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Torchaudio-Squim: TorchAudio 中的非侵入式语音评估¶

作者: Anurag Kumar, Zhaoheng Ni

1. 概述¶

本教程演示了如何使用 Torchaudio-Squim 评估语音质量和可懂度的客观和主观指标。

TorchAudio-Squim 支持在 Torchaudio 中进行语音评估。它提供了接口和预训练模型来估算各种语音质量和可懂度指标。目前，Torchaudio-Squim [1] 支持对 3 种广泛使用的客观指标进行无参考估计：

宽带语音质量感知估计 (PESQ) [2]
短时客观可懂度 (STOI) [3]
尺度不变信噪比 (SI-SDR) [4]

它还支持使用非匹配参考 [1, 5] 来估计给定音频波形的客观主观平均意见得分 (MOS)。

参考文献

[1] Kumar, Anurag, et al. “TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio.” ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023.

[2] I. Rec, “P.862.2: Wideband extension to recommendation P.862 for the assessment of wideband telephone networks and speech codecs,” International Telecommunication Union, CH–Geneva, 2005.

[3] Taal, C. H., Hendriks, R. C., Heusdens, R., & Jensen, J. (2010, March). A short-time objective intelligibility measure for time-frequency weighted noisy speech. In 2010 IEEE international conference on acoustics, speech and signal processing (pp. 4214-4217). IEEE.

[4] Le Roux, Jonathan, et al. “SDR–half-baked or well done?.” ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.

[5] Manocha, Pranay, and Anurag Kumar. “Speech quality assessment through MOS using non-matching references.” Interspeech, 2022.

import torch
import torchaudio

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

2.10.0.dev20251013+cu126
2.8.0a0+1d65bbe

2. 准备¶

首先导入模块并定义辅助函数。

我们将需要 torch 和 torchaudio 来使用 Torchaudio-squim，以及 Matplotlib 来绘制数据。

try:
    from torchaudio.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE
except ImportError:
    try:
        import google.colab  # noqa: F401

        print(
            """
            To enable running this notebook in Google Colab, install nightly
            torch and torchaudio builds by adding the following code block to the top
            of the notebook before running it:
            !pip3 uninstall -y torch torchvision torchaudio
            !pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
            """
        )
    except Exception:
        pass
    raise


import matplotlib.pyplot as plt

import torchaudio.functional as F
from IPython.display import Audio
from torchaudio.utils import _download_asset


def si_snr(estimate, reference, epsilon=1e-8):
    estimate = estimate - estimate.mean()
    reference = reference - reference.mean()
    reference_pow = reference.pow(2).mean(axis=1, keepdim=True)
    mix_pow = (estimate * reference).mean(axis=1, keepdim=True)
    scale = mix_pow / (reference_pow + epsilon)

    reference = scale * reference
    error = estimate - reference

    reference_pow = reference.pow(2)
    error_pow = error.pow(2)

    reference_pow = reference_pow.mean(axis=1)
    error_pow = error_pow.mean(axis=1)

    si_snr = 10 * torch.log10(reference_pow) - 10 * torch.log10(error_pow)
    return si_snr.item()


def plot(waveform, title, sample_rate=16000):
    wav_numpy = waveform.numpy()

    sample_size = waveform.shape[1]
    time_axis = torch.arange(0, sample_size) / sample_rate

    figure, axes = plt.subplots(2, 1)
    axes[0].plot(time_axis, wav_numpy[0], linewidth=1)
    axes[0].grid(True)
    axes[1].specgram(wav_numpy[0], Fs=sample_rate)
    figure.suptitle(title)

3. 加载语音和噪声样本¶

SAMPLE_SPEECH = _download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")
SAMPLE_NOISE = _download_asset("tutorial-assets/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo.wav")

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WAVEFORM_SPEECH, SAMPLE_RATE_SPEECH = torchaudio.load(SAMPLE_SPEECH)
WAVEFORM_NOISE, SAMPLE_RATE_NOISE = torchaudio.load(SAMPLE_NOISE)
WAVEFORM_NOISE = WAVEFORM_NOISE[0:1, :]

目前，Torchaudio-Squim 模型仅支持 16000 Hz 的采样率。如有必要，请重采样波形。

if SAMPLE_RATE_SPEECH != 16000:
    WAVEFORM_SPEECH = F.resample(WAVEFORM_SPEECH, SAMPLE_RATE_SPEECH, 16000)

if SAMPLE_RATE_NOISE != 16000:
    WAVEFORM_NOISE = F.resample(WAVEFORM_NOISE, SAMPLE_RATE_NOISE, 16000)

修剪波形，使它们具有相同数量的帧。

if WAVEFORM_SPEECH.shape[1] < WAVEFORM_NOISE.shape[1]:
    WAVEFORM_NOISE = WAVEFORM_NOISE[:, : WAVEFORM_SPEECH.shape[1]]
else:
    WAVEFORM_SPEECH = WAVEFORM_SPEECH[:, : WAVEFORM_NOISE.shape[1]]

播放语音样本

Audio(WAVEFORM_SPEECH.numpy()[0], rate=16000)

播放噪声样本

Audio(WAVEFORM_NOISE.numpy()[0], rate=16000)

4. 创建失真（噪声）语音样本¶

snr_dbs = torch.tensor([20, -5])
WAVEFORM_DISTORTED = F.add_noise(WAVEFORM_SPEECH, WAVEFORM_NOISE, snr_dbs)

播放具有 20dB SNR 的失真语音

Audio(WAVEFORM_DISTORTED.numpy()[0], rate=16000)

播放具有 -5dB SNR 的失真语音

Audio(WAVEFORM_DISTORTED.numpy()[1], rate=16000)

5. 可视化波形¶

可视化语音样本

plot(WAVEFORM_SPEECH, "Clean Speech")

可视化噪声样本

plot(WAVEFORM_NOISE, "Noise")

可视化具有 20dB SNR 的失真语音

plot(WAVEFORM_DISTORTED[0:1], f"Distorted Speech with {snr_dbs[0]}dB SNR")

可视化具有 -5dB SNR 的失真语音

plot(WAVEFORM_DISTORTED[1:2], f"Distorted Speech with {snr_dbs[1]}dB SNR")

6. 预测客观指标¶

获取预训练的 SquimObjective 模型。

objective_model = SQUIM_OBJECTIVE.get_model()

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比较具有 20dB SNR 的失真语音的模型输出与真实值

stoi_hyp, pesq_hyp, si_sdr_hyp = objective_model(WAVEFORM_DISTORTED[0:1, :])
print(f"Estimated metrics for distorted speech at {snr_dbs[0]}dB are\n")
print(f"STOI: {stoi_hyp[0]}")
print(f"PESQ: {pesq_hyp[0]}")
print(f"SI-SDR: {si_sdr_hyp[0]}\n")

# To calculate the STOI and PESQ reference metrics,
# we would need to install the pystoi and pesq packages and execute the following:
# ```python
# pesq_ref = pesq(16000, WAVEFORM_SPEECH[0].numpy(), WAVEFORM_DISTORTED[0].numpy(), mode="wb")
# stoi_ref = stoi(WAVEFORM_SPEECH[0].numpy(), WAVEFORM_DISTORTED[0].numpy(), 16000, extended=False)
# ```
# These values are precomputed and hard-coded below.
print(f"Reference metrics for distorted speech at {snr_dbs[0]}dB are\n")
print("STOI: 0.9670831113894452")
print("PESQ: 2.7961528301239014")
si_sdr_ref = si_snr(WAVEFORM_DISTORTED[0:1], WAVEFORM_SPEECH)
print(f"SI-SDR: {si_sdr_ref}")

Estimated metrics for distorted speech at 20dB are

STOI: 0.9610356092453003
PESQ: 2.7801527976989746
SI-SDR: 20.692630767822266

Reference metrics for distorted speech at 20dB are

STOI: 0.9670831113894452
PESQ: 2.7961528301239014
SI-SDR: 19.998966217041016

比较具有 -5dB SNR 的失真语音的模型输出与真实值

stoi_hyp, pesq_hyp, si_sdr_hyp = objective_model(WAVEFORM_DISTORTED[1:2, :])
print(f"Estimated metrics for distorted speech at {snr_dbs[1]}dB are\n")
print(f"STOI: {stoi_hyp[0]}")
print(f"PESQ: {pesq_hyp[0]}")
print(f"SI-SDR: {si_sdr_hyp[0]}\n")

si_sdr_ref = si_snr(WAVEFORM_DISTORTED[1:2], WAVEFORM_SPEECH)
# STOI and PESQ metrics are precomputed and hardcoded below.
print(f"Reference metrics for distorted speech at {snr_dbs[1]}dB are\n")
print("STOI: 0.5743247866630554")
print("PESQ: 1.1112866401672363")
print(f"SI-SDR: {si_sdr_ref}")

Estimated metrics for distorted speech at -5dB are

STOI: 0.5743248462677002
PESQ: 1.1112866401672363
SI-SDR: -6.248741149902344

Reference metrics for distorted speech at -5dB are

STOI: 0.5743247866630554
PESQ: 1.1112866401672363
SI-SDR: -5.016279220581055

7. 预测平均意见得分 (主观) 指标¶

获取预训练的 SquimSubjective 模型。

subjective_model = SQUIM_SUBJECTIVE.get_model()

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加载非匹配参考 (NMR)

NMR_SPEECH = _download_asset("tutorial-assets/ctc-decoding/1688-142285-0007.wav")

WAVEFORM_NMR, SAMPLE_RATE_NMR = torchaudio.load(NMR_SPEECH)
if SAMPLE_RATE_NMR != 16000:
    WAVEFORM_NMR = F.resample(WAVEFORM_NMR, SAMPLE_RATE_NMR, 16000)

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计算具有 20dB SNR 的失真语音的 MOS 指标

mos = subjective_model(WAVEFORM_DISTORTED[0:1, :], WAVEFORM_NMR)
print(f"Estimated MOS for distorted speech at {snr_dbs[0]}dB is MOS: {mos[0]}")

Estimated MOS for distorted speech at 20dB is MOS: 4.309267997741699

计算具有 -5dB SNR 的失真语音的 MOS 指标

mos = subjective_model(WAVEFORM_DISTORTED[1:2, :], WAVEFORM_NMR)
print(f"Estimated MOS for distorted speech at {snr_dbs[1]}dB is MOS: {mos[0]}")

Estimated MOS for distorted speech at -5dB is MOS: 3.2918035984039307

8. 与真实值和基线进行比较¶

可视化 SquimObjective 和 SquimSubjective 模型估算的指标，可以帮助用户更好地理解模型在实际场景中的应用。下图显示了三种不同系统的散点图：MOSA-Net [1]、AMSA [2] 和 SquimObjective 模型，其中 y 轴表示估计的 STOI、PESQ 和 Si-SDR 分数，x 轴表示相应的真实值。

https://download.pytorch.org/torchaudio/tutorial-assets/objective_plot.png

[1] Zezario, Ryandhimas E., Szu-Wei Fu, Fei Chen, Chiou-Shann Fuh, Hsin-Min Wang, and Yu Tsao. “Deep learning-based non-intrusive multi-objective speech assessment model with cross-domain features.” IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (2022): 54-70.

[2] Dong, Xuan, and Donald S. Williamson. “An attention enhanced multi-task model for objective speech assessment in real-world environments.” In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 911-915. IEEE, 2020.

下图显示了 SquimSubjective 模型的散点图，其中 y 轴表示估计的 MOS 指标得分，x 轴表示相应的真实值。

https://download.pytorch.org/torchaudio/tutorial-assets/subjective_plot.png

脚本总运行时间: ( 0 分钟 7.500 秒)

由 Sphinx-Gallery 生成的画廊