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tutorials/audio_data_augmentation_tutorial

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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

2.8.0+cu126
2.8.0

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")

/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:36: UserWarning: torchaudio.utils.download.download_asset has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  SAMPLE_WAV = download_asset("tutorial-assets/steam-train-whistle-daniel_simon.wav")

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/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:37: UserWarning: torchaudio.utils.download.download_asset has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  SAMPLE_RIR = download_asset("tutorial-assets/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo-8000hz.wav")

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/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:38: UserWarning: torchaudio.utils.download.download_asset has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  SAMPLE_SPEECH = download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042-8000hz.wav")
/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:39: UserWarning: torchaudio.utils.download.download_asset has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  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)

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)
/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:70: UserWarning: torchaudio.io._effector.AudioEffector has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  effector = torchaudio.io.AudioEffector(effect=effect)
/pytorch/audio/src/torchaudio/io/_effector.py:98: UserWarning: torio.io._streaming_media_encoder.StreamingMediaEncoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  writer = StreamWriter(buffer, format=muxer)
/pytorch/audio/src/torchaudio/io/_effector.py:286: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  reader = StreamReader(src, format=muxer, option=option)
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)
  • Original
  • Original


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)
  • Effects Applied
  • Effects Applied


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.

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)
  • Room Impulse Response (raw)
  • Room Impulse Response (raw)
/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)


First, we need to clean up the RIR. We extract the main impulse and normalize it by its power.

rir = rir_raw[:, int(sample_rate * 1.01) : int(sample_rate * 1.3)]
rir = rir / torch.linalg.vector_norm(rir, ord=2)

plot_waveform(rir, sample_rate, title="Room Impulse Response")
Room Impulse Response

Then, using torchaudio.functional.fftconvolve(), we convolve the speech signal with the RIR.

speech, _ = torchaudio.load(SAMPLE_SPEECH)
augmented = F.fftconvolve(speech, rir)

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

Original

plot_waveform(speech, sample_rate, title="Original")
plot_specgram(speech, sample_rate, title="Original")
Audio(speech, rate=sample_rate)
  • Original
  • Original


RIR applied

plot_waveform(augmented, sample_rate, title="RIR Applied")
plot_specgram(augmented, sample_rate, title="RIR Applied")
Audio(augmented, rate=sample_rate)
  • RIR Applied
  • RIR Applied


Adding background noise

To introduce background noise to audio data, we can add a noise Tensor to the Tensor representing the audio data according to some desired signal-to-noise ratio (SNR) [wikipedia], which determines the intensity of the audio data relative to that of the noise in the output.

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

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

To add noise to audio data per SNRs, we use torchaudio.functional.add_noise().

speech, _ = torchaudio.load(SAMPLE_SPEECH)
noise, _ = torchaudio.load(SAMPLE_NOISE)
noise = noise[:, : speech.shape[1]]

snr_dbs = torch.tensor([20, 10, 3])
noisy_speeches = F.add_noise(speech, noise, snr_dbs)

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

Background noise

plot_waveform(noise, sample_rate, title="Background noise")
plot_specgram(noise, sample_rate, title="Background noise")
Audio(noise, rate=sample_rate)
  • Background noise
  • Background noise


SNR 20 dB

snr_db, noisy_speech = snr_dbs[0], noisy_speeches[0:1]
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: 20 [dB]
  • SNR: 20 [dB]


SNR 10 dB

snr_db, noisy_speech = snr_dbs[1], noisy_speeches[1:2]
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)
  • SNR: 10 [dB]
  • SNR: 10 [dB]


SNR 3 dB

snr_db, noisy_speech = snr_dbs[2], noisy_speeches[2:3]
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: 3 [dB]


Applying codec to Tensor object

torchaudio.io.AudioEffector can also apply codecs to a Tensor object.

waveform, sample_rate = torchaudio.load(SAMPLE_SPEECH, channels_first=False)


def apply_codec(waveform, sample_rate, format, encoder=None):
    encoder = torchaudio.io.AudioEffector(format=format, encoder=encoder)
    return encoder.apply(waveform, sample_rate)

/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)

Original

plot_waveform(waveform.T, sample_rate, title="Original")
plot_specgram(waveform.T, sample_rate, title="Original")
Audio(waveform.T, rate=sample_rate)
  • Original
  • Original


8 bit mu-law

mulaw = apply_codec(waveform, sample_rate, "wav", encoder="pcm_mulaw")
plot_waveform(mulaw.T, sample_rate, title="8 bit mu-law")
plot_specgram(mulaw.T, sample_rate, title="8 bit mu-law")
Audio(mulaw.T, rate=sample_rate)
  • 8 bit mu-law
  • 8 bit mu-law
/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:282: UserWarning: torchaudio.io._effector.AudioEffector has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  encoder = torchaudio.io.AudioEffector(format=format, encoder=encoder)
/pytorch/audio/src/torchaudio/io/_effector.py:98: UserWarning: torio.io._streaming_media_encoder.StreamingMediaEncoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  writer = StreamWriter(buffer, format=muxer)
/pytorch/audio/src/torchaudio/io/_effector.py:286: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  reader = StreamReader(src, format=muxer, option=option)


G.722

g722 = apply_codec(waveform, sample_rate, "g722")
plot_waveform(g722.T, sample_rate, title="G.722")
plot_specgram(g722.T, sample_rate, title="G.722")
Audio(g722.T, rate=sample_rate)
  • G.722
  • G.722
/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:282: UserWarning: torchaudio.io._effector.AudioEffector has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  encoder = torchaudio.io.AudioEffector(format=format, encoder=encoder)
/pytorch/audio/src/torchaudio/io/_effector.py:98: UserWarning: torio.io._streaming_media_encoder.StreamingMediaEncoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  writer = StreamWriter(buffer, format=muxer)
/pytorch/audio/src/torchaudio/io/_effector.py:286: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  reader = StreamReader(src, format=muxer, option=option)


Vorbis

vorbis = apply_codec(waveform, sample_rate, "ogg", encoder="vorbis")
plot_waveform(vorbis.T, sample_rate, title="Vorbis")
plot_specgram(vorbis.T, sample_rate, title="Vorbis")
Audio(vorbis.T, rate=sample_rate)
  • Vorbis
  • Vorbis
/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:282: UserWarning: torchaudio.io._effector.AudioEffector has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  encoder = torchaudio.io.AudioEffector(format=format, encoder=encoder)
/pytorch/audio/src/torchaudio/io/_effector.py:98: UserWarning: torio.io._streaming_media_encoder.StreamingMediaEncoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  writer = StreamWriter(buffer, format=muxer)
/pytorch/audio/src/torchaudio/io/_effector.py:286: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  reader = StreamReader(src, format=muxer, option=option)


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
rir_applied = F.fftconvolve(speech, rir)

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 = torch.tensor([8])
bg_added = F.add_noise(rir_applied, noise, snr_db)

plot_specgram(bg_added, sample_rate, title="BG noise added")

# Apply filtering and change sample rate
effect = ",".join(
    [
        "lowpass=frequency=4000:poles=1",
        "compand=attacks=0.02:decays=0.05:points=-60/-60|-30/-10|-20/-8|-5/-8|-2/-8:gain=-8:volume=-7:delay=0.05",
    ]
)

filtered = apply_effect(bg_added.T, sample_rate, effect)
sample_rate2 = 8000

plot_specgram(filtered.T, sample_rate2, title="Filtered")

# Apply telephony codec
codec_applied = apply_codec(filtered, sample_rate2, "g722")
plot_specgram(codec_applied.T, sample_rate2, title="G.722 Codec Applied")
  • Original
  • RIR Applied
  • BG noise added
  • Filtered
  • G.722 Codec Applied
/pytorch/audio/src/torchaudio/_backend/utils.py:213: UserWarning: In 2.9, this function's implementation will be changed to use torchaudio.load_with_torchcodec` under the hood. Some parameters like ``normalize``, ``format``, ``buffer_size``, and ``backend`` will be ignored. We recommend that you port your code to rely directly on TorchCodec's decoder instead: https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.html#torchcodec.decoders.AudioDecoder.
  warnings.warn(
/pytorch/audio/src/torchaudio/_backend/ffmpeg.py:88: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  s = torchaudio.io.StreamReader(src, format, None, buffer_size)
/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:70: UserWarning: torchaudio.io._effector.AudioEffector has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  effector = torchaudio.io.AudioEffector(effect=effect)
/pytorch/audio/src/torchaudio/io/_effector.py:98: UserWarning: torio.io._streaming_media_encoder.StreamingMediaEncoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  writer = StreamWriter(buffer, format=muxer)
/pytorch/audio/src/torchaudio/io/_effector.py:286: UserWarning: torio.io._streaming_media_decoder.StreamingMediaDecoder has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  reader = StreamReader(src, format=muxer, option=option)
/pytorch/audio/examples/tutorials/audio_data_augmentation_tutorial.py:282: UserWarning: torchaudio.io._effector.AudioEffector has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release.
  encoder = torchaudio.io.AudioEffector(format=format, encoder=encoder)

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.T, rate=sample_rate2)


Codec applied

Audio(codec_applied.T, rate=sample_rate2)


Total running time of the script: ( 0 minutes 13.778 seconds)

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