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PaddleSpeech/deepspeech/decoders/scorers/score_interface.py

202 lines
6.4 KiB

3 years ago
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Scorer interface module."""
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import warnings
from typing import Any
from typing import List
from typing import Tuple
import paddle
class ScorerInterface:
"""Scorer interface for beam search.
The scorer performs scoring of the all tokens in vocabulary.
Examples:
* Search heuristics
* :class:`scorers.length_bonus.LengthBonus`
* Decoder networks of the sequence-to-sequence models
* :class:`transformer.decoder.Decoder`
* :class:`rnn.decoders.Decoder`
* Neural language models
* :class:`lm.transformer.TransformerLM`
* :class:`lm.default.DefaultRNNLM`
* :class:`lm.seq_rnn.SequentialRNNLM`
"""
def init_state(self, x: paddle.Tensor) -> Any:
"""Get an initial state for decoding (optional).
Args:
x (paddle.Tensor): The encoded feature tensor
Returns: initial state
"""
return None
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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]
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def score(self, y: paddle.Tensor, state: Any,
x: paddle.Tensor) -> Tuple[paddle.Tensor, Any]:
"""Score new token (required).
Args:
y (paddle.Tensor): 1D paddle.int64 prefix tokens.
state: Scorer state for prefix tokens
x (paddle.Tensor): The encoder feature that generates ys.
Returns:
tuple[paddle.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):
"""Batch scorer interface."""
def batch_init_state(self, x: paddle.Tensor) -> Any:
"""Get an initial state for decoding (optional).
Args:
x (paddle.Tensor): The encoded feature tensor
Returns: initial state
"""
return self.init_state(x)
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def batch_score(self,
ys: paddle.Tensor,
states: List[Any],
xs: paddle.Tensor) -> Tuple[paddle.Tensor, List[Any]]:
"""Score new token batch (required).
Args:
ys (paddle.Tensor): paddle.int64 prefix tokens (n_batch, ylen).
states (List[Any]): Scorer states for prefix tokens.
xs (paddle.Tensor):
The encoder feature that generates ys (n_batch, xlen, n_feat).
Returns:
tuple[paddle.Tensor, List[Any]]: Tuple of
batchfied scores for next token with shape of `(n_batch, n_vocab)`
and next state list for ys.
"""
warnings.warn(
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"{} batch score is implemented through for loop not parallelized".
format(self.__class__.__name__))
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 = paddle.cat(scores, 0).view(ys.shape[0], -1)
return scores, outstates
class PartialScorerInterface(ScorerInterface):
"""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.
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Score sub-set of tokens, not all.
Examples:
* Prefix search for connectionist-temporal-classification models
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* :class:`decoders.scorers.ctc.CTCPrefixScorer`
"""
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def score_partial(self,
y: paddle.Tensor,
next_tokens: paddle.Tensor,
state: Any,
x: paddle.Tensor) -> Tuple[paddle.Tensor, Any]:
"""Score new token (required).
Args:
y (paddle.Tensor): 1D prefix token
next_tokens (paddle.Tensor): paddle.int64 next token to score
state: decoder state for prefix tokens
x (paddle.Tensor): The encoder feature that generates ys
Returns:
tuple[paddle.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):
"""Batch partial scorer interface for beam search."""
def batch_score_partial(
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self,
ys: paddle.Tensor,
next_tokens: paddle.Tensor,
states: List[Any],
xs: paddle.Tensor, ) -> Tuple[paddle.Tensor, Any]:
"""Score new token (required).
Args:
ys (paddle.Tensor): paddle.int64 prefix tokens (n_batch, ylen).
next_tokens (paddle.Tensor): paddle.int64 tokens to score (n_batch, n_token).
states (List[Any]): Scorer states for prefix tokens.
xs (paddle.Tensor):
The encoder feature that generates ys (n_batch, xlen, n_feat).
Returns:
tuple[paddle.Tensor, Any]:
Tuple of a score tensor for ys that has a shape `(n_batch, n_vocab)`
and next states for ys
"""
raise NotImplementedError