注意

点击这里下载完整示例代码

使用CTC解码器进行ASR推理¶

作者: Caroline Chen

本教程演示了如何使用带有词典约束和 KenLM 语言模型支持的 CTC 贝叶斯搜索解码器进行语音识别推理。我们将在一个使用 CTC 损失训练的预训练 wav2vec 2.0 模型上展示这一过程。

概述¶

束搜索解码通过迭代扩展文本假设（束），使用下一个可能的字符，并在每个时间步仅保留得分最高的假设。可以将语言模型纳入评分计算中，添加词典约束可以限制假设的下一个可能标记，使得只能生成词典中的词语。

底层实现是从 Flashlight 的束搜索解码器移植而来的。解码器优化的数学公式可以在 Wav2Letter 论文中找到，更详细的算法可以在这篇博客中找到。

使用带有语言模型和词典约束的CTC Beam Search解码器运行ASR推理需要以下组件

声学模型：从音频波形预测语音特征的模型
Tokens: 语音模型可能预测的标记
词典：可能的单词与对应标记序列之间的映射
语言模型 (LM): 使用 KenLM 库训练的 n-gram 语言模型，或继承自 CTCDecoderLM 的自定义语言模型

声学模型与设置¶

首先我们导入必要的工具并获取我们正在使用的数据

import torch
import torchaudio

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

2.6.0.dev20241104
2.5.0.dev20241105

import time
from typing import List

import IPython
import matplotlib.pyplot as plt
from torchaudio.models.decoder import ctc_decoder
from torchaudio.utils import download_asset

我们使用在 Wav2Vec 2.0 基础模型上微调的10分钟 LibriSpeech 数据集，可以通过 torchaudio.pipelines.WAV2VEC2_ASR_BASE_10M 加载。有关在 torchaudio 中运行 Wav2Vec 2.0 语音识别流水线的更多详细信息，请参阅本教程。

bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_10M
acoustic_model = bundle.get_model()

Downloading: "https://download.pytorch.org/torchaudio/models/wav2vec2_fairseq_base_ls960_asr_ll10m.pth" to /root/.cache/torch/hub/checkpoints/wav2vec2_fairseq_base_ls960_asr_ll10m.pth

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我们将从 LibriSpeech test-other 数据集中加载一个样本。

speech_file = download_asset("tutorial-assets/ctc-decoding/1688-142285-0007.wav")

IPython.display.Audio(speech_file)

该音频文件对应的文本是

i really was very much afraid of showing him how much shocked i was at some parts of what he said

waveform, sample_rate = torchaudio.load(speech_file)

if sample_rate != bundle.sample_rate:
    waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)

文件和数据用于解码器¶

接下来，我们加载词元、词典和语言模型数据，这些数据用于解码器根据声学模型的输出预测词语。LibriSpeech 数据集的预训练文件可以通过 torchaudio 下载，或者用户也可以提供自己的文件。

Tokens¶

这些符号是声学模型可以预测的可能符号，包括空白符号和静音符号。它们既可以作为文件传入，每行对应相同索引的符号，也可以作为符号列表传入，每个符号映射到唯一的索引。

# tokens.txt
_
|
e
t
...

tokens = [label.lower() for label in bundle.get_labels()]
print(tokens)

['-', '|', 'e', 't', 'a', 'o', 'n', 'i', 'h', 's', 'r', 'd', 'l', 'u', 'm', 'w', 'c', 'f', 'g', 'y', 'p', 'b', 'v', 'k', "'", 'x', 'j', 'q', 'z']

术语表¶

词典是将单词映射到其对应标记序列的映射，用于将解码器的搜索空间限制为仅来自词典中的单词。词典文件的预期格式是每行一个单词，单词后跟由空格分隔的标记。

# lexcion.txt
a a |
able a b l e |
about a b o u t |
...
...

语言模型¶

在解码过程中，可以使用语言模型来改进结果，方法是将代表序列可能性的语言模型得分纳入到束搜索计算中。下面，我们将概述支持用于解码的不同形式的语言模型。

无语言模型¶

要创建一个不带语言模型的解码器实例，请在初始化解码器时将 lm=None 设置为参数。

KenLM¶

这是一个使用 KenLM库训练的n-gram语言模型。可以使用 .arpa 或者二进制化的 .bin 语言模型，但推荐使用二进制格式以加快加载速度。

本教程中使用的语言模型是一个使用 LibriSpeech训练的4-gram KenLM。

自定义语言模型¶

用户可以使用 Python 定义自己的自定义语言模型，无论是统计语言模型还是神经网络语言模型，只需使用 CTCDecoderLM 和 CTCDecoderLMState。

例如，以下代码创建了一个围绕 PyTorch torch.nn.Module 语言模型的基本包装器。

from torchaudio.models.decoder import CTCDecoderLM, CTCDecoderLMState


class CustomLM(CTCDecoderLM):
    """Create a Python wrapper around `language_model` to feed to the decoder."""

    def __init__(self, language_model: torch.nn.Module):
        CTCDecoderLM.__init__(self)
        self.language_model = language_model
        self.sil = -1  # index for silent token in the language model
        self.states = {}

        language_model.eval()

    def start(self, start_with_nothing: bool = False):
        state = CTCDecoderLMState()
        with torch.no_grad():
            score = self.language_model(self.sil)

        self.states[state] = score
        return state

    def score(self, state: CTCDecoderLMState, token_index: int):
        outstate = state.child(token_index)
        if outstate not in self.states:
            score = self.language_model(token_index)
            self.states[outstate] = score
        score = self.states[outstate]

        return outstate, score

    def finish(self, state: CTCDecoderLMState):
        return self.score(state, self.sil)

下载预训练文件¶

LibriSpeech 数据集的预训练文件可以使用 download_pretrained_files()下载。

注意：由于语言模型可能较大，此单元格运行可能需要几分钟。

from torchaudio.models.decoder import download_pretrained_files

files = download_pretrained_files("librispeech-4-gram")

print(files)

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PretrainedFiles(lexicon='/root/.cache/torch/hub/torchaudio/decoder-assets/librispeech-4-gram/lexicon.txt', tokens='/root/.cache/torch/hub/torchaudio/decoder-assets/librispeech-4-gram/tokens.txt', lm='/root/.cache/torch/hub/torchaudio/decoder-assets/librispeech-4-gram/lm.bin')

构建解码器¶

在本教程中，我们构建了一个束搜索解码器和一个贪婪解码器以进行比较。

束搜索解码器¶

解码器可以使用工厂函数 ctc_decoder()构建。除了前面提到的组件外，它还接受各种束搜索解码参数和令牌/单词参数。

通过向 None 参数传入 lm，此解码器也可以在不使用语言模型的情况下运行。

LM_WEIGHT = 3.23
WORD_SCORE = -0.26

beam_search_decoder = ctc_decoder(
    lexicon=files.lexicon,
    tokens=files.tokens,
    lm=files.lm,
    nbest=3,
    beam_size=1500,
    lm_weight=LM_WEIGHT,
    word_score=WORD_SCORE,
)

贪婪解码器¶

class GreedyCTCDecoder(torch.nn.Module):
    def __init__(self, labels, blank=0):
        super().__init__()
        self.labels = labels
        self.blank = blank

    def forward(self, emission: torch.Tensor) -> List[str]:
        """Given a sequence emission over labels, get the best path
        Args:
          emission (Tensor): Logit tensors. Shape `[num_seq, num_label]`.

        Returns:
          List[str]: The resulting transcript
        """
        indices = torch.argmax(emission, dim=-1)  # [num_seq,]
        indices = torch.unique_consecutive(indices, dim=-1)
        indices = [i for i in indices if i != self.blank]
        joined = "".join([self.labels[i] for i in indices])
        return joined.replace("|", " ").strip().split()


greedy_decoder = GreedyCTCDecoder(tokens)

运行推理¶

现在我们已经拥有了数据、声学模型和解码器，可以执行推理。束搜索解码器的输出类型为 CTCHypothesis，包含预测的 token ID、对应的单词（如果提供了词典）、假设分数以及与 token ID 对应的时间步。回顾一下，与波形对应的转录文本是

i really was very much afraid of showing him how much shocked i was at some parts of what he said

actual_transcript = "i really was very much afraid of showing him how much shocked i was at some parts of what he said"
actual_transcript = actual_transcript.split()

emission, _ = acoustic_model(waveform)

贪婪解码器给出以下结果。

greedy_result = greedy_decoder(emission[0])
greedy_transcript = " ".join(greedy_result)
greedy_wer = torchaudio.functional.edit_distance(actual_transcript, greedy_result) / len(actual_transcript)

print(f"Transcript: {greedy_transcript}")
print(f"WER: {greedy_wer}")

Transcript: i reily was very much affrayd of showing him howmuch shoktd i wause at some parte of what he seid
WER: 0.38095238095238093

使用束搜索解码器：

beam_search_result = beam_search_decoder(emission)
beam_search_transcript = " ".join(beam_search_result[0][0].words).strip()
beam_search_wer = torchaudio.functional.edit_distance(actual_transcript, beam_search_result[0][0].words) / len(
    actual_transcript
)

print(f"Transcript: {beam_search_transcript}")
print(f"WER: {beam_search_wer}")

Transcript: i really was very much afraid of showing him how much shocked i was at some part of what he said
WER: 0.047619047619047616

注意

如果未向解码器提供词典，输出假设的 words 字段将为空。要检索无词典解码的转录文本，您可以执行以下操作以获取令牌索引，将它们转换为原始令牌，然后将它们连接在一起。

tokens_str = "".join(beam_search_decoder.idxs_to_tokens(beam_search_result[0][0].tokens))
transcript = " ".join(tokens_str.split("|"))

我们发现，使用词典约束束搜索解码器生成的转录结果更为准确，由真实单词组成；而贪婪解码器则可能预测出拼写错误的单词，如"affrayd"和"shoktd"。

增量解码¶

如果输入语音较长，可以以增量方式解码发射概率。

您需要首先使用 decode_begin()初始化解码器的内部状态。

beam_search_decoder.decode_begin()

然后，您可以将发射传递给 decode_begin()。这里我们使用相同的发射，但每次向解码器传递一帧。

for t in range(emission.size(1)):
    beam_search_decoder.decode_step(emission[0, t:t + 1, :])

最后，完成解码器的内部状态，并获取结果。

beam_search_decoder.decode_end()
beam_search_result_inc = beam_search_decoder.get_final_hypothesis()

增量解码的结果与批量解码完全相同。

beam_search_transcript_inc = " ".join(beam_search_result_inc[0].words).strip()
beam_search_wer_inc = torchaudio.functional.edit_distance(
    actual_transcript, beam_search_result_inc[0].words) / len(actual_transcript)

print(f"Transcript: {beam_search_transcript_inc}")
print(f"WER: {beam_search_wer_inc}")

assert beam_search_result[0][0].words == beam_search_result_inc[0].words
assert beam_search_result[0][0].score == beam_search_result_inc[0].score
torch.testing.assert_close(beam_search_result[0][0].timesteps, beam_search_result_inc[0].timesteps)

Transcript: i really was very much afraid of showing him how much shocked i was at some part of what he said
WER: 0.047619047619047616

时间步对齐¶

请注意，生成的假设的组成部分之一是与时令 ID 对应的时间步。

timesteps = beam_search_result[0][0].timesteps
predicted_tokens = beam_search_decoder.idxs_to_tokens(beam_search_result[0][0].tokens)

print(predicted_tokens, len(predicted_tokens))
print(timesteps, timesteps.shape[0])

['|', 'i', '|', 'r', 'e', 'a', 'l', 'l', 'y', '|', 'w', 'a', 's', '|', 'v', 'e', 'r', 'y', '|', 'm', 'u', 'c', 'h', '|', 'a', 'f', 'r', 'a', 'i', 'd', '|', 'o', 'f', '|', 's', 'h', 'o', 'w', 'i', 'n', 'g', '|', 'h', 'i', 'm', '|', 'h', 'o', 'w', '|', 'm', 'u', 'c', 'h', '|', 's', 'h', 'o', 'c', 'k', 'e', 'd', '|', 'i', '|', 'w', 'a', 's', '|', 'a', 't', '|', 's', 'o', 'm', 'e', '|', 'p', 'a', 'r', 't', '|', 'o', 'f', '|', 'w', 'h', 'a', 't', '|', 'h', 'e', '|', 's', 'a', 'i', 'd', '|', '|'] 99
tensor([  0,  31,  33,  36,  39,  41,  42,  44,  46,  48,  49,  52,  54,  58,
         64,  66,  69,  73,  74,  76,  80,  82,  84,  86,  88,  94,  97, 107,
        111, 112, 116, 134, 136, 138, 140, 142, 146, 148, 151, 153, 155, 157,
        159, 161, 162, 166, 170, 176, 177, 178, 179, 182, 184, 186, 187, 191,
        193, 198, 201, 202, 203, 205, 207, 212, 213, 216, 222, 224, 230, 250,
        251, 254, 256, 261, 262, 264, 267, 270, 276, 277, 281, 284, 288, 289,
        292, 295, 297, 299, 300, 303, 305, 307, 310, 311, 324, 325, 329, 331,
        353], dtype=torch.int32) 99

在下图中，我们可视化了相对于原始波形的 token 时间步对齐情况。

def plot_alignments(waveform, emission, tokens, timesteps, sample_rate):

    t = torch.arange(waveform.size(0)) / sample_rate
    ratio = waveform.size(0) / emission.size(1) / sample_rate

    chars = []
    words = []
    word_start = None
    for token, timestep in zip(tokens, timesteps * ratio):
        if token == "|":
            if word_start is not None:
                words.append((word_start, timestep))
            word_start = None
        else:
            chars.append((token, timestep))
            if word_start is None:
                word_start = timestep

    fig, axes = plt.subplots(3, 1)

    def _plot(ax, xlim):
        ax.plot(t, waveform)
        for token, timestep in chars:
            ax.annotate(token.upper(), (timestep, 0.5))
        for word_start, word_end in words:
            ax.axvspan(word_start, word_end, alpha=0.1, color="red")
        ax.set_ylim(-0.6, 0.7)
        ax.set_yticks([0])
        ax.grid(True, axis="y")
        ax.set_xlim(xlim)

    _plot(axes[0], (0.3, 2.5))
    _plot(axes[1], (2.5, 4.7))
    _plot(axes[2], (4.7, 6.9))
    axes[2].set_xlabel("time (sec)")
    fig.tight_layout()


plot_alignments(waveform[0], emission, predicted_tokens, timesteps, bundle.sample_rate)

Beam Search Decoder Parameters¶

在本节中，我们将更深入地探讨一些不同的参数和权衡。有关可自定义参数的完整列表，请参阅 documentation。

辅助函数¶

def print_decoded(decoder, emission, param, param_value):
    start_time = time.monotonic()
    result = decoder(emission)
    decode_time = time.monotonic() - start_time

    transcript = " ".join(result[0][0].words).lower().strip()
    score = result[0][0].score
    print(f"{param} {param_value:<3}: {transcript} (score: {score:.2f}; {decode_time:.4f} secs)")

nbest¶

此参数指示要返回的最佳假设数量，这是贪婪解码器无法实现的属性。例如，通过在之前构建束搜索解码器时将 nbest=3 设置为该值，我们现在可以访问得分最高的前 3 个假设。

for i in range(3):
    transcript = " ".join(beam_search_result[0][i].words).strip()
    score = beam_search_result[0][i].score
    print(f"{transcript} (score: {score})")

i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3699.824109642502)
i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: 3697.858373688456)
i reply was very much afraid of showing him how much shocked i was at some part of what he said (score: 3695.0157600045172)

束宽¶

beam_size 参数决定了在每个解码步骤后保留的最佳假设的最大数量。使用更大的束宽（beam size）可以探索更大范围的潜在假设，从而可能生成得分更高的假设，但这在计算上更为昂贵，并且在达到某个临界点后不会带来额外的收益。

在下方的示例中，我们看到随着束宽从 1 增加到 5 再到 50，解码质量有所提升，但请注意，使用束宽 500 产生的输出与束宽 50 相同，却增加了计算时间。

beam_sizes = [1, 5, 50, 500]

for beam_size in beam_sizes:
    beam_search_decoder = ctc_decoder(
        lexicon=files.lexicon,
        tokens=files.tokens,
        lm=files.lm,
        beam_size=beam_size,
        lm_weight=LM_WEIGHT,
        word_score=WORD_SCORE,
    )

    print_decoded(beam_search_decoder, emission, "beam size", beam_size)

beam size 1  : i you ery much afra of shongut shot i was at some arte what he sad (score: 3144.93; 0.0470 secs)
beam size 5  : i rely was very much afraid of showing him how much shot i was at some parts of what he said (score: 3688.02; 0.0523 secs)
beam size 50 : i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3699.82; 0.1654 secs)
beam size 500: i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3699.82; 0.5398 secs)

束大小 token¶

beam_size_token 参数对应于在解码步骤中为扩展每个假设所考虑的标记（token）数量。探索更多的下一个可能标记会增加潜在假设的范围，但代价是计算量增加。

num_tokens = len(tokens)
beam_size_tokens = [1, 5, 10, num_tokens]

for beam_size_token in beam_size_tokens:
    beam_search_decoder = ctc_decoder(
        lexicon=files.lexicon,
        tokens=files.tokens,
        lm=files.lm,
        beam_size_token=beam_size_token,
        lm_weight=LM_WEIGHT,
        word_score=WORD_SCORE,
    )

    print_decoded(beam_search_decoder, emission, "beam size token", beam_size_token)

beam size token 1  : i rely was very much affray of showing him hoch shot i was at some part of what he sed (score: 3584.80; 0.1599 secs)
beam size token 5  : i rely was very much afraid of showing him how much shocked i was at some part of what he said (score: 3694.83; 0.1800 secs)
beam size token 10 : i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3696.25; 0.2000 secs)
beam size token 29 : i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3699.82; 0.2315 secs)

束搜索阈值¶

beam_threshold 参数用于在每个解码步骤中修剪存储的假设集，移除得分与最高分假设相差超过 beam_threshold 的假设。需要在选择较小的阈值以修剪更多假设并减少搜索空间，以及选择足够大的阈值以避免修剪合理假设之间取得平衡。

beam_thresholds = [1, 5, 10, 25]

for beam_threshold in beam_thresholds:
    beam_search_decoder = ctc_decoder(
        lexicon=files.lexicon,
        tokens=files.tokens,
        lm=files.lm,
        beam_threshold=beam_threshold,
        lm_weight=LM_WEIGHT,
        word_score=WORD_SCORE,
    )

    print_decoded(beam_search_decoder, emission, "beam threshold", beam_threshold)

beam threshold 1  : i ila ery much afraid of shongut shot i was at some parts of what he said (score: 3316.20; 0.0280 secs)
beam threshold 5  : i rely was very much afraid of showing him how much shot i was at some parts of what he said (score: 3682.23; 0.0511 secs)
beam threshold 10 : i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3699.82; 0.2134 secs)
beam threshold 25 : i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3699.82; 0.2340 secs)

语言模型权重¶

lm_weight 参数是分配给语言模型分数的权重，该分数将与声学模型分数累加以确定总体分数。较大的权重会鼓励模型基于语言模型预测下一个词，而较小的权重则会给声学模型分数赋予更大的权重。

lm_weights = [0, LM_WEIGHT, 15]

for lm_weight in lm_weights:
    beam_search_decoder = ctc_decoder(
        lexicon=files.lexicon,
        tokens=files.tokens,
        lm=files.lm,
        lm_weight=lm_weight,
        word_score=WORD_SCORE,
    )

    print_decoded(beam_search_decoder, emission, "lm weight", lm_weight)

lm weight 0  : i rely was very much affraid of showing him ho much shoke i was at some parte of what he seid (score: 3834.05; 0.2566 secs)
lm weight 3.23: i really was very much afraid of showing him how much shocked i was at some part of what he said (score: 3699.82; 0.2634 secs)
lm weight 15 : was there in his was at some of what he said (score: 2918.99; 0.2429 secs)

其他参数¶

可优化的其他参数包括以下内容

word_score: 单词结束时添加的分数
unk_score: 要添加的未知词出现分数
sil_score: 要添加的静音外观分数
log_add: 是否对词典 Trie 涂抹使用 log add

脚本总运行时间： (2分钟9.689秒)

通过 Sphinx-Gallery 生成的画廊