Note
Click here to download the full example code
Audio Data Augmentation¶
Author: Moto Hira
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
2.6.0.dev20241003
2.5.0.dev20241004
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")
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Applying effects and filtering¶
torchaudio.io.AudioEffector
allows for directly applying
filters and codecs to Tensor objects, in a similar way as ffmpeg
command
AudioEffector Usages <./effector_tutorial.html> explains how to use this class, so for the detail, please refer to the tutorial.
# 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
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)
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)
Original¶
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)
Effects applied¶
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)