注意
点击 这里 下载完整示例代码
音频特征提取¶
作者: Moto Hira
torchaudio 实现了音频领域常用的特征提取方法。它们在 torchaudio.functional 和
torchaudio.transforms 中可用。
functional 以独立函数的形式实现功能。
它们是无状态的。
transforms 将功能实现为对象，
使用来自 functional 和 torch.nn.Module 的实现。
它们可以使用 TorchScript 进行序列化。
import torch
import torchaudio
import torchaudio.functional as F
import torchaudio.transforms as T
print(torch.__version__)
print(torchaudio.__version__)
2.0.0
2.0.1
准备¶
注意
在 Google Colab 中运行本教程时，请安装所需的软件包
!pip install librosa
from IPython.display import Audio
import librosa
import matplotlib.pyplot as plt
from torchaudio.utils import download_asset
torch.random.manual_seed(0)
SAMPLE_SPEECH = download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")
def plot_waveform(waveform, sr, title="Waveform"):
waveform = waveform.numpy()
num_channels, num_frames = waveform.shape
time_axis = torch.arange(0, num_frames) / sr
figure, axes = plt.subplots(num_channels, 1)
axes.plot(time_axis, waveform[0], linewidth=1)
axes.grid(True)
figure.suptitle(title)
plt.show(block=False)
def plot_spectrogram(specgram, title=None, ylabel="freq_bin"):
fig, axs = plt.subplots(1, 1)
axs.set_title(title or "Spectrogram (db)")
axs.set_ylabel(ylabel)
axs.set_xlabel("frame")
im = axs.imshow(librosa.power_to_db(specgram), origin="lower", aspect="auto")
fig.colorbar(im, ax=axs)
plt.show(block=False)
def plot_fbank(fbank, title=None):
fig, axs = plt.subplots(1, 1)
axs.set_title(title or "Filter bank")
axs.imshow(fbank, aspect="auto")
axs.set_ylabel("frequency bin")
axs.set_xlabel("mel bin")
plt.show(block=False)
音频功能概述¶
下图展示了常见音频特征与用于生成它们的 torchaudio API 之间的关系。
如需查看完整的可用功能列表，请参阅文档。
频谱图¶
要获取音频信号随时间变化的频率组成，
你可以使用 torchaudio.transforms.Spectrogram()。
SPEECH_WAVEFORM, SAMPLE_RATE = torchaudio.load(SAMPLE_SPEECH)
plot_waveform(SPEECH_WAVEFORM, SAMPLE_RATE, title="Original waveform")
Audio(SPEECH_WAVEFORM.numpy(), rate=SAMPLE_RATE)
n_fft = 1024
win_length = None
hop_length = 512
# Define transform
spectrogram = T.Spectrogram(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
center=True,
pad_mode="reflect",
power=2.0,
)
# Perform transform
spec = spectrogram(SPEECH_WAVEFORM)
plot_spectrogram(spec[0], title="torchaudio")
GriffinLim¶
要从频谱图恢复波形，您可以使用 GriffinLim。
torch.random.manual_seed(0)
n_fft = 1024
win_length = None
hop_length = 512
spec = T.Spectrogram(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
)(SPEECH_WAVEFORM)
griffin_lim = T.GriffinLim(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
)
reconstructed_waveform = griffin_lim(spec)
plot_waveform(reconstructed_waveform, SAMPLE_RATE, title="Reconstructed")
Audio(reconstructed_waveform, rate=SAMPLE_RATE)
梅尔滤波器组¶
torchaudio.functional.melscale_fbanks() 生成用于将频率分 bin 转换为梅尔刻度分 bin 的滤波器组。
由于此函数不需要输入音频/特征，因此在 torchaudio.transforms() 中没有等效的转换。
n_fft = 256
n_mels = 64
sample_rate = 6000
mel_filters = F.melscale_fbanks(
int(n_fft // 2 + 1),
n_mels=n_mels,
f_min=0.0,
f_max=sample_rate / 2.0,
sample_rate=sample_rate,
norm="slaney",
)
plot_fbank(mel_filters, "Mel Filter Bank - torchaudio")
与librosa的对比¶
作为参考，以下是使用 librosa 获取 mel 滤波器组的等效方法。
mel_filters_librosa = librosa.filters.mel(
sr=sample_rate,
n_fft=n_fft,
n_mels=n_mels,
fmin=0.0,
fmax=sample_rate / 2.0,
norm="slaney",
htk=True,
).T
plot_fbank(mel_filters_librosa, "Mel Filter Bank - librosa")
mse = torch.square(mel_filters - mel_filters_librosa).mean().item()
print("Mean Square Difference: ", mse)
Mean Square Difference: 3.934872696751886e-17
MelSpectrogram¶
生成一个梅尔频谱图涉及生成一个频谱图
并进行梅尔尺度转换。在 torchaudio,
torchaudio.transforms.MelSpectrogram() 提供了
此功能。
n_fft = 1024
win_length = None
hop_length = 512
n_mels = 128
mel_spectrogram = T.MelSpectrogram(
sample_rate=sample_rate,
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
center=True,
pad_mode="reflect",
power=2.0,
norm="slaney",
onesided=True,
n_mels=n_mels,
mel_scale="htk",
)
melspec = mel_spectrogram(SPEECH_WAVEFORM)
/usr/local/envs/python3.8/lib/python3.8/site-packages/torchaudio/transforms/_transforms.py:611: UserWarning: Argument 'onesided' has been deprecated and has no influence on the behavior of this module.
warnings.warn(
plot_spectrogram(melspec[0], title="MelSpectrogram - torchaudio", ylabel="mel freq")
与librosa的对比¶
作为参考，以下是使用 librosa 生成梅尔尺度频谱图的等效方法。
melspec_librosa = librosa.feature.melspectrogram(
y=SPEECH_WAVEFORM.numpy()[0],
sr=sample_rate,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
center=True,
pad_mode="reflect",
power=2.0,
n_mels=n_mels,
norm="slaney",
htk=True,
)
plot_spectrogram(melspec_librosa, title="MelSpectrogram - librosa", ylabel="mel freq")
mse = torch.square(melspec - melspec_librosa).mean().item()
print("Mean Square Difference: ", mse)
Mean Square Difference: 1.2913579094941952e-09
MFCC¶
n_fft = 2048
win_length = None
hop_length = 512
n_mels = 256
n_mfcc = 256
mfcc_transform = T.MFCC(
sample_rate=sample_rate,
n_mfcc=n_mfcc,
melkwargs={
"n_fft": n_fft,
"n_mels": n_mels,
"hop_length": hop_length,
"mel_scale": "htk",
},
)
mfcc = mfcc_transform(SPEECH_WAVEFORM)
plot_spectrogram(mfcc[0])
与librosa的对比¶
melspec = librosa.feature.melspectrogram(
y=SPEECH_WAVEFORM.numpy()[0],
sr=sample_rate,
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
n_mels=n_mels,
htk=True,
norm=None,
)
mfcc_librosa = librosa.feature.mfcc(
S=librosa.core.spectrum.power_to_db(melspec),
n_mfcc=n_mfcc,
dct_type=2,
norm="ortho",
)
plot_spectrogram(mfcc_librosa)
mse = torch.square(mfcc - mfcc_librosa).mean().item()
print("Mean Square Difference: ", mse)
Mean Square Difference: 0.8103961944580078
LFCC¶
n_fft = 2048
win_length = None
hop_length = 512
n_lfcc = 256
lfcc_transform = T.LFCC(
sample_rate=sample_rate,
n_lfcc=n_lfcc,
speckwargs={
"n_fft": n_fft,
"win_length": win_length,
"hop_length": hop_length,
},
)
lfcc = lfcc_transform(SPEECH_WAVEFORM)
plot_spectrogram(lfcc[0])
简介¶
pitch = F.detect_pitch_frequency(SPEECH_WAVEFORM, SAMPLE_RATE)
def plot_pitch(waveform, sr, pitch):
figure, axis = plt.subplots(1, 1)
axis.set_title("Pitch Feature")
axis.grid(True)
end_time = waveform.shape[1] / sr
time_axis = torch.linspace(0, end_time, waveform.shape[1])
axis.plot(time_axis, waveform[0], linewidth=1, color="gray", alpha=0.3)
axis2 = axis.twinx()
time_axis = torch.linspace(0, end_time, pitch.shape[1])
axis2.plot(time_axis, pitch[0], linewidth=2, label="Pitch", color="green")
axis2.legend(loc=0)
plt.show(block=False)
plot_pitch(SPEECH_WAVEFORM, SAMPLE_RATE, pitch)
Kaldi 音高（测试版）¶
Kaldi 音高特征 [1] 是一种针对自动语音识别 (ASR) 应用进行优化的音高检测机制。这是 torchaudio 中的测试版功能，并作为 torchaudio.functional.compute_kaldi_pitch() 提供。
一种针对自动语音识别优化的音高提取算法
Ghahremani, B. BabaAli, D. Povey, K. Riedhammer, J. Trmal 和 S. Khudanpur
2014 IEEE国际声学、语音和信号处理会议 (ICASSP), 佛罗伦萨, 2014, pp. 2494-2498, doi: 10.1109/ICASSP.2014.6854049. [摘要], [论文]
pitch_feature = F.compute_kaldi_pitch(SPEECH_WAVEFORM, SAMPLE_RATE)
pitch, nfcc = pitch_feature[..., 0], pitch_feature[..., 1]
def plot_kaldi_pitch(waveform, sr, pitch, nfcc):
_, axis = plt.subplots(1, 1)
axis.set_title("Kaldi Pitch Feature")
axis.grid(True)
end_time = waveform.shape[1] / sr
time_axis = torch.linspace(0, end_time, waveform.shape[1])
axis.plot(time_axis, waveform[0], linewidth=1, color="gray", alpha=0.3)
time_axis = torch.linspace(0, end_time, pitch.shape[1])
ln1 = axis.plot(time_axis, pitch[0], linewidth=2, label="Pitch", color="green")
axis.set_ylim((-1.3, 1.3))
axis2 = axis.twinx()
time_axis = torch.linspace(0, end_time, nfcc.shape[1])
ln2 = axis2.plot(time_axis, nfcc[0], linewidth=2, label="NFCC", color="blue", linestyle="--")
lns = ln1 + ln2
labels = [l.get_label() for l in lns]
axis.legend(lns, labels, loc=0)
plt.show(block=False)
plot_kaldi_pitch(SPEECH_WAVEFORM, SAMPLE_RATE, pitch, nfcc)
脚本的总运行时间： ( 0 分钟 11.902 秒)