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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445
|
# -*- 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__)
######################################################################
# Preparation
# -----------
#
# First, we import the modules and download the audio assets we use in this tutorial.
#
import math
from IPython.display import Audio
import matplotlib.pyplot as plt
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")
######################################################################
# Applying effects and filtering
# ------------------------------
#
# :py:func:`torchaudio.sox_effects` allows for directly applying filters similar to
# those available in ``sox`` to Tensor objects and file object audio sources.
#
# There are two functions for this:
#
# - :py:func:`torchaudio.sox_effects.apply_effects_tensor` for applying effects
# to Tensor.
# - :py:func:`torchaudio.sox_effects.apply_effects_file` for applying effects to
# other audio sources.
#
# Both functions accept effect definitions in the form
# ``List[List[str]]``.
# This is mostly consistent with how ``sox`` command works, but one caveat is
# that ``sox`` adds some effects automatically, whereas ``torchaudio``’s
# implementation does not.
#
# For the list of available effects, please refer to `the sox
# documentation <http://sox.sourceforge.net/sox.html>`__.
#
# **Tip** If you need to load and resample your audio data on the fly,
# then you can use :py:func:`torchaudio.sox_effects.apply_effects_file`
# with effect ``"rate"``.
#
# **Note** :py:func:`torchaudio.sox_effects.apply_effects_file` accepts a
# file-like object or path-like object.
# Similar to :py:func:`torchaudio.load`, when the audio format cannot be
# inferred from either the file extension or header, you can provide
# argument ``format`` to specify the format of the audio source.
#
# **Note** This process is not differentiable.
#
# Load the data
waveform1, sample_rate1 = torchaudio.load(SAMPLE_WAV)
# Define effects
effects = [
["lowpass", "-1", "300"], # apply single-pole lowpass filter
["speed", "0.8"], # reduce the speed
# This only changes sample rate, so it is necessary to
# add `rate` effect with original sample rate after this.
["rate", f"{sample_rate1}"],
["reverb", "-w"], # Reverbration gives some dramatic feeling
]
# Apply effects
waveform2, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor(waveform1, sample_rate1, effects)
print(waveform1.shape, sample_rate1)
print(waveform2.shape, sample_rate2)
######################################################################
# Note that the number of frames and number of channels are different from
# those of the original after the effects are applied. 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)
plt.show(block=False)
######################################################################
#
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)
plt.show(block=False)
######################################################################
# Original:
# ~~~~~~~~~
#
plot_waveform(waveform1, sample_rate1, title="Original", xlim=(-0.1, 3.2))
plot_specgram(waveform1, sample_rate1, title="Original", xlim=(0, 3.04))
Audio(waveform1, rate=sample_rate1)
######################################################################
# Effects applied:
# ~~~~~~~~~~~~~~~~
#
plot_waveform(waveform2, sample_rate2, title="Effects Applied", xlim=(-0.1, 3.2))
plot_specgram(waveform2, sample_rate2, title="Effects Applied", xlim=(0, 3.04))
Audio(waveform2, rate=sample_rate2)
######################################################################
# Doesn’t it sound more dramatic?
#
######################################################################
# 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, normalize
# the signal power, then flip along the time axis.
#
rir = rir_raw[:, int(sample_rate * 1.01) : int(sample_rate * 1.3)]
rir = rir / torch.norm(rir, p=2)
RIR = torch.flip(rir, [1])
plot_waveform(rir, sample_rate, title="Room Impulse Response")
######################################################################
# Then, we convolve the speech signal with the RIR filter.
#
speech, _ = torchaudio.load(SAMPLE_SPEECH)
speech_ = torch.nn.functional.pad(speech, (RIR.shape[1] - 1, 0))
augmented = torch.nn.functional.conv1d(speech_[None, ...], RIR[None, ...])[0]
######################################################################
# 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 add background noise to audio data, you can simply add a noise Tensor to
# the Tensor representing the audio data. A common method to adjust the
# intensity of noise is changing the Signal-to-Noise Ratio (SNR).
# [`wikipedia <https://en.wikipedia.org/wiki/Signal-to-noise_ratio>`__]
#
# $$ \\mathrm{SNR} = \\frac{P_{signal}}{P_{noise}} $$
#
# $$ \\mathrm{SNR_{dB}} = 10 \\log _{{10}} \\mathrm {SNR} $$
#
speech, _ = torchaudio.load(SAMPLE_SPEECH)
noise, _ = torchaudio.load(SAMPLE_NOISE)
noise = noise[:, : speech.shape[1]]
speech_rms = speech.norm(p=2)
noise_rms = noise.norm(p=2)
snr_dbs = [20, 10, 3]
noisy_speeches = []
for snr_db in snr_dbs:
snr = 10 ** (snr_db / 20)
scale = snr * noise_rms / speech_rms
noisy_speeches.append((scale * speech + noise) / 2)
######################################################################
# 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]
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]
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]
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)
######################################################################
# Applying codec to Tensor object
# -------------------------------
#
# :py:func:`torchaudio.functional.apply_codec` can apply codecs to
# a Tensor object.
#
# **Note** This process is not differentiable.
#
waveform, sample_rate = torchaudio.load(SAMPLE_SPEECH)
configs = [
{"format": "wav", "encoding": "ULAW", "bits_per_sample": 8},
{"format": "gsm"},
{"format": "vorbis", "compression": -1},
]
waveforms = []
for param in configs:
augmented = F.apply_codec(waveform, sample_rate, **param)
waveforms.append(augmented)
######################################################################
# Original:
# ~~~~~~~~~
#
plot_waveform(waveform, sample_rate, title="Original")
plot_specgram(waveform, sample_rate, title="Original")
Audio(waveform, rate=sample_rate)
######################################################################
# 8 bit mu-law:
# ~~~~~~~~~~~~~
#
plot_waveform(waveforms[0], sample_rate, title="8 bit mu-law")
plot_specgram(waveforms[0], sample_rate, title="8 bit mu-law")
Audio(waveforms[0], rate=sample_rate)
######################################################################
# GSM-FR:
# ~~~~~~~
#
plot_waveform(waveforms[1], sample_rate, title="GSM-FR")
plot_specgram(waveforms[1], sample_rate, title="GSM-FR")
Audio(waveforms[1], rate=sample_rate)
######################################################################
# Vorbis:
# ~~~~~~~
#
plot_waveform(waveforms[2], sample_rate, title="Vorbis")
plot_specgram(waveforms[2], sample_rate, title="Vorbis")
Audio(waveforms[2], rate=sample_rate)
######################################################################
# Simulating a phone recoding
# ---------------------------
#
# Combining the previous techniques, we can simulate audio that sounds
# like a person talking over a phone in a echoey room with people talking
# in the background.
#
sample_rate = 16000
original_speech, sample_rate = torchaudio.load(SAMPLE_SPEECH)
plot_specgram(original_speech, sample_rate, title="Original")
# Apply RIR
speech_ = torch.nn.functional.pad(original_speech, (RIR.shape[1] - 1, 0))
rir_applied = torch.nn.functional.conv1d(speech_[None, ...], RIR[None, ...])[0]
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 = 8
scale = (10 ** (snr_db / 20)) * noise.norm(p=2) / rir_applied.norm(p=2)
bg_added = (scale * rir_applied + noise) / 2
plot_specgram(bg_added, sample_rate, title="BG noise added")
# Apply filtering and change sample rate
filtered, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor(
bg_added,
sample_rate,
effects=[
["lowpass", "4000"],
[
"compand",
"0.02,0.05",
"-60,-60,-30,-10,-20,-8,-5,-8,-2,-8",
"-8",
"-7",
"0.05",
],
["rate", "8000"],
],
)
plot_specgram(filtered, sample_rate2, title="Filtered")
# Apply telephony codec
codec_applied = F.apply_codec(filtered, sample_rate2, format="gsm")
plot_specgram(codec_applied, sample_rate2, title="GSM Codec Applied")
######################################################################
# Original speech:
# ~~~~~~~~~~~~~~~~
#
Audio(original_speech, rate=sample_rate)
######################################################################
# RIR applied:
# ~~~~~~~~~~~~
#
Audio(rir_applied, rate=sample_rate)
######################################################################
# Background noise added:
# ~~~~~~~~~~~~~~~~~~~~~~~
#
Audio(bg_added, rate=sample_rate)
######################################################################
# Filtered:
# ~~~~~~~~~
#
Audio(filtered, rate=sample_rate2)
######################################################################
# Codec applied:
# ~~~~~~~~~~~~~~
#
Audio(codec_applied, rate=sample_rate2)
|