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tutorials/forced_alignment_for_multilingual_data_tutorial

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多语言数据的强制对齐

作者：Xiaohui Zhang, Moto Hira。

警告

从 2.8 版本开始，我们正在重构 TorchAudio，以使其进入维护阶段。因此：

  • 本教程中描述的 API 在 2.8 版本中已被弃用，并将在 2.9 版本中移除。

  • PyTorch 用于音频和视频的解码和编码功能正在被整合到 TorchCodec 中。

请参阅 https://github.com/pytorch/audio/issues/3902 获取更多信息。

本教程展示了如何将非英语语言的文本与语音对齐。

对齐非英语（标准化）文本的过程与对齐英语（标准化）文本的过程相同，英语的对齐过程在CTC 强制对齐教程中详细介绍。在本教程中，我们使用 TorchAudio 的高级 API，torchaudio.pipelines.Wav2Vec2FABundle，它封装了预训练模型、分词器和对齐器，以更少的代码执行强制对齐。

import torch
import torchaudio

print(torch.__version__)
print(torchaudio.__version__)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

2.8.0+cu126
2.8.0
cuda

from typing import List

import IPython
import matplotlib.pyplot as plt

创建管道

首先，我们实例化模型和预/后处理管道。

下图说明了对齐过程。

https://download.pytorch.org/torchaudio/doc-assets/pipelines-wav2vec2fabundle.png

波形被传递给声学模型，声学模型生成标记的概率分布序列。文本被传递给分词器，分词器将文本转换为标记序列。对齐器接收声学模型和分词器的结果，并为每个标记生成时间戳。

注意

此过程要求输入文本已标准化。标准化的过程涉及非英语语言的罗马化，这是与语言相关的，因此本教程不涉及此内容，但我们将简要介绍一下。

声学模型和分词器必须使用相同的标记集。为了方便创建匹配的处理器，Wav2Vec2FABundle 将预训练的声学模型和分词器关联起来。torchaudio.pipelines.MMS_FA 就是其中一个实例。

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标记集的分词器，以及一个对齐器。

from torchaudio.pipelines import MMS_FA as bundle

model = bundle.get_model()
model.to(device)

tokenizer = bundle.get_tokenizer()
aligner = bundle.get_aligner()

注意

由MMS_FA的get_model()方法默认实例化的模型包含<star>标记的特征维度。您可以通过传递with_star=False来禁用此功能。

MMS_FA 的声学模型是在研究项目将语音技术扩展到 1,000 多种语言中创建并开源的。它使用来自 1100 多种语言的 23,000 小时音频进行训练。

分词器只是将规范化的字符映射到整数。您可以按如下方式检查映射；

print(bundle.get_dict())

{'-': 0, 'a': 1, 'i': 2, 'e': 3, 'n': 4, 'o': 5, 'u': 6, 't': 7, 's': 8, 'r': 9, 'm': 10, 'k': 11, 'l': 12, 'd': 13, 'g': 14, 'h': 15, 'y': 16, 'b': 17, 'p': 18, 'w': 19, 'c': 20, 'v': 21, 'j': 22, 'z': 23, 'f': 24, "'": 25, 'q': 26, 'x': 27, '*': 28}

对齐器内部使用torchaudio.functional.forced_align()和torchaudio.functional.merge_tokens()来推断输入标记的时间戳。

底层机制的详细信息在CTC 强制对齐 API 教程中介绍，请参考该教程。

我们定义了一个实用函数，用于使用上述模型、分词器和对齐器执行强制对齐。

def compute_alignments(waveform: torch.Tensor, transcript: List[str]):
    with torch.inference_mode():
        emission, _ = model(waveform.to(device))
        token_spans = aligner(emission[0], tokenizer(transcript))
    return emission, token_spans

我们还定义了用于绘制结果和预览音频段的实用函数。

# Compute average score weighted by the span length
def _score(spans):
    return sum(s.score * len(s) for s in spans) / sum(len(s) for s in spans)


def plot_alignments(waveform, token_spans, emission, transcript, sample_rate=bundle.sample_rate):
    ratio = waveform.size(1) / emission.size(1) / sample_rate

    fig, axes = plt.subplots(2, 1)
    axes[0].imshow(emission[0].detach().cpu().T, aspect="auto")
    axes[0].set_title("Emission")
    axes[0].set_xticks([])

    axes[1].specgram(waveform[0], Fs=sample_rate)
    for t_spans, chars in zip(token_spans, transcript):
        t0, t1 = t_spans[0].start, t_spans[-1].end
        axes[0].axvspan(t0 - 0.5, t1 - 0.5, facecolor="None", hatch="/", edgecolor="white")
        axes[1].axvspan(ratio * t0, ratio * t1, facecolor="None", hatch="/", edgecolor="white")
        axes[1].annotate(f"{_score(t_spans):.2f}", (ratio * t0, sample_rate * 0.51), annotation_clip=False)

        for span, char in zip(t_spans, chars):
            t0 = span.start * ratio
            axes[1].annotate(char, (t0, sample_rate * 0.55), annotation_clip=False)

    axes[1].set_xlabel("time [second]")
    fig.tight_layout()

def preview_word(waveform, spans, num_frames, transcript, sample_rate=bundle.sample_rate):
    ratio = waveform.size(1) / num_frames
    x0 = int(ratio * spans[0].start)
    x1 = int(ratio * spans[-1].end)
    print(f"{transcript} ({_score(spans):.2f}): {x0 / sample_rate:.3f} - {x1 / sample_rate:.3f} sec")
    segment = waveform[:, x0:x1]
    return IPython.display.Audio(segment.numpy(), rate=sample_rate)

规范化文本

传递给管道的文本必须事先进行规范化。规范化的具体过程取决于语言。

没有明确词边界的语言（如中文、日语和韩语）需要先进行分词。有专门的工具可以实现这一点，但假设我们已经有了分词后的文本。

规范化的第一步是罗马化。uroman 是一个支持多种语言的工具。

以下是使用uroman将输入文本文件罗马化并将输出写入另一个文本文件的 BASH 命令。

$ echo "des événements d'actualité qui se sont produits durant l'année 1882" > text.txt
$ uroman/bin/uroman.pl < text.txt > text_romanized.txt
$ cat text_romanized.txt

Cette page concerne des evenements d'actualite qui se sont produits durant l'annee 1882

下一步是删除非字母和标点符号。以下代码片段将罗马化后的文本进行规范化。

import re


def normalize_uroman(text):
    text = text.lower()
    text = text.replace("’", "'")
    text = re.sub("([^a-z' ])", " ", text)
    text = re.sub(' +', ' ', text)
    return text.strip()


with open("text_romanized.txt", "r") as f:
    for line in f:
        text_normalized = normalize_uroman(line)
        print(text_normalized)

在上述示例上运行脚本会产生以下结果。

cette page concerne des evenements d'actualite qui se sont produits durant l'annee

请注意，在此示例中，由于“1882”未被uroman罗马化，因此在规范化步骤中被删除。为了避免这种情况，需要将数字罗马化，但这被认为是一项非平凡的任务。

将文本与语音对齐

现在我们对多种语言执行强制对齐。

德语

text_raw = "aber seit ich bei ihnen das brot hole"
text_normalized = "aber seit ich bei ihnen das brot hole"

url = "https://download.pytorch.org/torchaudio/tutorial-assets/10349_8674_000087.flac"
waveform, sample_rate = torchaudio.load(
    url, frame_offset=int(0.5 * bundle.sample_rate), num_frames=int(2.5 * bundle.sample_rate)
)

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.ac.cn/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

assert sample_rate == bundle.sample_rate

transcript = text_normalized.split()
tokens = tokenizer(transcript)

emission, token_spans = compute_alignments(waveform, transcript)
num_frames = emission.size(1)

plot_alignments(waveform, token_spans, emission, transcript)

print("Raw Transcript: ", text_raw)
print("Normalized Transcript: ", text_normalized)
IPython.display.Audio(waveform, rate=sample_rate)
Emission
/pytorch/audio/src/torchaudio/pipelines/_wav2vec2/aligner.py:40: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  aligned_tokens, scores = F.forced_align(emission, targets, blank=blank)
Raw Transcript:  aber seit ich bei ihnen das brot hole
Normalized Transcript:  aber seit ich bei ihnen das brot hole



preview_word(waveform, token_spans[0], num_frames, transcript[0])

aber (0.96): 0.222 - 0.464 sec



preview_word(waveform, token_spans[1], num_frames, transcript[1])

seit (0.78): 0.565 - 0.766 sec



preview_word(waveform, token_spans[2], num_frames, transcript[2])

ich (0.91): 0.847 - 0.948 sec



preview_word(waveform, token_spans[3], num_frames, transcript[3])

bei (0.96): 1.028 - 1.190 sec



preview_word(waveform, token_spans[4], num_frames, transcript[4])

ihnen (0.65): 1.331 - 1.532 sec



preview_word(waveform, token_spans[5], num_frames, transcript[5])

das (0.54): 1.573 - 1.774 sec



preview_word(waveform, token_spans[6], num_frames, transcript[6])

brot (0.86): 1.855 - 2.117 sec



preview_word(waveform, token_spans[7], num_frames, transcript[7])

hole (0.71): 2.177 - 2.480 sec


中文

中文是字符型语言，其原始书面形式没有明确的词级分词（通过空格分隔）。为了获得词级对齐，您需要首先使用像“Stanford Tokenizer”这样的词分词器对文本进行词级分词。但是，如果您只需要字符级对齐，则不需要这样做。

text_raw = "关 服务 高端 产品 仍 处于 供不应求 的 局面"
text_normalized = "guan fuwu gaoduan chanpin reng chuyu gongbuyingqiu de jumian"

url = "https://download.pytorch.org/torchaudio/tutorial-assets/mvdr/clean_speech.wav"
waveform, sample_rate = torchaudio.load(url)
waveform = waveform[0:1]

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.ac.cn/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

assert sample_rate == bundle.sample_rate

transcript = text_normalized.split()
emission, token_spans = compute_alignments(waveform, transcript)
num_frames = emission.size(1)

plot_alignments(waveform, token_spans, emission, transcript)

print("Raw Transcript: ", text_raw)
print("Normalized Transcript: ", text_normalized)
IPython.display.Audio(waveform, rate=sample_rate)
Emission
/pytorch/audio/src/torchaudio/pipelines/_wav2vec2/aligner.py:40: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  aligned_tokens, scores = F.forced_align(emission, targets, blank=blank)
Raw Transcript:  关 服务 高端 产品 仍 处于 供不应求 的 局面
Normalized Transcript:  guan fuwu gaoduan chanpin reng chuyu gongbuyingqiu de jumian



preview_word(waveform, token_spans[0], num_frames, transcript[0])

guan (0.33): 0.020 - 0.141 sec



preview_word(waveform, token_spans[1], num_frames, transcript[1])

fuwu (0.31): 0.221 - 0.583 sec



preview_word(waveform, token_spans[2], num_frames, transcript[2])

gaoduan (0.74): 0.724 - 1.065 sec



preview_word(waveform, token_spans[3], num_frames, transcript[3])

chanpin (0.73): 1.126 - 1.528 sec



preview_word(waveform, token_spans[4], num_frames, transcript[4])

reng (0.86): 1.608 - 1.809 sec



preview_word(waveform, token_spans[5], num_frames, transcript[5])

chuyu (0.80): 1.849 - 2.151 sec



preview_word(waveform, token_spans[6], num_frames, transcript[6])

gongbuyingqiu (0.93): 2.251 - 2.894 sec



preview_word(waveform, token_spans[7], num_frames, transcript[7])

de (0.98): 2.935 - 3.015 sec



preview_word(waveform, token_spans[8], num_frames, transcript[8])

jumian (0.95): 3.075 - 3.477 sec


波兰语

text_raw = "wtedy ujrzałem na jego brzuchu okrągłą czarną ranę"
text_normalized = "wtedy ujrzalem na jego brzuchu okragla czarna rane"

url = "https://download.pytorch.org/torchaudio/tutorial-assets/5090_1447_000088.flac"
waveform, sample_rate = torchaudio.load(url, num_frames=int(4.5 * bundle.sample_rate))

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.ac.cn/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

assert sample_rate == bundle.sample_rate

transcript = text_normalized.split()
emission, token_spans = compute_alignments(waveform, transcript)
num_frames = emission.size(1)

plot_alignments(waveform, token_spans, emission, transcript)

print("Raw Transcript: ", text_raw)
print("Normalized Transcript: ", text_normalized)
IPython.display.Audio(waveform, rate=sample_rate)
Emission
/pytorch/audio/src/torchaudio/pipelines/_wav2vec2/aligner.py:40: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  aligned_tokens, scores = F.forced_align(emission, targets, blank=blank)
Raw Transcript:  wtedy ujrzałem na jego brzuchu okrągłą czarną ranę
Normalized Transcript:  wtedy ujrzalem na jego brzuchu okragla czarna rane



preview_word(waveform, token_spans[0], num_frames, transcript[0])

wtedy (1.00): 0.783 - 1.145 sec



preview_word(waveform, token_spans[1], num_frames, transcript[1])

ujrzalem (0.96): 1.286 - 1.788 sec



preview_word(waveform, token_spans[2], num_frames, transcript[2])

na (1.00): 1.868 - 1.949 sec



preview_word(waveform, token_spans[3], num_frames, transcript[3])

jego (1.00): 2.009 - 2.230 sec



preview_word(waveform, token_spans[4], num_frames, transcript[4])

brzuchu (0.97): 2.330 - 2.732 sec



preview_word(waveform, token_spans[5], num_frames, transcript[5])

okragla (1.00): 2.893 - 3.415 sec



preview_word(waveform, token_spans[6], num_frames, transcript[6])

czarna (0.90): 3.556 - 3.938 sec



preview_word(waveform, token_spans[7], num_frames, transcript[7])

rane (1.00): 4.098 - 4.399 sec


葡萄牙语

text_raw = "na imensa extensão onde se esconde o inconsciente imortal"
text_normalized = "na imensa extensao onde se esconde o inconsciente imortal"

url = "https://download.pytorch.org/torchaudio/tutorial-assets/6566_5323_000027.flac"
waveform, sample_rate = torchaudio.load(
    url, frame_offset=int(bundle.sample_rate), num_frames=int(4.6 * bundle.sample_rate)
)

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.ac.cn/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

assert sample_rate == bundle.sample_rate

transcript = text_normalized.split()
emission, token_spans = compute_alignments(waveform, transcript)
num_frames = emission.size(1)

plot_alignments(waveform, token_spans, emission, transcript)

print("Raw Transcript: ", text_raw)
print("Normalized Transcript: ", text_normalized)
IPython.display.Audio(waveform, rate=sample_rate)
Emission
/pytorch/audio/src/torchaudio/pipelines/_wav2vec2/aligner.py:40: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  aligned_tokens, scores = F.forced_align(emission, targets, blank=blank)
Raw Transcript:  na imensa extensão onde se esconde o inconsciente imortal
Normalized Transcript:  na imensa extensao onde se esconde o inconsciente imortal



preview_word(waveform, token_spans[0], num_frames, transcript[0])

na (1.00): 0.020 - 0.080 sec



preview_word(waveform, token_spans[1], num_frames, transcript[1])

imensa (0.90): 0.120 - 0.502 sec



preview_word(waveform, token_spans[2], num_frames, transcript[2])

extensao (0.92): 0.542 - 1.205 sec



preview_word(waveform, token_spans[3], num_frames, transcript[3])

onde (1.00): 1.446 - 1.667 sec



preview_word(waveform, token_spans[4], num_frames, transcript[4])

se (0.99): 1.748 - 1.828 sec



preview_word(waveform, token_spans[5], num_frames, transcript[5])

esconde (0.99): 1.888 - 2.591 sec



preview_word(waveform, token_spans[6], num_frames, transcript[6])

o (0.98): 2.852 - 2.872 sec



preview_word(waveform, token_spans[7], num_frames, transcript[7])

inconsciente (0.80): 2.933 - 3.897 sec



preview_word(waveform, token_spans[8], num_frames, transcript[8])

imortal (0.86): 3.937 - 4.560 sec


意大利语

text_raw = "elle giacean per terra tutte quante"
text_normalized = "elle giacean per terra tutte quante"

url = "https://download.pytorch.org/torchaudio/tutorial-assets/642_529_000025.flac"
waveform, sample_rate = torchaudio.load(url, num_frames=int(4 * bundle.sample_rate))

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.ac.cn/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

assert sample_rate == bundle.sample_rate

transcript = text_normalized.split()
emission, token_spans = compute_alignments(waveform, transcript)
num_frames = emission.size(1)

plot_alignments(waveform, token_spans, emission, transcript)

print("Raw Transcript: ", text_raw)
print("Normalized Transcript: ", text_normalized)
IPython.display.Audio(waveform, rate=sample_rate)
Emission
/pytorch/audio/src/torchaudio/pipelines/_wav2vec2/aligner.py:40: UserWarning: torchaudio.functional._alignment.forced_align has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  aligned_tokens, scores = F.forced_align(emission, targets, blank=blank)
Raw Transcript:  elle giacean per terra tutte quante
Normalized Transcript:  elle giacean per terra tutte quante



preview_word(waveform, token_spans[0], num_frames, transcript[0])

elle (1.00): 0.563 - 0.864 sec



preview_word(waveform, token_spans[1], num_frames, transcript[1])

giacean (0.99): 0.945 - 1.467 sec



preview_word(waveform, token_spans[2], num_frames, transcript[2])

per (1.00): 1.588 - 1.789 sec



preview_word(waveform, token_spans[3], num_frames, transcript[3])

terra (1.00): 1.950 - 2.392 sec



preview_word(waveform, token_spans[4], num_frames, transcript[4])

tutte (1.00): 2.533 - 2.975 sec



preview_word(waveform, token_spans[5], num_frames, transcript[5])

quante (1.00): 3.055 - 3.678 sec


结论

在本教程中，我们学习了如何使用 torchaudio 的强制对齐 API 和 Wav2Vec2 预训练多语言声学模型，将语音数据与五种语言的文本进行对齐。

致谢

感谢Vineel Pratap和Zhaoheng Ni开发并开源了强制对齐器 API。

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