Audio Feature Augmentation

Author: Moto Hira

# When running this tutorial in Google Colab, install the required packages
# with the following.
# !pip install torchaudio librosa

import torch
import torchaudio
import torchaudio.transforms as T

print(torch.__version__)
print(torchaudio.__version__)

2.1.1
2.1.2

Preparation

import librosa
import matplotlib.pyplot as plt
from IPython.display import Audio
from torchaudio.utils import download_asset

In this tutorial, we will use a speech data from VOiCES dataset, which is licensed under Creative Commos BY 4.0.

SAMPLE_WAV_SPEECH_PATH = download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")


def _get_sample(path, resample=None):
    effects = [["remix", "1"]]
    if resample:
        effects.extend(
            [
                ["lowpass", f"{resample // 2}"],
                ["rate", f"{resample}"],
            ]
        )
    return torchaudio.sox_effects.apply_effects_file(path, effects=effects)


def get_speech_sample(*, resample=None):
    return _get_sample(SAMPLE_WAV_SPEECH_PATH, resample=resample)


def get_spectrogram(
    n_fft=400,
    win_len=None,
    hop_len=None,
    power=2.0,
):
    waveform, _ = get_speech_sample()
    spectrogram = T.Spectrogram(
        n_fft=n_fft,
        win_length=win_len,
        hop_length=hop_len,
        center=True,
        pad_mode="reflect",
        power=power,
    )
    return spectrogram(waveform)

SpecAugment

SpecAugment is a popular spectrogram augmentation technique.

torchaudio implements torchaudio.transforms.TimeStretch(), torchaudio.transforms.TimeMasking() and torchaudio.transforms.FrequencyMasking().

TimeStretch

spec = get_spectrogram(power=None)
stretch = T.TimeStretch()

spec_12 = stretch(spec, overriding_rate=1.2)
spec_09 = stretch(spec, overriding_rate=0.9)

Visualization

def plot():
    def plot_spec(ax, spec, title):
        ax.set_title(title)
        ax.imshow(librosa.amplitude_to_db(spec), origin="lower", aspect="auto")

    fig, axes = plt.subplots(3, 1, sharex=True, sharey=True)
    plot_spec(axes[0], torch.abs(spec_12[0]), title="Stretched x1.2")
    plot_spec(axes[1], torch.abs(spec[0]), title="Original")
    plot_spec(axes[2], torch.abs(spec_09[0]), title="Stretched x0.9")
    fig.tight_layout()


plot()

Audio Samples

def preview(spec, rate=16000):
    ispec = T.InverseSpectrogram()
    waveform = ispec(spec)

    return Audio(waveform[0].numpy().T, rate=rate)


preview(spec)

preview(spec_12)

preview(spec_09)

Time and Frequency Masking

torch.random.manual_seed(4)

time_masking = T.TimeMasking(time_mask_param=80)
freq_masking = T.FrequencyMasking(freq_mask_param=80)

spec = get_spectrogram()
time_masked = time_masking(spec)
freq_masked = freq_masking(spec)

def plot():
    def plot_spec(ax, spec, title):
        ax.set_title(title)
        ax.imshow(librosa.power_to_db(spec), origin="lower", aspect="auto")

    fig, axes = plt.subplots(3, 1, sharex=True, sharey=True)
    plot_spec(axes[0], spec[0], title="Original")
    plot_spec(axes[1], time_masked[0], title="Masked along time axis")
    plot_spec(axes[2], freq_masked[0], title="Masked along frequency axis")
    fig.tight_layout()


plot()