340 lines
12 KiB
Python
340 lines
12 KiB
Python
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from typing import Callable, Dict, List, Optional, Tuple
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import torch
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from torchaudio.models import RNNT
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__all__ = ["Hypothesis", "RNNTBeamSearch"]
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Hypothesis = Tuple[List[int], torch.Tensor, List[List[torch.Tensor]], float]
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Hypothesis.__doc__ = """Hypothesis generated by RNN-T beam search decoder,
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represented as tuple of (tokens, prediction network output, prediction network state, score).
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"""
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def _get_hypo_tokens(hypo: Hypothesis) -> List[int]:
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return hypo[0]
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def _get_hypo_predictor_out(hypo: Hypothesis) -> torch.Tensor:
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return hypo[1]
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def _get_hypo_state(hypo: Hypothesis) -> List[List[torch.Tensor]]:
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return hypo[2]
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def _get_hypo_score(hypo: Hypothesis) -> float:
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return hypo[3]
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def _get_hypo_key(hypo: Hypothesis) -> str:
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return str(hypo[0])
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def _batch_state(hypos: List[Hypothesis]) -> List[List[torch.Tensor]]:
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states: List[List[torch.Tensor]] = []
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for i in range(len(_get_hypo_state(hypos[0]))):
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batched_state_components: List[torch.Tensor] = []
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for j in range(len(_get_hypo_state(hypos[0])[i])):
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batched_state_components.append(torch.cat([_get_hypo_state(hypo)[i][j] for hypo in hypos]))
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states.append(batched_state_components)
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return states
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def _slice_state(states: List[List[torch.Tensor]], idx: int, device: torch.device) -> List[List[torch.Tensor]]:
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idx_tensor = torch.tensor([idx], device=device)
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return [[state.index_select(0, idx_tensor) for state in state_tuple] for state_tuple in states]
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def _default_hypo_sort_key(hypo: Hypothesis) -> float:
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return _get_hypo_score(hypo) / (len(_get_hypo_tokens(hypo)) + 1)
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def _compute_updated_scores(
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hypos: List[Hypothesis],
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next_token_probs: torch.Tensor,
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beam_width: int,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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hypo_scores = torch.tensor([_get_hypo_score(h) for h in hypos]).unsqueeze(1)
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nonblank_scores = hypo_scores + next_token_probs[:, :-1] # [beam_width, num_tokens - 1]
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nonblank_nbest_scores, nonblank_nbest_idx = nonblank_scores.reshape(-1).topk(beam_width)
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nonblank_nbest_hypo_idx = nonblank_nbest_idx.div(nonblank_scores.shape[1], rounding_mode="trunc")
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nonblank_nbest_token = nonblank_nbest_idx % nonblank_scores.shape[1]
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return nonblank_nbest_scores, nonblank_nbest_hypo_idx, nonblank_nbest_token
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def _remove_hypo(hypo: Hypothesis, hypo_list: List[Hypothesis]) -> None:
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for i, elem in enumerate(hypo_list):
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if _get_hypo_key(hypo) == _get_hypo_key(elem):
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del hypo_list[i]
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break
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class RNNTBeamSearch(torch.nn.Module):
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r"""Beam search decoder for RNN-T model.
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See Also:
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* :class:`torchaudio.pipelines.RNNTBundle`: ASR pipeline with pretrained model.
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Args:
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model (RNNT): RNN-T model to use.
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blank (int): index of blank token in vocabulary.
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temperature (float, optional): temperature to apply to joint network output.
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Larger values yield more uniform samples. (Default: 1.0)
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hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
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for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
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hypothesis score normalized by token sequence length. (Default: None)
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step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
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"""
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def __init__(
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self,
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model: RNNT,
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blank: int,
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temperature: float = 1.0,
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hypo_sort_key: Optional[Callable[[Hypothesis], float]] = None,
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step_max_tokens: int = 100,
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) -> None:
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super().__init__()
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self.model = model
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self.blank = blank
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self.temperature = temperature
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if hypo_sort_key is None:
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self.hypo_sort_key = _default_hypo_sort_key
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else:
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self.hypo_sort_key = hypo_sort_key
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self.step_max_tokens = step_max_tokens
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def _init_b_hypos(self, device: torch.device) -> List[Hypothesis]:
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token = self.blank
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state = None
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one_tensor = torch.tensor([1], device=device)
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pred_out, _, pred_state = self.model.predict(torch.tensor([[token]], device=device), one_tensor, state)
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init_hypo = (
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[token],
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pred_out[0].detach(),
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pred_state,
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0.0,
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)
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return [init_hypo]
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def _gen_next_token_probs(
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self, enc_out: torch.Tensor, hypos: List[Hypothesis], device: torch.device
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) -> torch.Tensor:
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one_tensor = torch.tensor([1], device=device)
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predictor_out = torch.stack([_get_hypo_predictor_out(h) for h in hypos], dim=0)
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joined_out, _, _ = self.model.join(
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enc_out,
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one_tensor,
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predictor_out,
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torch.tensor([1] * len(hypos), device=device),
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) # [beam_width, 1, 1, num_tokens]
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joined_out = torch.nn.functional.log_softmax(joined_out / self.temperature, dim=3)
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return joined_out[:, 0, 0]
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def _gen_b_hypos(
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self,
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b_hypos: List[Hypothesis],
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a_hypos: List[Hypothesis],
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next_token_probs: torch.Tensor,
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key_to_b_hypo: Dict[str, Hypothesis],
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) -> List[Hypothesis]:
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for i in range(len(a_hypos)):
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h_a = a_hypos[i]
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append_blank_score = _get_hypo_score(h_a) + next_token_probs[i, -1]
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if _get_hypo_key(h_a) in key_to_b_hypo:
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h_b = key_to_b_hypo[_get_hypo_key(h_a)]
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_remove_hypo(h_b, b_hypos)
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score = float(torch.tensor(_get_hypo_score(h_b)).logaddexp(append_blank_score))
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else:
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score = float(append_blank_score)
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h_b = (
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_get_hypo_tokens(h_a),
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_get_hypo_predictor_out(h_a),
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_get_hypo_state(h_a),
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score,
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)
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b_hypos.append(h_b)
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key_to_b_hypo[_get_hypo_key(h_b)] = h_b
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_, sorted_idx = torch.tensor([_get_hypo_score(hypo) for hypo in b_hypos]).sort()
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return [b_hypos[idx] for idx in sorted_idx]
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def _gen_a_hypos(
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self,
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a_hypos: List[Hypothesis],
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b_hypos: List[Hypothesis],
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next_token_probs: torch.Tensor,
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t: int,
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beam_width: int,
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device: torch.device,
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) -> List[Hypothesis]:
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(
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nonblank_nbest_scores,
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nonblank_nbest_hypo_idx,
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nonblank_nbest_token,
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) = _compute_updated_scores(a_hypos, next_token_probs, beam_width)
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if len(b_hypos) < beam_width:
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b_nbest_score = -float("inf")
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else:
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b_nbest_score = _get_hypo_score(b_hypos[-beam_width])
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base_hypos: List[Hypothesis] = []
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new_tokens: List[int] = []
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new_scores: List[float] = []
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for i in range(beam_width):
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score = float(nonblank_nbest_scores[i])
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if score > b_nbest_score:
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a_hypo_idx = int(nonblank_nbest_hypo_idx[i])
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base_hypos.append(a_hypos[a_hypo_idx])
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new_tokens.append(int(nonblank_nbest_token[i]))
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new_scores.append(score)
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if base_hypos:
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new_hypos = self._gen_new_hypos(base_hypos, new_tokens, new_scores, t, device)
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else:
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new_hypos: List[Hypothesis] = []
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return new_hypos
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def _gen_new_hypos(
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self,
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base_hypos: List[Hypothesis],
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tokens: List[int],
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scores: List[float],
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t: int,
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device: torch.device,
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) -> List[Hypothesis]:
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tgt_tokens = torch.tensor([[token] for token in tokens], device=device)
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states = _batch_state(base_hypos)
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pred_out, _, pred_states = self.model.predict(
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tgt_tokens,
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torch.tensor([1] * len(base_hypos), device=device),
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states,
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)
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new_hypos: List[Hypothesis] = []
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for i, h_a in enumerate(base_hypos):
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new_tokens = _get_hypo_tokens(h_a) + [tokens[i]]
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new_hypos.append((new_tokens, pred_out[i].detach(), _slice_state(pred_states, i, device), scores[i]))
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return new_hypos
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def _search(
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self,
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enc_out: torch.Tensor,
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hypo: Optional[List[Hypothesis]],
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beam_width: int,
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) -> List[Hypothesis]:
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n_time_steps = enc_out.shape[1]
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device = enc_out.device
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a_hypos: List[Hypothesis] = []
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b_hypos = self._init_b_hypos(device) if hypo is None else hypo
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for t in range(n_time_steps):
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a_hypos = b_hypos
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b_hypos = torch.jit.annotate(List[Hypothesis], [])
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key_to_b_hypo: Dict[str, Hypothesis] = {}
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symbols_current_t = 0
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while a_hypos:
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next_token_probs = self._gen_next_token_probs(enc_out[:, t : t + 1], a_hypos, device)
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next_token_probs = next_token_probs.cpu()
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b_hypos = self._gen_b_hypos(b_hypos, a_hypos, next_token_probs, key_to_b_hypo)
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if symbols_current_t == self.step_max_tokens:
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break
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a_hypos = self._gen_a_hypos(
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a_hypos,
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b_hypos,
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next_token_probs,
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t,
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beam_width,
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device,
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)
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if a_hypos:
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symbols_current_t += 1
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_, sorted_idx = torch.tensor([self.hypo_sort_key(hyp) for hyp in b_hypos]).topk(beam_width)
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b_hypos = [b_hypos[idx] for idx in sorted_idx]
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return b_hypos
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def forward(self, input: torch.Tensor, length: torch.Tensor, beam_width: int) -> List[Hypothesis]:
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r"""Performs beam search for the given input sequence.
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T: number of frames;
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D: feature dimension of each frame.
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Args:
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input (torch.Tensor): sequence of input frames, with shape (T, D) or (1, T, D).
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length (torch.Tensor): number of valid frames in input
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sequence, with shape () or (1,).
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beam_width (int): beam size to use during search.
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Returns:
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List[Hypothesis]: top-``beam_width`` hypotheses found by beam search.
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"""
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if input.dim() != 2 and not (input.dim() == 3 and input.shape[0] == 1):
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raise ValueError("input must be of shape (T, D) or (1, T, D)")
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if input.dim() == 2:
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input = input.unsqueeze(0)
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if length.shape != () and length.shape != (1,):
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raise ValueError("length must be of shape () or (1,)")
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if length.dim() == 0:
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length = length.unsqueeze(0)
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enc_out, _ = self.model.transcribe(input, length)
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return self._search(enc_out, None, beam_width)
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@torch.jit.export
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def infer(
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self,
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input: torch.Tensor,
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length: torch.Tensor,
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beam_width: int,
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state: Optional[List[List[torch.Tensor]]] = None,
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hypothesis: Optional[List[Hypothesis]] = None,
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) -> Tuple[List[Hypothesis], List[List[torch.Tensor]]]:
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r"""Performs beam search for the given input sequence in streaming mode.
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T: number of frames;
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D: feature dimension of each frame.
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Args:
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input (torch.Tensor): sequence of input frames, with shape (T, D) or (1, T, D).
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length (torch.Tensor): number of valid frames in input
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sequence, with shape () or (1,).
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beam_width (int): beam size to use during search.
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state (List[List[torch.Tensor]] or None, optional): list of lists of tensors
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representing transcription network internal state generated in preceding
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invocation. (Default: ``None``)
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hypothesis (List[Hypothesis] or None): hypotheses from preceding invocation to seed
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search with. (Default: ``None``)
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Returns:
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(List[Hypothesis], List[List[torch.Tensor]]):
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List[Hypothesis]
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top-``beam_width`` hypotheses found by beam search.
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List[List[torch.Tensor]]
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list of lists of tensors representing transcription network
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internal state generated in current invocation.
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"""
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if input.dim() != 2 and not (input.dim() == 3 and input.shape[0] == 1):
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raise ValueError("input must be of shape (T, D) or (1, T, D)")
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if input.dim() == 2:
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input = input.unsqueeze(0)
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if length.shape != () and length.shape != (1,):
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raise ValueError("length must be of shape () or (1,)")
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if length.dim() == 0:
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length = length.unsqueeze(0)
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enc_out, _, state = self.model.transcribe_streaming(input, length, state)
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return self._search(enc_out, hypothesis, beam_width), state
|