam_size,)`
|
|
"""
|
# no pre beam performed
|
if weighted_scores.size(0) == ids.size(0):
|
top_ids = weighted_scores.topk(self.beam_size)[1]
|
return top_ids, top_ids
|
|
# mask pruned in pre-beam not to select in topk
|
tmp = weighted_scores[ids]
|
weighted_scores[:] = -float("inf")
|
weighted_scores[ids] = tmp
|
top_ids = weighted_scores.topk(self.beam_size)[1]
|
local_ids = weighted_scores[ids].topk(self.beam_size)[1]
|
return top_ids, local_ids
|
|
@staticmethod
|
def merge_scores(
|
prev_scores: Dict[str, float],
|
next_full_scores: Dict[str, torch.Tensor],
|
full_idx: int,
|
next_part_scores: Dict[str, torch.Tensor],
|
part_idx: int,
|
) -> Dict[str, torch.Tensor]:
|
"""Merge scores for new hypothesis.
|
|
Args:
|
prev_scores (Dict[str, float]):
|
The previous hypothesis scores by `self.scorers`
|
next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers`
|
full_idx (int): The next token id for `next_full_scores`
|
next_part_scores (Dict[str, torch.Tensor]):
|
scores of partial tokens by `self.part_scorers`
|
part_idx (int): The new token id for `next_part_scores`
|
|
Returns:
|
Dict[str, torch.Tensor]: The new score dict.
|
Its keys are names of `self.full_scorers` and `self.part_scorers`.
|
Its values are scalar tensors by the scorers.
|
|
"""
|
new_scores = dict()
|
for k, v in next_full_scores.items():
|
new_scores[k] = prev_scores[k] + v[full_idx]
|
for k, v in next_part_scores.items():
|
new_scores[k] = prev_scores[k] + v[part_idx]
|
return new_scores
|
|
def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any:
|
"""Merge states for new hypothesis.
|
|
Args:
|
states: states of `self.full_scorers`
|
part_states: states of `self.part_scorers`
|
part_idx (int): The new token id for `part_scores`
|
|
Returns:
|
Dict[str, torch.Tensor]: The new score dict.
|
Its keys are names of `self.full_scorers` and `self.part_scorers`.
|
Its values are states of the scorers.
|
|
"""
|
new_states = dict()
|
for k, v in states.items():
|
new_states[k] = v
|
for k, d in self.part_scorers.items():
|
new_states[k] = d.select_state(part_states[k], part_idx)
|
return new_states
|
|
def search(
|
self, running_hyps: List[Hypothesis],
|
x: torch.Tensor,
|
x_mask: torch.Tensor = None,
|
pre_acoustic_embeds: torch.Tensor = None,
|
) -> List[Hypothesis]:
|
"""Search new tokens for running hypotheses and encoded speech x.
|
|
Args:
|
running_hyps (List[Hypothesis]): Running hypotheses on beam
|
x (torch.Tensor): Encoded speech feature (T, D)
|
|
Returns:
|
List[Hypotheses]: Best sorted hypotheses
|
|
"""
|
best_hyps = []
|
part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam
|
for hyp in running_hyps:
|
# scoring
|
weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device)
|
scores, states = self.score_full(hyp, x, x_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds)
|
for k in self.full_scorers:
|
weighted_scores += self.weights[k] * scores[k]
|
# partial scoring
|
if self.do_pre_beam:
|
pre_beam_scores = (
|
weighted_scores
|
if self.pre_beam_score_key == "full"
|
else scores[self.pre_beam_score_key]
|
)
|
part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1]
|
part_scores, part_states = self.score_partial(hyp, part_ids, x)
|
for k in self.part_scorers:
|
weighted_scores[part_ids] += self.weights[k] * part_scores[k]
|
# add previous hyp score
|
weighted_scores += hyp.score
|
|
# update hyps
|
for j, part_j in zip(*self.beam(weighted_scores, part_ids)):
|
# will be (2 x beam at most)
|
best_hyps.append(
|
Hypothesis(
|
score=weighted_scores[j],
|
yseq=self.append_token(hyp.yseq, j),
|
scores=self.merge_scores(
|
hyp.scores, scores, j, part_scores, part_j
|
),
|
states=self.merge_states(states, part_states, part_j),
|
)
|
)
|
|
# sort and prune 2 x beam -> beam
|
best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[
|
: min(len(best_hyps), self.beam_size)
|
]
|
return best_hyps
|
|
def forward(
|
self, x: torch.Tensor,
|
scama_mask: torch.Tensor = None,
|
pre_acoustic_embeds: torch.Tensor = None,
|
maxlenratio: float = 0.0,
|
minlenratio: float = 0.0,
|
maxlen: int = None,
|
minlen: int = 0,
|
) -> List[Hypothesis]:
|
"""Perform beam search.
|
|
Args:
|
x (torch.Tensor): Encoded speech feature (T, D)
|
maxlenratio (float): Input length ratio to obtain max output length.
|
If maxlenratio=0.0 (default), it uses a end-detect function
|
to automatically find maximum hypothesis lengths
|
If maxlenratio<0.0, its absolute value is interpreted
|
as a constant max output length.
|
minlenratio (float): Input length ratio to obtain min output length.
|
|
Returns:
|
list[Hypothesis]: N-best decoding results
|
|
"""
|
if maxlen is None:
|
# set length bounds
|
if maxlenratio == 0:
|
maxlen = x.shape[0]
|
elif maxlenratio < 0:
|
maxlen = -1 * int(maxlenratio)
|
else:
|
maxlen = max(1, int(maxlenratio * x.size(0)))
|
minlen = int(minlenratio * x.size(0))
|
|
logging.info("decoder input length: " + str(x.shape[0]))
|
logging.info("max output length: " + str(maxlen))
|
logging.info("min output length: " + str(minlen))
|
|
# main loop of prefix search
|
running_hyps = self.init_hyp(x)
|
ended_hyps = []
|
for i in range(maxlen):
|
logging.debug("position " + str(i))
|
mask_enc = None
|
if scama_mask is not None:
|
token_num_predictor = scama_mask.size(1)
|
token_id_slice = min(i, token_num_predictor-1)
|
mask_enc = scama_mask[:, token_id_slice:token_id_slice+1, :]
|
# if mask_enc.size(1) == 0:
|
# mask_enc = scama_mask[:, -2:-1, :]
|
# # mask_enc = torch.zeros_like(mask_enc)
|
pre_acoustic_embeds_cur = None
|
if pre_acoustic_embeds is not None:
|
b, t, d = pre_acoustic_embeds.size()
|
pad = torch.zeros((b, 1, d), dtype=pre_acoustic_embeds.dtype).to(device=pre_acoustic_embeds.device)
|
pre_acoustic_embeds = torch.cat((pre_acoustic_embeds, pad), dim=1)
|
token_id_slice = min(i, t)
|
pre_acoustic_embeds_cur = pre_acoustic_embeds[:, token_id_slice:token_id_slice+1, :]
|
|
best = self.search(running_hyps, x, x_mask=mask_enc, pre_acoustic_embeds=pre_acoustic_embeds_cur)
|
# post process of one iteration
|
running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
|
# end detection
|
if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i):
|
logging.info(f"end detected at {i}")
|
break
|
if len(running_hyps) == 0:
|
logging.info("no hypothesis. Finish decoding.")
|
break
|
else:
|
logging.debug(f"remained hypotheses: {len(running_hyps)}")
|
|
nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True)
|
# check the number of hypotheses reaching to eos
|
if len(nbest_hyps) == 0:
|
logging.warning(
|
"there is no N-best results, perform recognition "
|
"again with smaller minlenratio."
|
)
|
return (
|
[]
|
if minlenratio < 0.1
|
else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1))
|
)
|
|
# report the best result
|
for x in nbest_hyps:
|
yseq = "".join([self.token_list[x] for x in x.yseq])
|
logging.debug("nbest: y: {}, yseq: {}, score: {}".format(x.yseq, yseq, x.score))
|
best = nbest_hyps[0]
|
for k, v in best.scores.items():
|
logging.info(
|
f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}"
|
)
|
logging.info(f"total log probability: {best.score:.2f}")
|
logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}")
|
logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}")
|
if self.token_list is not None:
|
logging.info(
|
"best hypo: "
|
+ "".join([self.token_list[x] for x in best.yseq[1:-1]])
|
+ "\n"
|
)
|
return nbest_hyps
|
|
def post_process(
|
self,
|
i: int,
|
maxlen: int,
|
maxlenratio: float,
|
running_hyps: List[Hypothesis],
|
ended_hyps: List[Hypothesis],
|
) -> List[Hypothesis]:
|
"""Perform post-processing of beam search iterations.
|
|
Args:
|
i (int): The length of hypothesis tokens.
|
maxlen (int): The maximum length of tokens in beam search.
|
maxlenratio (int): The maximum length ratio in beam search.
|
running_hyps (List[Hypothesis]): The running hypotheses in beam search.
|
ended_hyps (List[Hypothesis]): The ended hypotheses in beam search.
|
|
Returns:
|
List[Hypothesis]: The new running hypotheses.
|
|
"""
|
logging.debug(f"the number of running hypotheses: {len(running_hyps)}")
|
if self.token_list is not None:
|
logging.debug(
|
"best hypo: "
|
+ "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]])
|
)
|
# add eos in the final loop to avoid that there are no ended hyps
|
if i == maxlen - 1:
|
logging.info("adding <eos> in the last position in the loop")
|
running_hyps = [
|
h._replace(yseq=self.append_token(h.yseq, self.eos))
|
for h in running_hyps
|
]
|
|
# add ended hypotheses to a final list, and removed them from current hypotheses
|
# (this will be a problem, number of hyps < beam)
|
remained_hyps = []
|
for hyp in running_hyps:
|
if hyp.yseq[-1] == self.eos:
|
# e.g., Word LM needs to add final <eos> score
|
for k, d in chain(self.full_scorers.items(), self.part_scorers.items()):
|
s = d.final_score(hyp.states[k])
|
hyp.scores[k] += s
|
hyp = hyp._replace(score=hyp.score + self.weights[k] * s)
|
ended_hyps.append(hyp)
|
else:
|
remained_hyps.append(hyp)
|
return remained_hyps
|
|
class BeamSearchPara(torch.nn.Module):
|
"""Beam search implementation."""
|
|
def __init__(
|
self,
|
scorers: Dict[str, ScorerInterface],
|
weights: Dict[str, float],
|
beam_size: int,
|
vocab_size: int,
|
sos: int,
|
eos: int,
|
token_list: List[str] = None,
|
pre_beam_ratio: float = 1.5,
|
pre_beam_score_key: str = None,
|
):
|
"""Initialize beam search.
|
|
Args:
|
scorers (dict[str, ScorerInterface]): Dict of decoder modules
|
e.g., Decoder, CTCPrefixScorer, LM
|
The scorer will be ignored if it is `None`
|
weights (dict[str, float]): Dict of weights for each scorers
|
The scorer will be ignored if its weight is 0
|
beam_size (int): The number of hypotheses kept during search
|
vocab_size (int): The number of vocabulary
|
sos (int): Start of sequence id
|
eos (int): End of sequence id
|
token_list (list[str]): List of tokens for debug log
|
pre_beam_score_key (str): key of scores to perform pre-beam search
|
pre_beam_ratio (float): beam size in the pre-beam search
|
will be `int(pre_beam_ratio * beam_size)`
|
|
"""
|
super().__init__()
|
# set scorers
|
self.weights = weights
|
self.scorers = dict()
|
self.full_scorers = dict()
|
self.part_scorers = dict()
|
# this module dict is required for recursive cast
|
# `self.to(device, dtype)` in `recog.py`
|
self.nn_dict = torch.nn.ModuleDict()
|
for k, v in scorers.items():
|
w = weights.get(k, 0)
|
if w == 0 or v is None:
|
continue
|
assert isinstance(
|
v, ScorerInterface
|
), f"{k} ({type(v)}) does not implement ScorerInterface"
|
self.scorers[k] = v
|
if isinstance(v, PartialScorerInterface):
|
self.part_scorers[k] = v
|
else:
|
self.full_scorers[k] = v
|
if isinstance(v, torch.nn.Module):
|
self.nn_dict[k] = v
|
|
# set configurations
|
self.sos = sos
|
self.eos = eos
|
self.token_list = token_list
|
self.pre_beam_size = int(pre_beam_ratio * beam_size)
|
self.beam_size = beam_size
|
self.n_vocab = vocab_size
|
if (
|
pre_beam_score_key is not None
|
and pre_beam_score_key != "full"
|
and pre_beam_score_key not in self.full_scorers
|
):
|
raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}")
|
self.pre_beam_score_key = pre_beam_score_key
|
self.do_pre_beam = (
|
self.pre_beam_score_key is not None
|
and self.pre_beam_size < self.n_vocab
|
and len(self.part_scorers) > 0
|
)
|
|
def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]:
|
"""Get an initial hypothesis data.
|
|
Args:
|
x (torch.Tensor): The encoder output feature
|
|
Returns:
|
Hypothesis: The initial hypothesis.
|
|
"""
|
init_states = dict()
|
init_scores = dict()
|
for k, d in self.scorers.items():
|
init_states[k] = d.init_state(x)
|
init_scores[k] = 0.0
|
return [
|
Hypothesis(
|
score=0.0,
|
scores=init_scores,
|
states=init_states,
|
yseq=torch.tensor([self.sos], device=x.device),
|
)
|
]
|
|
@staticmethod
|
def append_token(xs: torch.Tensor, x: int) -> torch.Tensor:
|
"""Append new token to prefix tokens.
|
|
Args:
|
xs (torch.Tensor): The prefix token
|
x (int): The new token to append
|
|
Returns:
|
torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device
|
|
"""
|
x = torch.tensor([x], dtype=xs.dtype, device=xs.device)
|
return torch.cat((xs, x))
|
|
def score_full(
|
self, hyp: Hypothesis, x: torch.Tensor
|
) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
|
"""Score new hypothesis by `self.full_scorers`.
|
|
Args:
|
hyp (Hypothesis): Hypothesis with prefix tokens to score
|
x (torch.Tensor): Corresponding input feature
|
|
Returns:
|
Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
|
score dict of `hyp` that has string keys of `self.full_scorers`
|
and tensor score values of shape: `(self.n_vocab,)`,
|
and state dict that has string keys
|
and state values of `self.full_scorers`
|
|
"""
|
scores = dict()
|
states = dict()
|
for k, d in self.full_scorers.items():
|
scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x)
|
return scores, states
|
|
def score_partial(
|
self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor
|
) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
|
"""Score new hypothesis by `self.part_scorers`.
|
|
Args:
|
hyp (Hypothesis): Hypothesis with prefix tokens to score
|
ids (torch.Tensor): 1D tensor of new partial tokens to score
|
x (torch.Tensor): Corresponding input feature
|
|
Returns:
|
Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
|
score dict of `hyp` that has string keys of `self.part_scorers`
|
and tensor score values of shape: `(len(ids),)`,
|
and state dict that has string keys
|
and state values of `self.part_scorers`
|
|
"""
|
scores = dict()
|
states = dict()
|
for k, d in self.part_scorers.items():
|
scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x)
|
return scores, states
|
|
def beam(
|
self, weighted_scores: torch.Tensor, ids: torch.Tensor
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Compute topk full token ids and partial token ids.
|
|
Args:
|
weighted_scores (torch.Tensor): The weighted sum scores for each tokens.
|
Its shape is `(self.n_vocab,)`.
|
ids (torch.Tensor): The partial token ids to compute topk
|
|
Returns:
|
Tuple[torch.Tensor, torch.Tensor]:
|
The topk full token ids and partial token ids.
|
Their shapes are `(self.beam_size,)`
|
|
"""
|
# no pre beam performed
|
if weighted_scores.size(0) == ids.size(0):
|
top_ids = weighted_scores.topk(self.beam_size)[1]
|
return top_ids, top_ids
|
|
# mask pruned in pre-beam not to select in topk
|
tmp = weighted_scores[ids]
|
weighted_scores[:] = -float("inf")
|
weighted_scores[ids] = tmp
|
top_ids = weighted_scores.topk(self.beam_size)[1]
|
local_ids = weighted_scores[ids].topk(self.beam_size)[1]
|
return top_ids, local_ids
|
|
@staticmethod
|
def merge_scores(
|
prev_scores: Dict[str, float],
|
next_full_scores: Dict[str, torch.Tensor],
|
full_idx: int,
|
next_part_scores: Dict[str, torch.Tensor],
|
part_idx: int,
|
) -> Dict[str, torch.Tensor]:
|
"""Merge scores for new hypothesis.
|
|
Args:
|
prev_scores (Dict[str, float]):
|
The previous hypothesis scores by `self.scorers`
|
next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers`
|
full_idx (int): The next token id for `next_full_scores`
|
next_part_scores (Dict[str, torch.Tensor]):
|
scores of partial tokens by `self.part_scorers`
|
part_idx (int): The new token id for `next_part_scores`
|
|
Returns:
|
Dict[str, torch.Tensor]: The new score dict.
|
Its keys are names of `self.full_scorers` and `self.part_scorers`.
|
Its values are scalar tensors by the scorers.
|
|
"""
|
new_scores = dict()
|
for k, v in next_full_scores.items():
|
new_scores[k] = prev_scores[k] + v[full_idx]
|
for k, v in next_part_scores.items():
|
new_scores[k] = prev_scores[k] + v[part_idx]
|
return new_scores
|
|
def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any:
|
"""Merge states for new hypothesis.
|
|
Args:
|
states: states of `self.full_scorers`
|
part_states: states of `self.part_scorers`
|
part_idx (int): The new token id for `part_scores`
|
|
Returns:
|
Dict[str, torch.Tensor]: The new score dict.
|
Its keys are names of `self.full_scorers` and `self.part_scorers`.
|
Its values are states of the scorers.
|
|
"""
|
new_states = dict()
|
for k, v in states.items():
|
new_states[k] = v
|
for k, d in self.part_scorers.items():
|
new_states[k] = d.select_state(part_states[k], part_idx)
|
return new_states
|
|
def search(
|
self, running_hyps: List[Hypothesis], x: torch.Tensor, am_score: torch.Tensor
|
) -> List[Hypothesis]:
|
"""Search new tokens for running hypotheses and encoded speech x.
|
|
Args:
|
running_hyps (List[Hypothesis]): Running hypotheses on beam
|
x (torch.Tensor): Encoded speech feature (T, D)
|
|
Returns:
|
List[Hypotheses]: Best sorted hypotheses
|
|
"""
|
best_hyps = []
|
part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam
|
for hyp in running_hyps:
|
# scoring
|
weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device)
|
weighted_scores += am_score
|
scores, states = self.score_full(hyp, x)
|
for k in self.full_scorers:
|
weighted_scores += self.weights[k] * scores[k]
|
# partial scoring
|
if self.do_pre_beam:
|
pre_beam_scores = (
|
weighted_scores
|
if self.pre_beam_score_key == "full"
|
else scores[self.pre_beam_score_key]
|
)
|
part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1]
|
part_scores, part_states = self.score_partial(hyp, part_ids, x)
|
for k in self.part_scorers:
|
weighted_scores[part_ids] += self.weights[k] * part_scores[k]
|
# add previous hyp score
|
weighted_scores += hyp.score
|
|
# update hyps
|
for j, part_j in zip(*self.beam(weighted_scores, part_ids)):
|
# will be (2 x beam at most)
|
best_hyps.append(
|
Hypothesis(
|
score=weighted_scores[j],
|
yseq=self.append_token(hyp.yseq, j),
|
scores=self.merge_scores(
|
hyp.scores, scores, j, part_scores, part_j
|
),
|
states=self.merge_states(states, part_states, part_j),
|
)
|
)
|
|
# sort and prune 2 x beam -> beam
|
best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[
|
: min(len(best_hyps), self.beam_size)
|
]
|
return best_hyps
|
|
def forward(
|
self, x: torch.Tensor, am_scores: torch.Tensor, maxlenratio: float = 0.0, minlenratio: float = 0.0
|
) -> List[Hypothesis]:
|
"""Perform beam search.
|
|
Args:
|
x (torch.Tensor): Encoded speech feature (T, D)
|
maxlenratio (float): Input length ratio to obtain max output length.
|
If maxlenratio=0.0 (default), it uses a end-detect function
|
to automatically find maximum hypothesis lengths
|
If maxlenratio<0.0, its absolute value is interpreted
|
as a constant max output length.
|
minlenratio (float): Input length ratio to obtain min output length.
|
|
Returns:
|
list[Hypothesis]: N-best decoding results
|
|
"""
|
# set length bounds
|
maxlen = am_scores.shape[0]
|
logging.info("decoder input length: " + str(x.shape[0]))
|
logging.info("max output length: " + str(maxlen))
|
|
# main loop of prefix search
|
running_hyps = self.init_hyp(x)
|
ended_hyps = []
|
for i in range(maxlen):
|
logging.debug("position " + str(i))
|
best = self.search(running_hyps, x, am_scores[i])
|
# post process of one iteration
|
running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
|
# end detection
|
if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i):
|
logging.info(f"end detected at {i}")
|
break
|
if len(running_hyps) == 0:
|
logging.info("no hypothesis. Finish decoding.")
|
break
|
else:
|
logging.debug(f"remained hypotheses: {len(running_hyps)}")
|
|
nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True)
|
# check the number of hypotheses reaching to eos
|
if len(nbest_hyps) == 0:
|
logging.warning(
|
"there is no N-best results, perform recognition "
|
"again with smaller minlenratio."
|
)
|
return (
|
[]
|
if minlenratio < 0.1
|
else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1))
|
)
|
|
# report the best result
|
best = nbest_hyps[0]
|
for k, v in best.scores.items():
|
logging.info(
|
f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}"
|
)
|
logging.info(f"total log probability: {best.score:.2f}")
|
logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}")
|
logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}")
|
if self.token_list is not None:
|
logging.info(
|
"best hypo: "
|
+ "".join([self.token_list[x] for x in best.yseq[1:-1]])
|
+ "\n"
|
)
|
return nbest_hyps
|
|
def post_process(
|
self,
|
i: int,
|
maxlen: int,
|
maxlenratio: float,
|
running_hyps: List[Hypothesis],
|
ended_hyps: List[Hypothesis],
|
) -> List[Hypothesis]:
|
"""Perform post-processing of beam search iterations.
|
|
Args:
|
i (int): The length of hypothesis tokens.
|
maxlen (int): The maximum length of tokens in beam search.
|
maxlenratio (int): The maximum length ratio in beam search.
|
running_hyps (List[Hypothesis]): The running hypotheses in beam search.
|
ended_hyps (List[Hypothesis]): The ended hypotheses in beam search.
|
|
Returns:
|
List[Hypothesis]: The new running hypotheses.
|
|
"""
|
logging.debug(f"the number of running hypotheses: {len(running_hyps)}")
|
if self.token_list is not None:
|
logging.debug(
|
"best hypo: "
|
+ "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]])
|
)
|
# add eos in the final loop to avoid that there are no ended hyps
|
if i == maxlen - 1:
|
logging.info("adding <eos> in the last position in the loop")
|
running_hyps = [
|
h._replace(yseq=self.append_token(h.yseq, self.eos))
|
for h in running_hyps
|
]
|
|
# add ended hypotheses to a final list, and removed them from current hypotheses
|
# (this will be a problem, number of hyps < beam)
|
remained_hyps = []
|
for hyp in running_hyps:
|
if hyp.yseq[-1] == self.eos:
|
# e.g., Word LM needs to add final <eos> score
|
for k, d in chain(self.full_scorers.items(), self.part_scorers.items()):
|
s = d.final_score(hyp.states[k])
|
hyp.scores[k] += s
|
hyp = hyp._replace(score=hyp.score + self.weights[k] * s)
|
ended_hyps.append(hyp)
|
else:
|
remained_hyps.append(hyp)
|
return remained_hyps
|
