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102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
# Copyright 2023 Xiaomi Corp. (Author: Liyong Guo)
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#
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# See ../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import itertools
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import torch
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from typing import List, Tuple
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class WakeupWordTokenizer(object):
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def __init__(
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self,
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wakeup_word: str,
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wakeup_word_tokens: List[int],
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) -> None:
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"""
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Args:
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wakeup_word: content of positive samples.
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A sample will be treated as a negative sample unless its content
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is exactly the same to key_words.
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wakeup_word_tokens: A list of int representing token ids of wakeup_word.
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For example: the pronunciation of "你好米雅" is
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"n i h ao m i y a".
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Suppose we are using following lexicon:
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blk 0
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unk 1
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n 2
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i 3
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h 4
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ao 5
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m 6
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y 7
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a 8
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Then wakeup_word_tokens for "你好米雅" is:
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n i h ao m i y a
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[2, 3, 4, 5, 6, 3, 7, 8]
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"""
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super().__init__()
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assert wakeup_word is not None
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assert wakeup_word_tokens is not None
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assert (
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0 not in wakeup_word_tokens
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), f"0 is kept for blank. Please Remove 0 from {wakeup_word_tokens}"
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assert 1 not in wakeup_word_tokens, (
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f"1 is kept for unknown and negative samples. "
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f" Please Remove 1 from {wakeup_word_tokens}"
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)
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self.wakeup_word = wakeup_word
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self.wakeup_word_tokens = wakeup_word_tokens
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self.positive_number_tokens = len(wakeup_word_tokens)
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self.negative_word_tokens = [1]
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self.negative_number_tokens = 1
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def texts_to_token_ids(
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self, texts: List[str]
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) -> Tuple[torch.Tensor, torch.Tensor, int]:
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"""Convert a list of texts to parameters needed by CTC loss.
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Args:
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texts:
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It is a list of strings,
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each element is a reference text for an audio.
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Returns:
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Return a tuple of 3 elements.
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The first one is torch.Tensor(List[List[int]]),
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each List[int] is tokens sequence for each reference text.
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The second one is number of tokens for each sample,
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mainly used by CTC loss.
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The last one is number_positive_samples,
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used to track proportion of positive samples in each batch.
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"""
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batch_token_ids = []
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target_lengths = []
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number_positive_samples = 0
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for utt_text in texts:
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if utt_text == self.wakeup_word:
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batch_token_ids.extend(self.wakeup_word_tokens)
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target_lengths.append(self.positive_number_tokens)
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number_positive_samples += 1
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else:
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batch_token_ids.extend(self.negative_word_tokens)
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target_lengths.append(self.negative_number_tokens)
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target = torch.tensor(batch_token_ids)
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target_lengths = torch.tensor(target_lengths)
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return target, target_lengths, number_positive_samples
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