aud22_41.yaml

- en: Forced alignment for multilingual data
  id: totrans-0
  prefs:
  - PREF_H1
  type: TYPE_NORMAL
  zh: 多语言数据的强制对齐
- en: 原文：[https://pytorch.org/audio/stable/tutorials/forced_alignment_for_multilingual_data_tutorial.html](https://pytorch.org/audio/stable/tutorials/forced_alignment_for_multilingual_data_tutorial.html)
  id: totrans-1
  prefs:
  - PREF_BQ
  type: TYPE_NORMAL
  zh: 原文：[https://pytorch.org/audio/stable/tutorials/forced_alignment_for_multilingual_data_tutorial.html](https://pytorch.org/audio/stable/tutorials/forced_alignment_for_multilingual_data_tutorial.html)
- en: Note
  id: totrans-2
  prefs: []
  type: TYPE_NORMAL
  zh: 注意
- en: Click [here](#sphx-glr-download-tutorials-forced-alignment-for-multilingual-data-tutorial-py)
    to download the full example code
  id: totrans-3
  prefs: []
  type: TYPE_NORMAL
  zh: 点击[这里](#sphx-glr-download-tutorials-forced-alignment-for-multilingual-data-tutorial-py)下载完整示例代码
- en: '**Authors**: [Xiaohui Zhang](mailto:xiaohuizhang%40meta.com), [Moto Hira](mailto:moto%40meta.com).'
  id: totrans-4
  prefs: []
  type: TYPE_NORMAL
  zh: '**作者**：[Xiaohui Zhang](mailto:xiaohuizhang%40meta.com), [Moto Hira](mailto:moto%40meta.com)。'
- en: This tutorial shows how to align transcript to speech for non-English languages.
  id: totrans-5
  prefs: []
  type: TYPE_NORMAL
  zh: 本教程展示了如何为非英语语言对齐转录和语音。
- en: The process of aligning non-English (normalized) transcript is identical to
    aligning English (normalized) transcript, and the process for English is covered
    in detail in [CTC forced alignment tutorial](./ctc_forced_alignment_api_tutorial.html).
    In this tutorial, we use TorchAudio’s high-level API, [`torchaudio.pipelines.Wav2Vec2FABundle`](../generated/torchaudio.pipelines.Wav2Vec2FABundle.html#torchaudio.pipelines.Wav2Vec2FABundle
    "torchaudio.pipelines.Wav2Vec2FABundle"), which packages the pre-trained model,
    tokenizer and aligner, to perform the forced alignment with less code.
  id: totrans-6
  prefs: []
  type: TYPE_NORMAL
  zh: 对齐非英语（标准化）转录的过程与对齐英语（标准化）转录的过程相同，对于英语的过程在[CTC强制对齐教程](./ctc_forced_alignment_api_tutorial.html)中有详细介绍。在本教程中，我们使用TorchAudio的高级API，[`torchaudio.pipelines.Wav2Vec2FABundle`](../generated/torchaudio.pipelines.Wav2Vec2FABundle.html#torchaudio.pipelines.Wav2Vec2FABundle
    "torchaudio.pipelines.Wav2Vec2FABundle")，它打包了预训练模型、分词器和对齐器，以更少的代码执行强制对齐。
- en: '[PRE0]'
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  prefs: []
  type: TYPE_PRE
  zh: '[PRE0]'
- en: '[PRE1]'
  id: totrans-8
  prefs: []
  type: TYPE_PRE
  zh: '[PRE1]'
- en: '[PRE2]'
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  prefs: []
  type: TYPE_PRE
  zh: '[PRE2]'
- en: Creating the pipeline[](#creating-the-pipeline "Permalink to this heading")
  id: totrans-10
  prefs:
  - PREF_H2
  type: TYPE_NORMAL
  zh: 创建流程[](#creating-the-pipeline "Permalink to this heading")
- en: First, we instantiate the model and pre/post-processing pipelines.
  id: totrans-11
  prefs: []
  type: TYPE_NORMAL
  zh: 首先，我们实例化模型和前/后处理流程。
- en: The following diagram illustrates the process of alignment.
  id: totrans-12
  prefs: []
  type: TYPE_NORMAL
  zh: 以下图示了对齐的过程。
- en: '![https://download.pytorch.org/torchaudio/doc-assets/pipelines-wav2vec2fabundle.png](../Images/81159a1c90b6bf1cc96789ecb75c13f0.png)'
  id: totrans-13
  prefs: []
  type: TYPE_IMG
  zh: '![https://download.pytorch.org/torchaudio/doc-assets/pipelines-wav2vec2fabundle.png](../Images/81159a1c90b6bf1cc96789ecb75c13f0.png)'
- en: The waveform is passed to an acoustic model, which produces the sequence of
    probability distribution of tokens. The transcript is passed to tokenizer, which
    converts the transcript to sequence of tokens. Aligner takes the results from
    the acoustic model and the tokenizer and generate timestamps for each token.
  id: totrans-14
  prefs: []
  type: TYPE_NORMAL
  zh: 波形被传递给声学模型，该模型生成标记的概率分布序列。转录被传递给分词器，将转录转换为标记序列。对齐器获取声学模型和分词器的结果，并为每个标记生成时间戳。
- en: Note
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  prefs: []
  type: TYPE_NORMAL
  zh: 注意
- en: This process expects that the input transcript is already normalized. The process
    of normalization, which involves romanization of non-English languages, is language-dependent,
    so it is not covered in this tutorial, but we will breifly look into it.
  id: totrans-16
  prefs: []
  type: TYPE_NORMAL
  zh: 该过程期望输入的转录已经被标准化。标准化的过程涉及非英语语言的罗马化，是与语言相关的，因此本教程不涵盖，但我们将简要介绍。
- en: The acoustic model and the tokenizer must use the same set of tokens. To facilitate
    the creation of matching processors, [`Wav2Vec2FABundle`](../generated/torchaudio.pipelines.Wav2Vec2FABundle.html#torchaudio.pipelines.Wav2Vec2FABundle
    "torchaudio.pipelines.Wav2Vec2FABundle") associates a pre-trained accoustic model
    and a tokenizer. [`torchaudio.pipelines.MMS_FA`](../generated/torchaudio.pipelines.MMS_FA.html#torchaudio.pipelines.MMS_FA
    "torchaudio.pipelines.MMS_FA") is one of such instance.
  id: totrans-17
  prefs: []
  type: TYPE_NORMAL
  zh: 声学模型和分词器必须使用相同的标记集。为了便于创建匹配的处理器，[`Wav2Vec2FABundle`](../generated/torchaudio.pipelines.Wav2Vec2FABundle.html#torchaudio.pipelines.Wav2Vec2FABundle
    "torchaudio.pipelines.Wav2Vec2FABundle")关联了一个预训练的声学模型和一个分词器。[`torchaudio.pipelines.MMS_FA`](../generated/torchaudio.pipelines.MMS_FA.html#torchaudio.pipelines.MMS_FA
    "torchaudio.pipelines.MMS_FA")就是这样一个实例。
- en: The following code instantiates a pre-trained acoustic model, a tokenizer which
    uses the same set of tokens as the model, and an aligner.
  id: totrans-18
  prefs: []
  type: TYPE_NORMAL
  zh: 以下代码实例化了一个预训练的声学模型，一个使用与模型相同的标记集的分词器，以及一个对齐器。
- en: '[PRE3]'
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  type: TYPE_PRE
  zh: '[PRE3]'
- en: Note
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  type: TYPE_NORMAL
  zh: 注意
- en: The model instantiated by [`MMS_FA`](../generated/torchaudio.pipelines.MMS_FA.html#torchaudio.pipelines.MMS_FA
    "torchaudio.pipelines.MMS_FA")’s [`get_model()`](../generated/torchaudio.pipelines.Wav2Vec2FABundle.html#torchaudio.pipelines.Wav2Vec2FABundle.get_model
    "torchaudio.pipelines.Wav2Vec2FABundle.get_model") method by default includes
    the feature dimension for `<star>` token. You can disable this by passing `with_star=False`.
  id: totrans-21
  prefs: []
  type: TYPE_NORMAL
  zh: 通过[`MMS_FA`](../generated/torchaudio.pipelines.MMS_FA.html#torchaudio.pipelines.MMS_FA
    "torchaudio.pipelines.MMS_FA")的[`get_model()`](../generated/torchaudio.pipelines.Wav2Vec2FABundle.html#torchaudio.pipelines.Wav2Vec2FABundle.get_model
    "torchaudio.pipelines.Wav2Vec2FABundle.get_model")方法实例化的模型默认包含`<star>`标记的特征维度。您可以通过传递`with_star=False`来禁用此功能。
- en: The acoustic model of [`MMS_FA`](../generated/torchaudio.pipelines.MMS_FA.html#torchaudio.pipelines.MMS_FA
    "torchaudio.pipelines.MMS_FA") was created and open-sourced as part of the research
    project, [Scaling Speech Technology to 1,000+ Languages](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/).
    It was trained with 23,000 hours of audio from 1100+ languages.
  id: totrans-22
  prefs: []
  type: TYPE_NORMAL
  zh: '[`MMS_FA`](../generated/torchaudio.pipelines.MMS_FA.html#torchaudio.pipelines.MMS_FA
    "torchaudio.pipelines.MMS_FA")的声学模型是作为研究项目[将语音技术扩展到1000多种语言](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/)的一部分创建并开源的。它使用来自1100多种语言的23000小时音频进行训练。'
- en: The tokenizer simply maps the normalized characters to integers. You can check
    the mapping as follow;
  id: totrans-23
  prefs: []
  type: TYPE_NORMAL
  zh: 分词器简单地将标准化字符映射为整数。您可以按以下方式检查映射；
- en: '[PRE4]'
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  type: TYPE_PRE
  zh: '[PRE4]'
- en: '[PRE5]'
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  type: TYPE_PRE
  zh: '[PRE5]'
- en: The aligner internally uses [`torchaudio.functional.forced_align()`](../generated/torchaudio.functional.forced_align.html#torchaudio.functional.forced_align
    "torchaudio.functional.forced_align") and [`torchaudio.functional.merge_tokens()`](../generated/torchaudio.functional.merge_tokens.html#torchaudio.functional.merge_tokens
    "torchaudio.functional.merge_tokens") to infer the time stamps of the input tokens.
  id: totrans-26
  prefs: []
  type: TYPE_NORMAL
  zh: 对齐器内部使用[`torchaudio.functional.forced_align()`](../generated/torchaudio.functional.forced_align.html#torchaudio.functional.forced_align
    "torchaudio.functional.forced_align")和[`torchaudio.functional.merge_tokens()`](../generated/torchaudio.functional.merge_tokens.html#torchaudio.functional.merge_tokens
    "torchaudio.functional.merge_tokens")来推断输入标记的时间戳。
- en: The detail of the underlying mechanism is covered in [CTC forced alignment API
    tutorial](./ctc_forced_alignment_api_tutorial.html), so please refer to it.
  id: totrans-27
  prefs: []
  type: TYPE_NORMAL
  zh: 底层机制的详细信息在[CTC强制对齐API教程](./ctc_forced_alignment_api_tutorial.html)中介绍，请参考。
- en: We define a utility function that performs the forced alignment with the above
    model, the tokenizer and the aligner.
  id: totrans-28
  prefs: []
  type: TYPE_NORMAL
  zh: 我们定义了一个实用函数，使用上述模型、分词器和对齐器执行强制对齐。
- en: '[PRE6]'
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  zh: '[PRE6]'
- en: We also define utility functions for plotting the result and previewing the
    audio segments.
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  type: TYPE_NORMAL
  zh: 我们还定义了用于绘制结果和预览音频片段的实用函数。
- en: '[PRE7]'
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  type: TYPE_PRE
  zh: '[PRE7]'
- en: '[PRE8]'
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  type: TYPE_PRE
  zh: '[PRE8]'
- en: Normalizing the transcript[](#normalizing-the-transcript "Permalink to this
    heading")
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  prefs:
  - PREF_H2
  type: TYPE_NORMAL
  zh: 将文本标准化[](#normalizing-the-transcript "跳转到此标题")
- en: The transcripts passed to the pipeline must be normalized beforehand. The exact
    process of normalization depends on language.
  id: totrans-34
  prefs: []
  type: TYPE_NORMAL
  zh: 传递到流水线的文本必须事先进行标准化。标准化的确切过程取决于语言。
- en: Languages that do not have explicit word boundaries (such as Chinese, Japanese
    and Korean) require segmentation first. There are dedicated tools for this, but
    let’s say we have segmented transcript.
  id: totrans-35
  prefs: []
  type: TYPE_NORMAL
  zh: 没有明确单词边界的语言（如中文、日文和韩文）需要首先进行分词。有专门的工具可以做到这一点，但假设我们已经对文本进行了分词。
- en: The first step of normalization is romanization. [uroman](https://github.com/isi-nlp/uroman)
    is a tool that supports many languages.
  id: totrans-36
  prefs: []
  type: TYPE_NORMAL
  zh: 标准化的第一步是罗马化。[uroman](https://github.com/isi-nlp/uroman)是一个支持多种语言的工具。
- en: Here is a BASH commands to romanize the input text file and write the output
    to another text file using `uroman`.
  id: totrans-37
  prefs: []
  type: TYPE_NORMAL
  zh: 以下是一个BASH命令，用于罗马化输入文本文件并使用`uroman`将输出写入另一个文本文件。
- en: '[PRE9]'
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  prefs: []
  type: TYPE_PRE
  zh: '[PRE9]'
- en: '[PRE10]'
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  type: TYPE_PRE
  zh: '[PRE10]'
- en: The next step is to remove non-alphabets and punctuations. The following snippet
    normalizes the romanized transcript.
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  prefs: []
  type: TYPE_NORMAL
  zh: 下一步是删除非字母和标点符号。以下代码段标准化了罗马化的文本。
- en: '[PRE11]'
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  prefs: []
  type: TYPE_PRE
  zh: '[PRE11]'
- en: Running the script on the above exanple produces the following.
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  type: TYPE_NORMAL
  zh: 在上述示例上运行脚本会产生以下结果。
- en: '[PRE12]'
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  type: TYPE_PRE
  zh: '[PRE12]'
- en: Note that, in this example, since “1882” was not romanized by `uroman`, it was
    removed in the normalization step. To avoid this, one needs to romanize numbers,
    but this is known to be a non-trivial task.
  id: totrans-44
  prefs: []
  type: TYPE_NORMAL
  zh: 请注意，在此示例中，“1882”未被`uroman`罗马化，因此在标准化步骤中被移除。为了避免这种情况，需要罗马化数字，但这被认为是一个非常困难的任务。
- en: Aligning transcripts to speech[](#aligning-transcripts-to-speech "Permalink
    to this heading")
  id: totrans-45
  prefs:
  - PREF_H2
  type: TYPE_NORMAL
  zh: 将文本对齐到语音[](#aligning-transcripts-to-speech "跳转到此标题")
- en: Now we perform the forced alignment for multiple languages.
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  type: TYPE_NORMAL
  zh: 现在我们为多种语言执行强制对齐。
- en: German[](#german "Permalink to this heading")
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  - PREF_H3
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  zh: 德语[](#german "跳转到此标题")
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- en: '![Emission](../Images/ae919088b5d459e4f9ccbdc7d3fc7a22.png)'
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  zh: '![发射](../Images/ae919088b5d459e4f9ccbdc7d3fc7a22.png)'
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
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  zh: '[PRE27]'
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE29]'
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  zh: '[PRE29]'
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  zh: '[PRE30]'
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE31]'
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  zh: '[PRE31]'
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  zh: '[PRE32]'
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
- en: Chinese[](#chinese "Permalink to this heading")
  id: totrans-87
  prefs:
  - PREF_H3
  type: TYPE_NORMAL
  zh: 中文[](#chinese "跳转到此标题")
- en: Chinese is a character-based language, and there is not explicit word-level
    tokenization (separated by spaces) in its raw written form. In order to obtain
    word level alignments, you need to first tokenize the transcripts at the word
    level using a word tokenizer like [“Stanford Tokenizer”](https://michelleful.github.io/code-blog/2015/09/10/parsing-chinese-with-stanford/).
    However this is not needed if you only want character-level alignments.
  id: totrans-88
  prefs: []
  type: TYPE_NORMAL
  zh: 中文是一种基于字符的语言，在其原始书面形式中没有明确的单词级标记化（由空格分隔）。为了获得单词级别的对齐，您需要首先使用像[“斯坦福分词器”](https://michelleful.github.io/code-blog/2015/09/10/parsing-chinese-with-stanford/)这样的单词分词器对文本进行单词级别的标记化。但是，如果您只需要字符级别的对齐，则不需要这样做。
- en: '[PRE33]'
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  zh: '[PRE36]'
- en: '![Emission](../Images/331a9b3d8f84020b493b73104b8177af.png)'
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  prefs: []
  type: TYPE_IMG
  zh: '![发射](../Images/331a9b3d8f84020b493b73104b8177af.png)'
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  zh: '[PRE37]'
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE38]'
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- en: null
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- en: '[PRE54]'
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- en: Polish[](#polish "Permalink to this heading")
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  zh: 波兰语[](#polish "跳转到此标题")
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- en: Portuguese[](#portuguese "Permalink to this heading")
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  - PREF_H3
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  zh: 葡萄牙语[](#portuguese "跳转到此标题的永久链接")
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  zh: '[PRE88]'
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- en: null
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- en: '[PRE91]'
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  zh: '[PRE91]'
- en: null
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- en: '[PRE92]'
  id: totrans-205
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  zh: '[PRE92]'
- en: '[PRE93]'
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  zh: '[PRE93]'
- en: null
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE94]'
  id: totrans-209
  prefs: []
  type: TYPE_PRE
  zh: '[PRE94]'
- en: '[PRE95]'
  id: totrans-210
  prefs: []
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  zh: '[PRE95]'
- en: null
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- en: Your browser does not support the audio element.
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE96]'
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  zh: '[PRE96]'
- en: '[PRE97]'
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  zh: '[PRE97]'
- en: null
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- en: Your browser does not support the audio element.
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- en: Italian[](#italian "Permalink to this heading")
  id: totrans-217
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  - PREF_H3
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  zh: 意大利语[](#italian "跳转到此标题的永久链接")
- en: '[PRE98]'
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- en: '[PRE99]'
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  zh: '![发射](../Images/731d611433f5cf2db8c00bfa366eced7.png)'
- en: '[PRE101]'
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  zh: '[PRE101]'
- en: null
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  prefs: []
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE102]'
  id: totrans-225
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  zh: '[PRE102]'
- en: '[PRE103]'
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  zh: '[PRE103]'
- en: null
  id: totrans-227
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- en: Your browser does not support the audio element.
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  prefs: []
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE104]'
  id: totrans-229
  prefs: []
  type: TYPE_PRE
  zh: '[PRE104]'
- en: '[PRE105]'
  id: totrans-230
  prefs: []
  type: TYPE_PRE
  zh: '[PRE105]'
- en: null
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  prefs: []
  type: TYPE_NORMAL
- en: Your browser does not support the audio element.
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- en: '[PRE106]'
  id: totrans-233
  prefs: []
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  zh: '[PRE106]'
- en: '[PRE107]'
  id: totrans-234
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  zh: '[PRE107]'
- en: null
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  zh: 您的浏览器不支持音频元素。
- en: '[PRE108]'
  id: totrans-237
  prefs: []
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  zh: '[PRE108]'
- en: '[PRE109]'
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  prefs: []
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  zh: '[PRE109]'
- en: null
  id: totrans-239
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- en: '[PRE110]'
  id: totrans-241
  prefs: []
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  zh: '[PRE110]'
- en: '[PRE111]'
  id: totrans-242
  prefs: []
  type: TYPE_PRE
  zh: '[PRE111]'
- en: null
  id: totrans-243
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- en: Your browser does not support the audio element.
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- en: '[PRE112]'
  id: totrans-245
  prefs: []
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  zh: '[PRE112]'
- en: '[PRE113]'
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  zh: '[PRE113]'
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- en: Conclusion[](#conclusion "Permalink to this heading")
  id: totrans-249
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  - PREF_H2
  type: TYPE_NORMAL
  zh: 结论[](#conclusion "跳转到此标题的永久链接")
- en: In this tutorial, we looked at how to use torchaudio’s forced alignment API
    and a Wav2Vec2 pre-trained mulilingual acoustic model to align speech data to
    transcripts in five languages.
  id: totrans-250
  prefs: []
  type: TYPE_NORMAL
  zh: 在本教程中，我们看了如何使用torchaudio的强制对齐API和一个Wav2Vec2预训练的多语言声学模型来将五种语言的语音数据与文本对齐。
- en: Acknowledgement[](#acknowledgement "Permalink to this heading")
  id: totrans-251
  prefs:
  - PREF_H2
  type: TYPE_NORMAL
  zh: 致谢[](#acknowledgement "跳转到此标题的永久链接")
- en: Thanks to [Vineel Pratap](mailto:vineelkpratap%40meta.com) and [Zhaoheng Ni](mailto:zni%40meta.com)
    for developing and open-sourcing the forced aligner API.
  id: totrans-252
  prefs: []
  type: TYPE_NORMAL
  zh: 感谢[Vineel Pratap](mailto:vineelkpratap%40meta.com)和[Zhaoheng Ni](mailto:zni%40meta.com)开发并开源了强制对齐器API。
- en: '**Total running time of the script:** ( 0 minutes 4.115 seconds)'
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- en: '[`Download Python source code: forced_alignment_for_multilingual_data_tutorial.py`](../_downloads/a662d1f1f11633103b4b95ad4b68013c/forced_alignment_for_multilingual_data_tutorial.py)'
  id: totrans-254
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  zh: '[`下载Python源代码：forced_alignment_for_multilingual_data_tutorial.py`](../_downloads/a662d1f1f11633103b4b95ad4b68013c/forced_alignment_for_multilingual_data_tutorial.py)'
- en: '[`Download Jupyter notebook: forced_alignment_for_multilingual_data_tutorial.ipynb`](../_downloads/454ce4c8debdfeda1ab0ab945c52976d/forced_alignment_for_multilingual_data_tutorial.ipynb)'
  id: totrans-255
  prefs: []
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  zh: '[`下载Jupyter笔记本：forced_alignment_for_multilingual_data_tutorial.ipynb`](../_downloads/454ce4c8debdfeda1ab0ab945c52976d/forced_alignment_for_multilingual_data_tutorial.ipynb)'
- en: '[Gallery generated by Sphinx-Gallery](https://sphinx-gallery.github.io)'
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