73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
# Copyright 2021 Mobvoi Inc. All Rights Reserved.
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# Author: binbinzhang@mobvoi.com (Di Wu)
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import numpy as np
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import torch
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def insert_blank(label, blank_id=0):
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"""Insert blank token between every two label token."""
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label = np.expand_dims(label, 1)
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blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + blank_id
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label = np.concatenate([blanks, label], axis=1)
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label = label.reshape(-1)
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label = np.append(label, label[0])
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return label
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def forced_align(ctc_probs: torch.Tensor,
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y: torch.Tensor,
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blank_id=0) -> list:
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"""ctc forced alignment.
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Args:
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torch.Tensor ctc_probs: hidden state sequence, 2d tensor (T, D)
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torch.Tensor y: id sequence tensor 1d tensor (L)
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int blank_id: blank symbol index
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Returns:
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torch.Tensor: alignment result
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"""
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y_insert_blank = insert_blank(y, blank_id)
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log_alpha = torch.zeros((ctc_probs.size(0), len(y_insert_blank)))
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log_alpha = log_alpha - float('inf') # log of zero
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state_path = (torch.zeros(
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(ctc_probs.size(0), len(y_insert_blank)), dtype=torch.int16) - 1
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) # state path
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# init start state
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log_alpha[0, 0] = ctc_probs[0][y_insert_blank[0]]
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log_alpha[0, 1] = ctc_probs[0][y_insert_blank[1]]
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for t in range(1, ctc_probs.size(0)):
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for s in range(len(y_insert_blank)):
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if y_insert_blank[s] == blank_id or s < 2 or y_insert_blank[
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s] == y_insert_blank[s - 2]:
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candidates = torch.tensor(
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[log_alpha[t - 1, s], log_alpha[t - 1, s - 1]])
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prev_state = [s, s - 1]
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else:
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candidates = torch.tensor([
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log_alpha[t - 1, s],
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log_alpha[t - 1, s - 1],
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log_alpha[t - 1, s - 2],
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])
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prev_state = [s, s - 1, s - 2]
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log_alpha[t, s] = torch.max(candidates) + ctc_probs[t][y_insert_blank[s]]
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state_path[t, s] = prev_state[torch.argmax(candidates)]
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state_seq = -1 * torch.ones((ctc_probs.size(0), 1), dtype=torch.int16)
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candidates = torch.tensor([
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log_alpha[-1, len(y_insert_blank) - 1],
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log_alpha[-1, len(y_insert_blank) - 2]
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])
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prev_state = [len(y_insert_blank) - 1, len(y_insert_blank) - 2]
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state_seq[-1] = prev_state[torch.argmax(candidates)]
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for t in range(ctc_probs.size(0) - 2, -1, -1):
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state_seq[t] = state_path[t + 1, state_seq[t + 1, 0]]
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output_alignment = []
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for t in range(0, ctc_probs.size(0)):
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output_alignment.append(y_insert_blank[state_seq[t, 0]])
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return output_alignment
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