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PaddleSpeech/paddlespeech/s2t/decoders/scorers/length_bonus.py

74 lines
2.4 KiB

# 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.
# Modified from espnet(https://github.com/espnet/espnet)
"""Length bonus module."""
from typing import Any
from typing import List
from typing import Tuple
import paddle
from .scorer_interface import BatchScorerInterface
class LengthBonus(BatchScorerInterface):
"""Length bonus in beam search."""
def __init__(self, n_vocab: int):
"""Initialize class.
Args:
n_vocab (int): The number of tokens in vocabulary for beam search
"""
self.n = n_vocab
def score(self, y, state, x):
"""Score new token.
Args:
y (paddle.Tensor): 1D paddle.int64 prefix tokens.
state: Scorer state for prefix tokens
x (paddle.Tensor): 2D encoder feature that generates ys.
Returns:
tuple[paddle.Tensor, Any]: Tuple of
paddle.float32 scores for next token (n_vocab)
and None
"""
return paddle.to_tensor(
[1.0], place=x.place, dtype=x.dtype).expand(self.n), None
def batch_score(self,
ys: paddle.Tensor,
states: List[Any],
xs: paddle.Tensor) -> Tuple[paddle.Tensor, List[Any]]:
"""Score new token batch.
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.
"""
return (paddle.to_tensor([1.0], place=xs.place, dtype=xs.dtype).expand(
ys.shape[0], self.n), None, )