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# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
"""Scorer interface module."""
from abc import ABC
from typing import Any
from typing import List
from typing import Tuple
import torch
class ScorerInterface:
"""Scorer interface for beam search.
The scorer performs scoring of the all tokens in vocabulary.
Examples:
* Search heuristics
* :class:`espnet.nets.scorers.length_bonus.LengthBonus`
* Decoder networks of the sequence-to-sequence models
* :class:`espnet.nets.pytorch_backend.nets.transformer.decoder
.Decoder`
* :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder`
* Neural language models
* :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM`
* :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM`
* :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM`
"""
def init_state(self, x: torch.Tensor) -> Any:
"""Get an initial state for decoding (optional).
Args:
x (torch.Tensor): The encoded feature tensor
Returns: initial state
"""
return None
def select_state(self, state: Any, i: int, new_id: int = None) -> Any:
"""Select state with relative ids in the main beam search.
Args:
state: Decoder state for prefix tokens
i (int): Index to select a state in the main beam search
new_id (int): New label index to select a state if necessary
Returns:
state: pruned state
"""
return None if state is None else state[i]
def score(
self, y: torch.Tensor, state: Any, x: torch.Tensor
) -> Tuple[torch.Tensor, Any]:
"""Score new token (required).
Args:
y (torch.Tensor): 1D torch.int64 prefix tokens.
state: Scorer state for prefix tokens
x (torch.Tensor): The encoder feature that generates ys.
Returns:
tuple[torch.Tensor, Any]: Tuple of
scores for next token that has a shape of `(n_vocab)`
and next state for ys
"""
raise NotImplementedError
def final_score(self, state: Any) -> float:
"""Score eos (optional).
Args:
state: Scorer state for prefix tokens
Returns:
float: final score
"""
return 0.0
class BatchScorerInterface(ScorerInterface, ABC):
"""Batch scorer interface."""
def batch_init_state(self, x: torch.Tensor) -> Any:
"""Get an initial state for decoding (optional).
Args:
x (torch.Tensor): The encoded feature tensor
Returns: initial state
"""
return self.init_state(x)
def batch_score(
self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
) -> Tuple[torch.Tensor, List[Any]]:
"""Score new token batch (required).
Args:
ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
states (List[Any]): Scorer states for prefix tokens.
xs (torch.Tensor):
The encoder feature that generates ys (n_batch, xlen, n_feat).
Returns:
tuple[torch.Tensor, List[Any]]: Tuple of
batchfied scores for next token with shape of `(n_batch,
n_vocab)`
and next state list for ys.
"""
scores = list()
outstates = list()
for i, (y, state, x) in enumerate(zip(ys, states, xs)):
score, outstate = self.score(y, state, x)
outstates.append(outstate)
scores.append(score)
scores = torch.cat(scores, 0).view(ys.shape[0], -1)
return scores, outstates
class PartialScorerInterface(ScorerInterface, ABC):
"""Partial scorer interface for beam search.
The partial scorer performs scoring when non-partial scorer finished scoring
and receives pre-pruned next tokens to score because it is too heavy to
score all the tokens.
Examples:
* Prefix search for connectionist-temporal-classification models
* :class:`espnet.nets.scorers.ctc.CTCPrefixScorer`
"""
def score_partial(
self, y: torch.Tensor, next_tokens: torch.Tensor, state: Any, x: torch.Tensor
) -> Tuple[torch.Tensor, Any]:
"""Score new token (required).
Args:
y (torch.Tensor): 1D prefix token
next_tokens (torch.Tensor): torch.int64 next token to score
state: decoder state for prefix tokens
x (torch.Tensor): The encoder feature that generates ys
Returns:
tuple[torch.Tensor, Any]:
Tuple of a score tensor for y that has a shape
`(len(next_tokens),)` and next state for ys
"""
raise NotImplementedError
class BatchPartialScorerInterface(BatchScorerInterface, PartialScorerInterface, ABC):
"""Batch partial scorer interface for beam search."""
def batch_score_partial(
self,
ys: torch.Tensor,
next_tokens: torch.Tensor,
states: List[Any],
xs: torch.Tensor,
) -> Tuple[torch.Tensor, Any]:
"""Score new token (required).
Args:
ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
next_tokens (torch.Tensor): torch.int64 tokens to score (n_batch,
n_token).
states (List[Any]): Scorer states for prefix tokens.
xs (torch.Tensor):
The encoder feature that generates ys (n_batch, xlen, n_feat).
Returns:
tuple[torch.Tensor, Any]:
Tuple of a score tensor for ys that has a shape `(n_batch,
n_vocab)`
and next states for ys
"""
raise NotImplementedError
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