Audio Data Augmentation

torchaudio provides a variety of ways to augment audio data.

import torch
import torchaudio
import torchaudio.functional as F

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

Out:

1.11.0+cpu
0.11.0+cpu

Preparing data and utility functions (skip this section)

# @title Prepare data and utility functions. {display-mode: "form"}
# @markdown
# @markdown You do not need to look into this cell.
# @markdown Just execute once and you are good to go.
# @markdown
# @markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/),
# @markdown which is licensed under Creative Commos BY 4.0.

# -------------------------------------------------------------------------------
# Preparation of data and helper functions.
# -------------------------------------------------------------------------------

import math
import os

import matplotlib.pyplot as plt
import requests
from IPython.display import Audio, display


_SAMPLE_DIR = "_assets"

SAMPLE_WAV_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.wav"
SAMPLE_WAV_PATH = os.path.join(_SAMPLE_DIR, "steam.wav")

SAMPLE_RIR_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/room-response/rm1/impulse/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo.wav"  # noqa: E501
SAMPLE_RIR_PATH = os.path.join(_SAMPLE_DIR, "rir.wav")

SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"  # noqa: E501
SAMPLE_WAV_SPEECH_PATH = os.path.join(_SAMPLE_DIR, "speech.wav")

SAMPLE_NOISE_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/distractors/rm1/babb/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo.wav"  # noqa: E501
SAMPLE_NOISE_PATH = os.path.join(_SAMPLE_DIR, "bg.wav")

os.makedirs(_SAMPLE_DIR, exist_ok=True)


def _fetch_data():
    uri = [
        (SAMPLE_WAV_URL, SAMPLE_WAV_PATH),
        (SAMPLE_RIR_URL, SAMPLE_RIR_PATH),
        (SAMPLE_WAV_SPEECH_URL, SAMPLE_WAV_SPEECH_PATH),
        (SAMPLE_NOISE_URL, SAMPLE_NOISE_PATH),
    ]
    for url, path in uri:
        with open(path, "wb") as file_:
            file_.write(requests.get(url).content)


_fetch_data()


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_sample(*, resample=None):
    return _get_sample(SAMPLE_WAV_PATH, resample=resample)


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


def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=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)
        if ylim:
            axes[c].set_ylim(ylim)
    figure.suptitle(title)
    plt.show(block=False)


def print_stats(waveform, sample_rate=None, src=None):
    if src:
        print("-" * 10)
        print("Source:", src)
        print("-" * 10)
    if sample_rate:
        print("Sample Rate:", sample_rate)
    print("Shape:", tuple(waveform.shape))
    print("Dtype:", waveform.dtype)
    print(f" - Max:     {waveform.max().item():6.3f}")
    print(f" - Min:     {waveform.min().item():6.3f}")
    print(f" - Mean:    {waveform.mean().item():6.3f}")
    print(f" - Std Dev: {waveform.std().item():6.3f}")
    print()
    print(waveform)
    print()


def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
    waveform = waveform.numpy()

    num_channels, num_frames = 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)


def play_audio(waveform, sample_rate):
    waveform = waveform.numpy()

    num_channels, num_frames = waveform.shape
    if num_channels == 1:
        return Audio(waveform[0], rate=sample_rate)
    elif num_channels == 2:
        return Audio((waveform[0], waveform[1]), rate=sample_rate)
    else:
        raise ValueError("Waveform with more than 2 channels are not supported.")


def get_rir_sample(*, resample=None, processed=False):
    rir_raw, sample_rate = _get_sample(SAMPLE_RIR_PATH, resample=resample)
    if not processed:
        return rir_raw, sample_rate
    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])
    return rir, sample_rate


def get_noise_sample(*, resample=None):
    return _get_sample(SAMPLE_NOISE_PATH, resample=resample)

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:

• torchaudio.sox_effects.apply_effects_tensor() for applying effects to Tensor.

• 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.

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 = get_sample(resample=16000)

# 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_stats(waveform1, sample_rate=sample_rate1, src="Original")
print_stats(waveform2, sample_rate=sample_rate2, src="Effects Applied")

Out:

----------
Source: Original
----------
Sample Rate: 16000
Shape: (1, 39680)
Dtype: torch.float32
 - Max:      0.507
 - Min:     -0.448
 - Mean:    -0.000
 - Std Dev:  0.122

tensor([[ 0.0007,  0.0076,  0.0122,  ..., -0.0049, -0.0025,  0.0020]])

----------
Source: Effects Applied
----------
Sample Rate: 16000
Shape: (2, 49600)
Dtype: torch.float32
 - Max:      0.091
 - Min:     -0.091
 - Mean:    -0.000
 - Std Dev:  0.021

tensor([[0.0000, 0.0000, 0.0000,  ..., 0.0069, 0.0058, 0.0045],
        [0.0000, 0.0000, 0.0000,  ..., 0.0085, 0.0085, 0.0085]])

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.

Original:

plot_waveform(waveform1, sample_rate1, title="Original", xlim=(-0.1, 3.2))
plot_specgram(waveform1, sample_rate1, title="Original", xlim=(0, 3.04))
play_audio(waveform1, sample_rate1)

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• 

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))
play_audio(waveform2, sample_rate2)

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• 

Doesn’t it sound more dramatic?

Simulating room reverberation

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.

sample_rate = 8000

rir_raw, _ = get_rir_sample(resample=sample_rate)

plot_waveform(rir_raw, sample_rate, title="Room Impulse Response (raw)", ylim=None)
plot_specgram(rir_raw, sample_rate, title="Room Impulse Response (raw)")
play_audio(rir_raw, sample_rate)

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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])

print_stats(rir)
plot_waveform(rir, sample_rate, title="Room Impulse Response", ylim=None)

Out:

Shape: (1, 2320)
Dtype: torch.float32
 - Max:      0.395
 - Min:     -0.286
 - Mean:    -0.000
 - Std Dev:  0.021

tensor([[-0.0052, -0.0076, -0.0071,  ...,  0.0184,  0.0173,  0.0070]])

Then, we convolve the speech signal with the RIR filter.

speech, _ = get_speech_sample(resample=sample_rate)

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", ylim=None)
plot_specgram(speech, sample_rate, title="Original")
play_audio(speech, sample_rate)

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• 

RIR applied:

plot_waveform(augmented, sample_rate, title="RIR Applied", ylim=None)
plot_specgram(augmented, sample_rate, title="RIR Applied")
play_audio(augmented, sample_rate)

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• 

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]

$$ \mathrm{SNR} = \frac{P_{signal}}{P_{noise}} $$

$$ \mathrm{SNR_{dB}} = 10 \log _{{10}} \mathrm {SNR} $$

sample_rate = 8000
speech, _ = get_speech_sample(resample=sample_rate)
noise, _ = get_noise_sample(resample=sample_rate)
noise = noise[:, : speech.shape[1]]

speech_power = speech.norm(p=2)
noise_power = noise.norm(p=2)

snr_dbs = [20, 10, 3]
noisy_speeches = []
for snr_db in snr_dbs:
    snr = math.exp(snr_db / 10)
    scale = snr * noise_power / speech_power
    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")
play_audio(noise, sample_rate)

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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]")
play_audio(noisy_speech, sample_rate)

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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]")
play_audio(noisy_speech, sample_rate)

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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]")
play_audio(noisy_speech, sample_rate)

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Applying codec to Tensor object

torchaudio.functional.apply_codec() can apply codecs to a Tensor object.

Note This process is not differentiable.

waveform, sample_rate = get_speech_sample(resample=8000)

plot_specgram(waveform, sample_rate, title="Original")

configs = [
    ({"format": "wav", "encoding": "ULAW", "bits_per_sample": 8}, "8 bit mu-law"),
    ({"format": "gsm"}, "GSM-FR"),
    ({"format": "mp3", "compression": -9}, "MP3"),
    ({"format": "vorbis", "compression": -1}, "Vorbis"),
]
waveforms = []
for param, title in configs:
    augmented = F.apply_codec(waveform, sample_rate, **param)
    plot_specgram(augmented, sample_rate, title=title)
    waveforms.append(augmented)

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Original:

play_audio(waveform, sample_rate)

8 bit mu-law:

play_audio(waveforms[0], sample_rate)

GSM-FR:

play_audio(waveforms[1], sample_rate)

MP3:

play_audio(waveforms[2], sample_rate)

Vorbis:

play_audio(waveforms[3], 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, _ = get_speech_sample(resample=sample_rate)

plot_specgram(original_speech, sample_rate, title="Original")

# Apply RIR
rir, _ = get_rir_sample(resample=sample_rate, processed=True)
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, _ = get_noise_sample(resample=sample_rate)
noise = noise[:, : rir_applied.shape[1]]

snr_db = 8
scale = math.exp(snr_db / 10) * 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")

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Original speech:

play_audio(original_speech, sample_rate)

RIR applied:

play_audio(rir_applied, sample_rate)

Background noise added:

play_audio(bg_added, sample_rate)

Filtered:

play_audio(filtered, sample_rate2)

Codec aplied:

play_audio(codec_applied, sample_rate2)