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目录

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音频数据增强

作者: Moto Hira

torchaudio 提供多种增强音频数据的方法。

在本教程中，我们将探讨如何应用效果、滤波器、 RIR（房间脉冲响应）和编解码器。

最后，我们从干净的语音中合成带有噪声的语音。

import torch
import torchaudio
import torchaudio.functional as F

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

import matplotlib.pyplot as plt

2.2.0
2.2.0

准备

首先，我们导入本教程中使用的模块并下载音频资源。

from IPython.display import Audio

from torchaudio.utils import download_asset

SAMPLE_WAV = download_asset("tutorial-assets/steam-train-whistle-daniel_simon.wav")
SAMPLE_RIR = download_asset("tutorial-assets/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo-8000hz.wav")
SAMPLE_SPEECH = download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042-8000hz.wav")
SAMPLE_NOISE = download_asset("tutorial-assets/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo-8000hz.wav")

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应用效果和过滤

torchaudio.io.AudioEffector 允许直接将 过滤器和编解码器应用于 Tensor 对象，方式类似于 ffmpeg 命令

AudioEffector Usages <./effector_tutorial.html> 说明了如何使用 此类，因此详细内容请参阅教程。

# Load the data
waveform1, sample_rate = torchaudio.load(SAMPLE_WAV, channels_first=False)

# Define effects
effect = ",".join(
    [
        "lowpass=frequency=300:poles=1",  # apply single-pole lowpass filter
        "atempo=0.8",  # reduce the speed
        "aecho=in_gain=0.8:out_gain=0.9:delays=200:decays=0.3|delays=400:decays=0.3"
        # Applying echo gives some dramatic feeling
    ],
)


# Apply effects
def apply_effect(waveform, sample_rate, effect):
    effector = torchaudio.io.AudioEffector(effect=effect)
    return effector.apply(waveform, sample_rate)


waveform2 = apply_effect(waveform1, sample_rate, effect)

print(waveform1.shape, sample_rate)
print(waveform2.shape, sample_rate)

torch.Size([109368, 2]) 44100
torch.Size([144642, 2]) 44100

请注意，应用效果后，帧数和通道数与原始数据不同。让我们来听一下音频。

def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None):
    waveform = waveform.numpy()

    num_channels, num_frames = waveform.shape
    time_axis = torch.arange(0, num_frames) / sample_rate

    figure, axes = plt.subplots(num_channels, 1)
    if num_channels == 1:
        axes = [axes]
    for c in range(num_channels):
        axes[c].plot(time_axis, waveform[c], linewidth=1)
        axes[c].grid(True)
        if num_channels > 1:
            axes[c].set_ylabel(f"Channel {c+1}")
        if xlim:
            axes[c].set_xlim(xlim)
    figure.suptitle(title)

def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
    waveform = waveform.numpy()

    num_channels, _ = waveform.shape

    figure, axes = plt.subplots(num_channels, 1)
    if num_channels == 1:
        axes = [axes]
    for c in range(num_channels):
        axes[c].specgram(waveform[c], Fs=sample_rate)
        if num_channels > 1:
            axes[c].set_ylabel(f"Channel {c+1}")
        if xlim:
            axes[c].set_xlim(xlim)
    figure.suptitle(title)

原始内容

plot_waveform(waveform1.T, sample_rate, title="Original", xlim=(-0.1, 3.2))
plot_specgram(waveform1.T, sample_rate, title="Original", xlim=(0, 3.04))
Audio(waveform1.T, rate=sample_rate)
  • Original
  • Original


应用的效果

plot_waveform(waveform2.T, sample_rate, title="Effects Applied", xlim=(-0.1, 3.2))
plot_specgram(waveform2.T, sample_rate, title="Effects Applied", xlim=(0, 3.04))
Audio(waveform2.T, rate=sample_rate)
  • Effects Applied
  • Effects Applied


模拟房间混响

卷积混响 是一种用于使干净音频听起来像是在不同环境中生成的技术。

例如，使用房间脉冲响应（RIR），我们可以使清晰的语音听起来就像是在会议室中发出的一样。

为此过程，我们需要混响（RIR）数据。以下数据来自 VOiCES 数据集，但你也可以自己录制——只需打开麦克风并拍手即可。

rir_raw, sample_rate = torchaudio.load(SAMPLE_RIR)
plot_waveform(rir_raw, sample_rate, title="Room Impulse Response (raw)")
plot_specgram(rir_raw, sample_rate, title="Room Impulse Response (raw)")
Audio(rir_raw, rate=sample_rate)
  • Room Impulse Response (raw)
  • Room Impulse Response (raw)


首先，我们需要清理 RIR。我们提取主要脉冲并按其功率进行归一化。

rir = rir_raw[:, int(sample_rate * 1.01) : int(sample_rate * 1.3)]
rir = rir / torch.linalg.vector_norm(rir, ord=2)

plot_waveform(rir, sample_rate, title="Room Impulse Response")
Room Impulse Response

然后，使用 torchaudio.functional.fftconvolve(), 我们将语音信号与RIR进行卷积。

speech, _ = torchaudio.load(SAMPLE_SPEECH)
augmented = F.fftconvolve(speech, rir)

原始内容

plot_waveform(speech, sample_rate, title="Original")
plot_specgram(speech, sample_rate, title="Original")
Audio(speech, rate=sample_rate)
  • Original
  • Original


RIR 应用

plot_waveform(augmented, sample_rate, title="RIR Applied")
plot_specgram(augmented, sample_rate, title="RIR Applied")
Audio(augmented, rate=sample_rate)
  • RIR Applied
  • RIR Applied


添加背景噪声

为了向音频数据引入背景噪声，我们可以根据所需的信噪比（SNR） [wikipedia] ，将噪声 Tensor 添加到表示音频数据的 Tensor 中，该比例决定了输出中音频数据相对于噪声的强度。

$$ \mathrm{信噪比} = \frac{P_{信号}}{P_{噪声}} $$

$$ \mathrm{SNR_{dB}} = 10 \log _{{10}} \mathrm {SNR} $$

要根据信噪比（SNRs）向音频数据添加噪声，我们使用 torchaudio.functional.add_noise()。

speech, _ = torchaudio.load(SAMPLE_SPEECH)
noise, _ = torchaudio.load(SAMPLE_NOISE)
noise = noise[:, : speech.shape[1]]

snr_dbs = torch.tensor([20, 10, 3])
noisy_speeches = F.add_noise(speech, noise, snr_dbs)

背景噪声

plot_waveform(noise, sample_rate, title="Background noise")
plot_specgram(noise, sample_rate, title="Background noise")
Audio(noise, rate=sample_rate)
  • Background noise
  • Background noise


信噪比 20 dB

snr_db, noisy_speech = snr_dbs[0], noisy_speeches[0:1]
plot_waveform(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
plot_specgram(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
Audio(noisy_speech, rate=sample_rate)
  • SNR: 20 [dB]
  • SNR: 20 [dB]


信噪比 10 dB

snr_db, noisy_speech = snr_dbs[1], noisy_speeches[1:2]
plot_waveform(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
plot_specgram(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
Audio(noisy_speech, rate=sample_rate)
  • SNR: 10 [dB]
  • SNR: 10 [dB]


信噪比 3 dB

snr_db, noisy_speech = snr_dbs[2], noisy_speeches[2:3]
plot_waveform(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
plot_specgram(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
Audio(noisy_speech, rate=sample_rate)
  • SNR: 3 [dB]
  • SNR: 3 [dB]


将编解码器应用于张量对象

torchaudio.io.AudioEffector 也可以将编解码器应用于 一个 Tensor 对象。

waveform, sample_rate = torchaudio.load(SAMPLE_SPEECH, channels_first=False)


def apply_codec(waveform, sample_rate, format, encoder=None):
    encoder = torchaudio.io.AudioEffector(format=format, encoder=encoder)
    return encoder.apply(waveform, sample_rate)

原始内容

plot_waveform(waveform.T, sample_rate, title="Original")
plot_specgram(waveform.T, sample_rate, title="Original")
Audio(waveform.T, rate=sample_rate)
  • Original
  • Original


8 bit mu-law

mulaw = apply_codec(waveform, sample_rate, "wav", encoder="pcm_mulaw")
plot_waveform(mulaw.T, sample_rate, title="8 bit mu-law")
plot_specgram(mulaw.T, sample_rate, title="8 bit mu-law")
Audio(mulaw.T, rate=sample_rate)
  • 8 bit mu-law
  • 8 bit mu-law


G.722

g722 = apply_codec(waveform, sample_rate, "g722")
plot_waveform(g722.T, sample_rate, title="G.722")
plot_specgram(g722.T, sample_rate, title="G.722")
Audio(g722.T, rate=sample_rate)
  • G.722
  • G.722


Vorbis

vorbis = apply_codec(waveform, sample_rate, "ogg", encoder="vorbis")
plot_waveform(vorbis.T, sample_rate, title="Vorbis")
plot_specgram(vorbis.T, sample_rate, title="Vorbis")
Audio(vorbis.T, rate=sample_rate)
  • Vorbis
  • Vorbis


模拟手机录音

结合之前的技巧，我们可以模拟出听起来像一个人在回声较大的房间中通过电话讲话的音频，同时还有背景中人们交谈的声音。

sample_rate = 16000
original_speech, sample_rate = torchaudio.load(SAMPLE_SPEECH)

plot_specgram(original_speech, sample_rate, title="Original")

# Apply RIR
rir_applied = F.fftconvolve(speech, rir)

plot_specgram(rir_applied, sample_rate, title="RIR Applied")

# Add background noise
# Because the noise is recorded in the actual environment, we consider that
# the noise contains the acoustic feature of the environment. Therefore, we add
# the noise after RIR application.
noise, _ = torchaudio.load(SAMPLE_NOISE)
noise = noise[:, : rir_applied.shape[1]]

snr_db = torch.tensor([8])
bg_added = F.add_noise(rir_applied, noise, snr_db)

plot_specgram(bg_added, sample_rate, title="BG noise added")

# Apply filtering and change sample rate
effect = ",".join(
    [
        "lowpass=frequency=4000:poles=1",
        "compand=attacks=0.02:decays=0.05:points=-60/-60|-30/-10|-20/-8|-5/-8|-2/-8:gain=-8:volume=-7:delay=0.05",
    ]
)

filtered = apply_effect(bg_added.T, sample_rate, effect)
sample_rate2 = 8000

plot_specgram(filtered.T, sample_rate2, title="Filtered")

# Apply telephony codec
codec_applied = apply_codec(filtered, sample_rate2, "g722")
plot_specgram(codec_applied.T, sample_rate2, title="G.722 Codec Applied")
  • Original
  • RIR Applied
  • BG noise added
  • Filtered
  • G.722 Codec Applied

原始语音

Audio(original_speech, rate=sample_rate)


RIR 应用

Audio(rir_applied, rate=sample_rate)


添加背景噪声

Audio(bg_added, rate=sample_rate)


过滤器

Audio(filtered.T, rate=sample_rate2)


应用的编解码器

Audio(codec_applied.T, rate=sample_rate2)


脚本总运行时间： ( 0 分钟 13.432 秒)

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