1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
|
# -*- coding: utf-8 -*-
"""
Audio Data Augmentation
=======================
**Author**: `Moto Hira <moto@meta.com>`__
``torchaudio`` provides a variety of ways to augment audio data.
In this tutorial, we look into a way to apply effects, filters,
RIR (room impulse response) and codecs.
At the end, we synthesize noisy speech over phone from clean speech.
"""
import torch
import torchaudio
import torchaudio.functional as F
print(torch.__version__)
print(torchaudio.__version__)
import matplotlib.pyplot as plt
######################################################################
# Preparation
# -----------
#
# First, we import the modules and download the audio assets we use in this tutorial.
#
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")
######################################################################
# Loading the data
# ------------------------------
#
waveform1, sample_rate = torchaudio.load(SAMPLE_WAV, channels_first=False)
print(waveform1.shape, sample_rate)
######################################################################
# Let’s listen to the audio.
#
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)
######################################################################
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)
######################################################################
# Simulating room reverberation
# -----------------------------
#
# `Convolution
# reverb <https://en.wikipedia.org/wiki/Convolution_reverb>`__ is a
# technique that's used to make clean audio sound as though it has been
# produced in a different environment.
#
# Using Room Impulse Response (RIR), for instance, we can make clean speech
# sound as though it has been uttered in a conference room.
#
# For this process, we need RIR data. The following data are from the VOiCES
# dataset, but you can record your own — just turn on your microphone
# and clap your hands.
#
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)
######################################################################
# First, we need to clean up the RIR. We extract the main impulse and normalize
# it by its power.
#
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")
######################################################################
# Then, using :py:func:`torchaudio.functional.fftconvolve`,
# we convolve the speech signal with the RIR.
#
speech, _ = torchaudio.load(SAMPLE_SPEECH)
augmented = F.fftconvolve(speech, rir)
######################################################################
# Original
# ~~~~~~~~
#
plot_waveform(speech, sample_rate, title="Original")
plot_specgram(speech, sample_rate, title="Original")
Audio(speech, rate=sample_rate)
######################################################################
# RIR applied
# ~~~~~~~~~~~
#
plot_waveform(augmented, sample_rate, title="RIR Applied")
plot_specgram(augmented, sample_rate, title="RIR Applied")
Audio(augmented, rate=sample_rate)
######################################################################
# Adding background noise
# -----------------------
#
# To introduce background noise to audio data, we can add a noise Tensor to
# the Tensor representing the audio data according to some desired
# signal-to-noise ratio (SNR)
# [`wikipedia <https://en.wikipedia.org/wiki/Signal-to-noise_ratio>`__],
# which determines the intensity of the audio data relative to that of the noise
# in the output.
#
# $$ \\mathrm{SNR} = \\frac{P_{signal}}{P_{noise}} $$
#
# $$ \\mathrm{SNR_{dB}} = 10 \\log _{{10}} \\mathrm {SNR} $$
#
# To add noise to audio data per SNRs, we
# use :py:func:`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)
######################################################################
# Background noise
# ~~~~~~~~~~~~~~~~
#
plot_waveform(noise, sample_rate, title="Background noise")
plot_specgram(noise, sample_rate, title="Background noise")
Audio(noise, rate=sample_rate)
######################################################################
# SNR 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 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 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)
|
