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PaddleSpeech/deepspeech/decoders/scorers/ctc.py

164 lines
5.3 KiB

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# 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.
"""ScorerInterface implementation for CTC."""
import numpy as np
import paddle
from .ctc_prefix_score import CTCPrefixScorer
from .ctc_prefix_score import CTCPrefixScorerPD
from .scorer_interface import BatchPartialScorerInterface
class CTCPrefixScorer(BatchPartialScorerInterface):
"""Decoder interface wrapper for CTCPrefixScore."""
def __init__(self, ctc: paddle.nn.Layer, eos: int):
"""Initialize class.
Args:
ctc (paddle.nn.Layer): The CTC implementation.
For example, :class:`deepspeech.modules.ctc.CTC`
eos (int): The end-of-sequence id.
"""
self.ctc = ctc
self.eos = eos
self.impl = None
def init_state(self, x: paddle.Tensor):
"""Get an initial state for decoding.
Args:
x (paddle.Tensor): The encoded feature tensor
Returns: initial state
"""
logp = self.ctc.log_softmax(x.unsqueeze(0)).squeeze(0).numpy()
# TODO(karita): use CTCPrefixScorePD
self.impl = CTCPrefixScore(logp, 0, self.eos, np)
return 0, self.impl.initial_state()
def select_state(self, state, i, new_id=None):
"""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 id to select a state if necessary
Returns:
state: pruned state
"""
if type(state) == tuple:
if len(state) == 2: # for CTCPrefixScore
sc, st = state
return sc[i], st[i]
else: # for CTCPrefixScorePD (need new_id > 0)
r, log_psi, f_min, f_max, scoring_idmap = state
s = log_psi[i, new_id].expand(log_psi.size(1))
if scoring_idmap is not None:
return r[:, :, i, scoring_idmap[i, new_id]], s, f_min, f_max
else:
return r[:, :, i, new_id], s, f_min, f_max
return None if state is None else state[i]
def score_partial(self, y, ids, state, x):
"""Score new token.
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): 2D 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
"""
prev_score, state = state
presub_score, new_st = self.impl(y.cpu(), ids.cpu(), state)
tscore = paddle.to_tensor(
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presub_score - prev_score, place=x.place, dtype=x.dtype)
return tscore, (presub_score, new_st)
def batch_init_state(self, x: paddle.Tensor):
"""Get an initial state for decoding.
Args:
x (paddle.Tensor): The encoded feature tensor
Returns: initial state
"""
logp = self.ctc.log_softmax(x.unsqueeze(0)) # assuming batch_size = 1
xlen = paddle.to_tensor([logp.size(1)])
self.impl = CTCPrefixScorePD(logp, xlen, 0, self.eos)
return None
def batch_score_partial(self, y, ids, state, x):
"""Score new token.
Args:
y (paddle.Tensor): 1D prefix token
ids (paddle.Tensor): paddle.int64 next token to score
state: decoder state for prefix tokens
x (paddle.Tensor): 2D 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
"""
batch_state = (
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(paddle.stack([s[0] for s in state], axis=2),
paddle.stack([s[1] for s in state]), state[0][2], state[0][3], )
if state[0] is not None else None)
return self.impl(y, batch_state, ids)
def extend_prob(self, x: paddle.Tensor):
"""Extend probs for decoding.
This extension is for streaming decoding
as in Eq (14) in https://arxiv.org/abs/2006.14941
Args:
x (paddle.Tensor): The encoded feature tensor
"""
logp = self.ctc.log_softmax(x.unsqueeze(0))
self.impl.extend_prob(logp)
def extend_state(self, state):
"""Extend state for decoding.
This extension is for streaming decoding
as in Eq (14) in https://arxiv.org/abs/2006.14941
Args:
state: The states of hyps
Returns: exteded state
"""
new_state = []
for s in state:
new_state.append(self.impl.extend_state(s))
return new_state