PaddleSpeech/paddlespeech/s2t/utils/ctc_utils.py

# 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 typing import List
from pathlib import Path
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"]


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


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


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))