注意

点击这里下载完整的示例代码

多语言数据强制对齐¶

本教程展示了如何将转录对齐到非英语语言的语音。

对齐非英语（规范化）转录的过程与对齐英语（规范化）转录的过程相同，英语的过程在 CTC 强制对齐教程中有详细介绍。在本教程中，我们使用 TorchAudio 的高级 API，torchaudio.pipelines.Wav2Vec2FABundle，它将预训练的模型、分词器和对齐器打包在一起，以便使用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)

2.5.0
2.5.0
cuda

from typing import List

import IPython
import matplotlib.pyplot as plt

创建流水线¶

首先，我们实例化模型和预/后处理流水线。

下图说明了对齐过程。

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

波形传递到声学模型，声学模型生成标记概率分布序列。转录传递到分词器，分词器将转录转换为标记序列。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

此过程需要输入转录已规范化。规范化过程涉及非英语语言的罗马化，是语言相关的，因此在本教程中没有介绍，但我们将简要介绍它。

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 是其中一个实例。

以下代码实例化了预训练的声学模型、分词器（使用与模型相同的标记集）和对齐器。

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

默认情况下，由 MMS_FA 的 get_model() 方法实例化的模型包含 <star> 标记的特征维度。您可以通过传递 with_star=False 来禁用它。

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

分词器只需将规范化的字符映射到整数。您可以检查映射，如下所示；

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}

对齐器在内部使用 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 教程中有介绍，请参考该教程。

我们定义了一个实用函数，该函数使用上述模型、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片段的实用函数。

# 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)

规范化转录¶

传递到流水线的转录必须事先规范化。规范化的确切过程取决于语言。

没有明确词界限的语言（如中文、日语和韩语）需要先进行分段。为此有专门的工具，但我们假设我们已经有了分段的转录。

规范化的第一步是罗马化。 uroman 是一个支持多种语言的工具。

以下是一些使用 uroman 对输入文本文件进行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。以下代码段规范化了罗马化的转录。

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，在这个示例中，由于 uroman 没有对“1882”进行罗马化，因此在规范化步骤中将其删除了。为了避免这种情况，需要对数字进行罗马化，但众所周知这是一项非平凡的任务。

将转录对齐到语音¶

现在我们对多种语言进行强制对齐。

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)
)

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)

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¶

中文是表意文字语言，其原始书面形式没有明确的词级分词（以空格隔开）。为了获得词级对齐，您需要首先使用词级分词器（如 “斯坦福分词器”）在词级对转录进行分词。但是，如果您只需要字符级对齐，则不需要执行此操作。

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]

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)

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))

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)

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)
)

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)

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))

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)

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，我们介绍了如何使用 torchaudio 的强制对齐 API 和 Wav2Vec2 预训练的多语言声学模型将五种语言的语音数据对齐到转录。

致谢¶

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

脚本总运行时间：（0 分钟 4.835 秒）

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