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Audio Data Augmentation

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





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")


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Applying effects and filtering

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:

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.

Tip If you need to load and resample your audio data on the fly, then you can use torchaudio.sox_effects.apply_effects_file() with effect "rate".

Note torchaudio.sox_effects.apply_effects_file() accepts a file-like object or path-like object. Similar to 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)


torch.Size([2, 109368]) 44100
torch.Size([2, 136710]) 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)
        if num_channels > 1:
            axes[c].set_ylabel(f"Channel {c+1}")
        if xlim:
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:


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)
  • Original
  • Original

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)
  • Effects Applied
  • Effects Applied