# Copyright 2021 Mobvoi Inc. All Rights Reserved.
# 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 wenet(https://github.com/wenet-e2e/wenet)
from pathlib import Path
from typing import List
import numpy as np
import paddle
from paddlespeech.s2t.utils import text_grid
from paddlespeech.s2t.utils import utility
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ["forced_align", "remove_duplicates_and_blank", "insert_blank"]
[docs]def remove_duplicates_and_blank(hyp: List[int], blank_id=0) -> List[int]:
"""ctc alignment to ctc label ids.
"abaa-acee-" -> "abaace"
Args:
hyp (List[int]): hypotheses ids, (L)
blank_id (int, optional): blank id. Defaults to 0.
Returns:
List[int]: remove dupicate ids, then remove blank id.
"""
new_hyp: List[int] = []
cur = 0
while cur < len(hyp):
# add non-blank into new_hyp
if hyp[cur] != blank_id:
new_hyp.append(hyp[cur])
# skip repeat label
prev = cur
while cur < len(hyp) and hyp[cur] == hyp[prev]:
cur += 1
return new_hyp
[docs]def insert_blank(label: np.ndarray, blank_id: int=0) -> np.ndarray:
"""Insert blank token between every two label token.
"abcdefg" -> "-a-b-c-d-e-f-g-"
Args:
label ([np.ndarray]): label ids, List[int], (L).
blank_id (int, optional): blank id. Defaults to 0.
Returns:
[np.ndarray]: (2L+1).
"""
label = np.expand_dims(label, 1) #[L, 1]
blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + blank_id
label = np.concatenate([blanks, label], axis=1) #[L, 2]
label = label.reshape(-1) #[2L], -l-l-l
label = np.append(label, label[0]) #[2L + 1], -l-l-l-
return label
[docs]def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
blank_id=0) -> List[int]:
"""ctc forced alignment.
https://distill.pub/2017/ctc/
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:
List[int]: best alignment result, (T).
"""
y_insert_blank = insert_blank(y, blank_id) #(2L+1)
log_alpha = paddle.zeros(
(ctc_probs.shape[0], len(y_insert_blank))) #(T, 2L+1)
log_alpha = log_alpha - float('inf') # log of zero
# TODO(Hui Zhang): zeros not support paddle.int16
# self.__setitem_varbase__(item, value) When assign a value to a paddle.Tensor, the data type of the paddle.Tensor not support int16
state_path = (paddle.zeros(
(ctc_probs.shape[0], len(y_insert_blank)), dtype=paddle.int32) - 1
) # state path, Tuple((T, 2L+1))
# init start state
# TODO(Hui Zhang): VarBase.__getitem__() not support np.int64
log_alpha[0, 0] = ctc_probs[0][int(y_insert_blank[0])] # State-b, Sb
log_alpha[0, 1] = ctc_probs[0][int(y_insert_blank[1])] # State-nb, Snb
for t in range(1, ctc_probs.shape[0]): # T
for s in range(len(y_insert_blank)): # 2L+1
if y_insert_blank[s] == blank_id or s < 2 or y_insert_blank[
s] == y_insert_blank[s - 2]:
candidates = paddle.to_tensor(
[log_alpha[t - 1, s], log_alpha[t - 1, s - 1]])
prev_state = [s, s - 1]
else:
candidates = paddle.to_tensor([
log_alpha[t - 1, s],
log_alpha[t - 1, s - 1],
log_alpha[t - 1, s - 2],
])
prev_state = [s, s - 1, s - 2]
# TODO(Hui Zhang): VarBase.__getitem__() not support np.int64
log_alpha[t, s] = paddle.max(candidates) + ctc_probs[t][int(
y_insert_blank[s])]
state_path[t, s] = prev_state[paddle.argmax(candidates)]
# TODO(Hui Zhang): zeros not support paddle.int16
# self.__setitem_varbase__(item, value) When assign a value to a paddle.Tensor, the data type of the paddle.Tensor not support int16
state_seq = -1 * paddle.ones((ctc_probs.shape[0], 1), dtype=paddle.int32)
candidates = paddle.to_tensor([
log_alpha[-1, len(y_insert_blank) - 1], # Sb
log_alpha[-1, len(y_insert_blank) - 2] # Snb
])
prev_state = [len(y_insert_blank) - 1, len(y_insert_blank) - 2]
state_seq[-1] = prev_state[paddle.argmax(candidates)]
for t in range(ctc_probs.shape[0] - 2, -1, -1):
state_seq[t] = state_path[t + 1, state_seq[t + 1, 0]]
output_alignment = []
for t in range(0, ctc_probs.shape[0]):
output_alignment.append(y_insert_blank[state_seq[t, 0]])
return output_alignment
def ctc_align(config, model, dataloader, batch_size, stride_ms, token_dict,
result_file):
"""ctc alignment.
Args:
config (cfgNode): config
model (nn.Layer): U2 Model.
dataloader (io.DataLoader): dataloader.
batch_size (int): decoding batchsize.
stride_ms (int): audio feature stride in ms unit.
token_dict (List[str]): vocab list, e.g. ['blank', 'unk', 'a', 'b', '<eos>'].
result_file (str): alignment output file, e.g. /path/to/xxx.align.
"""
if batch_size > 1:
logger.fatal('alignment mode must be running with batch_size == 1')
sys.exit(1)
assert result_file and result_file.endswith('.align')
model.eval()
# conv subsampling rate
subsample = utility.get_subsample(config)
logger.info(f"Align Total Examples: {len(dataloader.dataset)}")
with open(result_file, 'w') as fout:
# one example in batch
for i, batch in enumerate(dataloader):
key, feat, feats_length, target, target_length = batch
# 1. Encoder
encoder_out, encoder_mask = model._forward_encoder(
feat, feats_length) # (B, maxlen, encoder_dim)
maxlen = encoder_out.shape[1]
ctc_probs = model.ctc.log_softmax(
encoder_out) # (1, maxlen, vocab_size)
# 2. alignment
ctc_probs = ctc_probs.squeeze(0)
target = target.squeeze(0)
alignment = forced_align(ctc_probs, target)
logger.info(f"align ids: {key[0]} {alignment}")
fout.write('{} {}\n'.format(key[0], alignment))
# 3. gen praat
# segment alignment
align_segs = text_grid.segment_alignment(alignment)
logger.info(f"align tokens: {key[0]}, {align_segs}")
# IntervalTier, List["start end token\n"]
tierformat = text_grid.align_to_tierformat(align_segs, subsample,
token_dict)
# write tier
align_output_path = Path(result_file).parent / "align"
align_output_path.mkdir(parents=True, exist_ok=True)
tier_path = align_output_path / (key[0] + ".tier")
with tier_path.open('w') as f:
f.writelines(tierformat)
# write textgrid
textgrid_path = align_output_path / (key[0] + ".TextGrid")
second_per_frame = 1. / (1000. /
stride_ms) # 25ms window, 10ms stride
second_per_example = (
len(alignment) + 1) * subsample * second_per_frame
text_grid.generate_textgrid(
maxtime=second_per_example,
intervals=tierformat,
output=str(textgrid_path))