You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
470 lines
19 KiB
470 lines
19 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.
|
|
from typing import Union
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
from paddle.nn import functional as F
|
|
from typeguard import check_argument_types
|
|
|
|
from paddlespeech.s2t.modules.loss import CTCLoss
|
|
from paddlespeech.s2t.utils import ctc_utils
|
|
from paddlespeech.s2t.utils.log import Log
|
|
|
|
logger = Log(__name__).getlog()
|
|
|
|
try:
|
|
from paddlespeech.s2t.decoders.ctcdecoder import ctc_beam_search_decoding_batch # noqa: F401
|
|
from paddlespeech.s2t.decoders.ctcdecoder import ctc_greedy_decoding # noqa: F401
|
|
from paddlespeech.s2t.decoders.ctcdecoder import Scorer # noqa: F401
|
|
from paddlespeech.s2t.decoders.ctcdecoder import CTCBeamSearchDecoder # noqa: F401
|
|
except ImportError:
|
|
try:
|
|
from paddlespeech.s2t.utils import dynamic_pip_install
|
|
package_name = 'paddlespeech_ctcdecoders'
|
|
dynamic_pip_install.install(package_name)
|
|
from paddlespeech.s2t.decoders.ctcdecoder import ctc_beam_search_decoding_batch # noqa: F401
|
|
from paddlespeech.s2t.decoders.ctcdecoder import ctc_greedy_decoding # noqa: F401
|
|
from paddlespeech.s2t.decoders.ctcdecoder import Scorer # noqa: F401
|
|
from paddlespeech.s2t.decoders.ctcdecoder import CTCBeamSearchDecoder # noqa: F401
|
|
except Exception as e:
|
|
logger.info("paddlespeech_ctcdecoders not installed!")
|
|
|
|
__all__ = ['CTCDecoder']
|
|
|
|
|
|
class CTCDecoderBase(nn.Layer):
|
|
def __init__(self,
|
|
odim,
|
|
enc_n_units,
|
|
blank_id=0,
|
|
dropout_rate: float=0.0,
|
|
reduction: bool=True,
|
|
batch_average: bool=True,
|
|
grad_norm_type: Union[str, None]=None):
|
|
"""CTC decoder
|
|
|
|
Args:
|
|
odim ([int]): text vocabulary size
|
|
enc_n_units ([int]): encoder output dimention
|
|
dropout_rate (float): dropout rate (0.0 ~ 1.0)
|
|
reduction (bool): reduce the CTC loss into a scalar, True for 'sum' or 'none'
|
|
batch_average (bool): do batch dim wise average.
|
|
grad_norm_type (str): Default, None. one of 'instance', 'batch', 'frame', None.
|
|
"""
|
|
assert check_argument_types()
|
|
super().__init__()
|
|
|
|
self.blank_id = blank_id
|
|
self.odim = odim
|
|
self.dropout = nn.Dropout(dropout_rate)
|
|
self.ctc_lo = nn.Linear(enc_n_units, self.odim)
|
|
reduction_type = "sum" if reduction else "none"
|
|
self.criterion = CTCLoss(
|
|
blank=self.blank_id,
|
|
reduction=reduction_type,
|
|
batch_average=batch_average,
|
|
grad_norm_type=grad_norm_type)
|
|
|
|
def forward(self, hs_pad, hlens, ys_pad, ys_lens):
|
|
"""Calculate CTC loss.
|
|
|
|
Args:
|
|
hs_pad (Tensor): batch of padded hidden state sequences (B, Tmax, D)
|
|
hlens (Tensor): batch of lengths of hidden state sequences (B)
|
|
ys_pad (Tensor): batch of padded character id sequence tensor (B, Lmax)
|
|
ys_lens (Tensor): batch of lengths of character sequence (B)
|
|
Returns:
|
|
loss (Tensor): ctc loss value, scalar.
|
|
"""
|
|
logits = self.ctc_lo(self.dropout(hs_pad))
|
|
loss = self.criterion(logits, ys_pad, hlens, ys_lens)
|
|
return loss
|
|
|
|
def softmax(self, eouts: paddle.Tensor, temperature: float=1.0):
|
|
"""Get CTC probabilities.
|
|
Args:
|
|
eouts (FloatTensor): `[B, T, enc_units]`
|
|
Returns:
|
|
probs (FloatTensor): `[B, T, odim]`
|
|
"""
|
|
self.probs = F.softmax(self.ctc_lo(eouts) / temperature, axis=2)
|
|
return self.probs
|
|
|
|
def log_softmax(self, hs_pad: paddle.Tensor,
|
|
temperature: float=1.0) -> paddle.Tensor:
|
|
"""log_softmax of frame activations
|
|
Args:
|
|
Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
|
|
Returns:
|
|
paddle.Tensor: log softmax applied 3d tensor (B, Tmax, odim)
|
|
"""
|
|
return F.log_softmax(self.ctc_lo(hs_pad) / temperature, axis=2)
|
|
|
|
def argmax(self, hs_pad: paddle.Tensor) -> paddle.Tensor:
|
|
"""argmax of frame activations
|
|
Args:
|
|
paddle.Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
|
|
Returns:
|
|
paddle.Tensor: argmax applied 2d tensor (B, Tmax)
|
|
"""
|
|
return paddle.argmax(self.ctc_lo(hs_pad), dim=2)
|
|
|
|
def forced_align(self,
|
|
ctc_probs: paddle.Tensor,
|
|
y: paddle.Tensor,
|
|
blank_id=0) -> list:
|
|
"""ctc forced alignment.
|
|
Args:
|
|
ctc_probs (paddle.Tensor): hidden state sequence, 2d tensor (T, D)
|
|
y (paddle.Tensor): label id sequence tensor, 1d tensor (L)
|
|
blank_id (int): blank symbol index
|
|
Returns:
|
|
paddle.Tensor: best alignment result, (T).
|
|
"""
|
|
return ctc_utils.forced_align(ctc_probs, y, blank_id)
|
|
|
|
|
|
class CTCDecoder(CTCDecoderBase):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
# CTCDecoder LM Score handle
|
|
self._ext_scorer = None
|
|
self.beam_search_decoder = None
|
|
|
|
def _decode_batch_greedy_offline(self, probs_split, vocab_list):
|
|
"""This function will be deprecated in future.
|
|
Decode by best path for a batch of probs matrix input.
|
|
:param probs_split: List of 2-D probability matrix, and each consists
|
|
of prob vectors for one speech utterancce.
|
|
:param probs_split: List of matrix
|
|
:param vocab_list: List of tokens in the vocabulary, for decoding.
|
|
:type vocab_list: list
|
|
:return: List of transcription texts.
|
|
:rtype: List of str
|
|
"""
|
|
results = []
|
|
for i, probs in enumerate(probs_split):
|
|
output_transcription = ctc_greedy_decoding(
|
|
probs_seq=probs, vocabulary=vocab_list, blank_id=self.blank_id)
|
|
results.append(output_transcription)
|
|
return results
|
|
|
|
def _init_ext_scorer(self, beam_alpha, beam_beta, language_model_path,
|
|
vocab_list):
|
|
"""Initialize the external scorer.
|
|
:param beam_alpha: Parameter associated with language model.
|
|
:type beam_alpha: float
|
|
:param beam_beta: Parameter associated with word count.
|
|
:type beam_beta: float
|
|
:param language_model_path: Filepath for language model. If it is
|
|
empty, the external scorer will be set to
|
|
None, and the decoding method will be pure
|
|
beam search without scorer.
|
|
:type language_model_path: str|None
|
|
:param vocab_list: List of tokens in the vocabulary, for decoding.
|
|
:type vocab_list: list
|
|
"""
|
|
# init once
|
|
if self._ext_scorer is not None:
|
|
return
|
|
|
|
if language_model_path != '':
|
|
logger.info("begin to initialize the external scorer "
|
|
"for decoding")
|
|
self._ext_scorer = Scorer(beam_alpha, beam_beta,
|
|
language_model_path, vocab_list)
|
|
lm_char_based = self._ext_scorer.is_character_based()
|
|
lm_max_order = self._ext_scorer.get_max_order()
|
|
lm_dict_size = self._ext_scorer.get_dict_size()
|
|
logger.info("language model: "
|
|
"is_character_based = %d," % lm_char_based +
|
|
" max_order = %d," % lm_max_order + " dict_size = %d" %
|
|
lm_dict_size)
|
|
logger.info("end initializing scorer")
|
|
else:
|
|
self._ext_scorer = None
|
|
logger.info("no language model provided, "
|
|
"decoding by pure beam search without scorer.")
|
|
|
|
def _decode_batch_beam_search_offline(
|
|
self, probs_split, beam_alpha, beam_beta, beam_size, cutoff_prob,
|
|
cutoff_top_n, vocab_list, num_processes):
|
|
"""
|
|
This function will be deprecated in future.
|
|
Decode by beam search for a batch of probs matrix input.
|
|
:param probs_split: List of 2-D probability matrix, and each consists
|
|
of prob vectors for one speech utterancce.
|
|
:param probs_split: List of matrix
|
|
:param beam_alpha: Parameter associated with language model.
|
|
:type beam_alpha: float
|
|
:param beam_beta: Parameter associated with word count.
|
|
:type beam_beta: float
|
|
:param beam_size: Width for Beam search.
|
|
:type beam_size: int
|
|
:param cutoff_prob: Cutoff probability in pruning,
|
|
default 1.0, no pruning.
|
|
:type cutoff_prob: float
|
|
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
|
|
characters with highest probs in vocabulary will be
|
|
used in beam search, default 40.
|
|
:type cutoff_top_n: int
|
|
:param vocab_list: List of tokens in the vocabulary, for decoding.
|
|
:type vocab_list: list
|
|
:param num_processes: Number of processes (CPU) for decoder.
|
|
:type num_processes: int
|
|
:return: List of transcription texts.
|
|
:rtype: List of str
|
|
"""
|
|
if self._ext_scorer is not None:
|
|
self._ext_scorer.reset_params(beam_alpha, beam_beta)
|
|
|
|
# beam search decode
|
|
num_processes = min(num_processes, len(probs_split))
|
|
beam_search_results = ctc_beam_search_decoding_batch(
|
|
probs_split=probs_split,
|
|
vocabulary=vocab_list,
|
|
beam_size=beam_size,
|
|
num_processes=num_processes,
|
|
ext_scoring_func=self._ext_scorer,
|
|
cutoff_prob=cutoff_prob,
|
|
cutoff_top_n=cutoff_top_n,
|
|
blank_id=self.blank_id)
|
|
|
|
results = [result[0][1] for result in beam_search_results]
|
|
return results
|
|
|
|
def init_decoder(self, batch_size, vocab_list, decoding_method,
|
|
lang_model_path, beam_alpha, beam_beta, beam_size,
|
|
cutoff_prob, cutoff_top_n, num_processes):
|
|
"""
|
|
init ctc decoders
|
|
Args:
|
|
batch_size(int): Batch size for input data
|
|
vocab_list (list): List of tokens in the vocabulary, for decoding
|
|
decoding_method (str): ctc_beam_search
|
|
lang_model_path (str): language model path
|
|
beam_alpha (float): beam_alpha
|
|
beam_beta (float): beam_beta
|
|
beam_size (int): beam_size
|
|
cutoff_prob (float): cutoff probability in beam search
|
|
cutoff_top_n (int): cutoff_top_n
|
|
num_processes (int): num_processes
|
|
|
|
Raises:
|
|
ValueError: when decoding_method not support.
|
|
|
|
Returns:
|
|
CTCBeamSearchDecoder
|
|
"""
|
|
self.batch_size = batch_size
|
|
self.vocab_list = vocab_list
|
|
self.decoding_method = decoding_method
|
|
self.beam_size = beam_size
|
|
self.cutoff_prob = cutoff_prob
|
|
self.cutoff_top_n = cutoff_top_n
|
|
self.num_processes = num_processes
|
|
if decoding_method == "ctc_beam_search":
|
|
self._init_ext_scorer(beam_alpha, beam_beta, lang_model_path,
|
|
vocab_list)
|
|
if self.beam_search_decoder is None:
|
|
self.beam_search_decoder = self.get_decoder(
|
|
vocab_list, batch_size, beam_alpha, beam_beta, beam_size,
|
|
num_processes, cutoff_prob, cutoff_top_n)
|
|
return self.beam_search_decoder
|
|
elif decoding_method == "ctc_greedy":
|
|
self._init_ext_scorer(beam_alpha, beam_beta, lang_model_path,
|
|
vocab_list)
|
|
else:
|
|
raise ValueError(f"Not support: {decoding_method}")
|
|
|
|
def decode_probs_offline(self, probs, logits_lens, vocab_list,
|
|
decoding_method, lang_model_path, beam_alpha,
|
|
beam_beta, beam_size, cutoff_prob, cutoff_top_n,
|
|
num_processes):
|
|
"""
|
|
This function will be deprecated in future.
|
|
ctc decoding with probs.
|
|
Args:
|
|
probs (Tensor): activation after softmax
|
|
logits_lens (Tensor): audio output lens
|
|
vocab_list (list): List of tokens in the vocabulary, for decoding
|
|
decoding_method (str): ctc_beam_search
|
|
lang_model_path (str): language model path
|
|
beam_alpha (float): beam_alpha
|
|
beam_beta (float): beam_beta
|
|
beam_size (int): beam_size
|
|
cutoff_prob (float): cutoff probability in beam search
|
|
cutoff_top_n (int): cutoff_top_n
|
|
num_processes (int): num_processes
|
|
|
|
Raises:
|
|
ValueError: when decoding_method not support.
|
|
|
|
Returns:
|
|
List[str]: transcripts.
|
|
"""
|
|
logger.warn(
|
|
"This function will be deprecated in future: decode_probs_offline")
|
|
probs_split = [probs[i, :l, :] for i, l in enumerate(logits_lens)]
|
|
if decoding_method == "ctc_greedy":
|
|
result_transcripts = self._decode_batch_greedy_offline(
|
|
probs_split=probs_split, vocab_list=vocab_list)
|
|
elif decoding_method == "ctc_beam_search":
|
|
result_transcripts = self._decode_batch_beam_search_offline(
|
|
probs_split=probs_split,
|
|
beam_alpha=beam_alpha,
|
|
beam_beta=beam_beta,
|
|
beam_size=beam_size,
|
|
cutoff_prob=cutoff_prob,
|
|
cutoff_top_n=cutoff_top_n,
|
|
vocab_list=vocab_list,
|
|
num_processes=num_processes)
|
|
else:
|
|
raise ValueError(f"Not support: {decoding_method}")
|
|
return result_transcripts
|
|
|
|
def get_decoder(self, vocab_list, batch_size, beam_alpha, beam_beta,
|
|
beam_size, num_processes, cutoff_prob, cutoff_top_n):
|
|
"""
|
|
init get ctc decoder
|
|
Args:
|
|
vocab_list (list): List of tokens in the vocabulary, for decoding.
|
|
batch_size(int): Batch size for input data
|
|
beam_alpha (float): beam_alpha
|
|
beam_beta (float): beam_beta
|
|
beam_size (int): beam_size
|
|
num_processes (int): num_processes
|
|
cutoff_prob (float): cutoff probability in beam search
|
|
cutoff_top_n (int): cutoff_top_n
|
|
|
|
Raises:
|
|
ValueError: when decoding_method not support.
|
|
|
|
Returns:
|
|
CTCBeamSearchDecoder
|
|
"""
|
|
num_processes = min(num_processes, batch_size)
|
|
if self._ext_scorer is not None:
|
|
self._ext_scorer.reset_params(beam_alpha, beam_beta)
|
|
if self.decoding_method == "ctc_beam_search":
|
|
beam_search_decoder = CTCBeamSearchDecoder(
|
|
vocab_list, batch_size, beam_size, num_processes, cutoff_prob,
|
|
cutoff_top_n, self._ext_scorer, self.blank_id)
|
|
else:
|
|
raise ValueError(f"Not support: {decoding_method}")
|
|
return beam_search_decoder
|
|
|
|
def next(self, probs, logits_lens):
|
|
"""
|
|
Input probs into ctc decoder
|
|
Args:
|
|
probs (list(list(float))): probs for a batch of data
|
|
logits_lens (list(int)): logits lens for a batch of data
|
|
Raises:
|
|
Exception: when the ctc decoder is not initialized
|
|
ValueError: when decoding_method not support.
|
|
"""
|
|
|
|
if self.beam_search_decoder is None:
|
|
raise Exception(
|
|
"You need to initialize the beam_search_decoder firstly")
|
|
beam_search_decoder = self.beam_search_decoder
|
|
|
|
has_value = (logits_lens > 0).tolist()
|
|
has_value = [
|
|
"true" if has_value[i] is True else "false"
|
|
for i in range(len(has_value))
|
|
]
|
|
probs_split = [
|
|
probs[i, :l, :].tolist() if has_value[i] else probs[i].tolist()
|
|
for i, l in enumerate(logits_lens)
|
|
]
|
|
if self.decoding_method == "ctc_beam_search":
|
|
beam_search_decoder.next(probs_split, has_value)
|
|
else:
|
|
raise ValueError(f"Not support: {decoding_method}")
|
|
|
|
return
|
|
|
|
def decode(self):
|
|
"""
|
|
Get the decoding result
|
|
Raises:
|
|
Exception: when the ctc decoder is not initialized
|
|
ValueError: when decoding_method not support.
|
|
Returns:
|
|
results_best (list(str)): The best result for a batch of data
|
|
results_beam (list(list(str))): The beam search result for a batch of data
|
|
"""
|
|
if self.beam_search_decoder is None:
|
|
raise Exception(
|
|
"You need to initialize the beam_search_decoder firstly")
|
|
|
|
beam_search_decoder = self.beam_search_decoder
|
|
if self.decoding_method == "ctc_beam_search":
|
|
batch_beam_results = beam_search_decoder.decode()
|
|
batch_beam_results = [[(res[0], res[1]) for res in beam_results]
|
|
for beam_results in batch_beam_results]
|
|
results_best = [result[0][1] for result in batch_beam_results]
|
|
results_beam = [[trans[1] for trans in result]
|
|
for result in batch_beam_results]
|
|
|
|
else:
|
|
raise ValueError(f"Not support: {decoding_method}")
|
|
|
|
return results_best, results_beam
|
|
|
|
def reset_decoder(self,
|
|
batch_size=-1,
|
|
beam_size=-1,
|
|
num_processes=-1,
|
|
cutoff_prob=-1.0,
|
|
cutoff_top_n=-1):
|
|
if batch_size > 0:
|
|
self.batch_size = batch_size
|
|
if beam_size > 0:
|
|
self.beam_size = beam_size
|
|
if num_processes > 0:
|
|
self.num_processes = num_processes
|
|
if cutoff_prob > 0:
|
|
self.cutoff_prob = cutoff_prob
|
|
if cutoff_top_n > 0:
|
|
self.cutoff_top_n = cutoff_top_n
|
|
"""
|
|
Reset the decoder state
|
|
Args:
|
|
batch_size(int): Batch size for input data
|
|
beam_size (int): beam_size
|
|
num_processes (int): num_processes
|
|
cutoff_prob (float): cutoff probability in beam search
|
|
cutoff_top_n (int): cutoff_top_n
|
|
Raises:
|
|
Exception: when the ctc decoder is not initialized
|
|
"""
|
|
if self.beam_search_decoder is None:
|
|
raise Exception(
|
|
"You need to initialize the beam_search_decoder firstly")
|
|
self.beam_search_decoder.reset_state(
|
|
self.batch_size, self.beam_size, self.num_processes,
|
|
self.cutoff_prob, self.cutoff_top_n)
|
|
|
|
def del_decoder(self):
|
|
"""
|
|
Delete the decoder
|
|
"""
|
|
if self.beam_search_decoder is not None:
|
|
del self.beam_search_decoder
|
|
self.beam_search_decoder = None
|