Note
Click here to download the full example code
Audio Datasets¶
Author: Moto Hira
torchaudio
provides easy access to common, publicly accessible
datasets. Please refer to the official documentation for the list of
available datasets.
import torch
import torchaudio
print(torch.__version__)
print(torchaudio.__version__)
2.8.0+cu126
2.8.0
import os
import IPython
import matplotlib.pyplot as plt
_SAMPLE_DIR = "_assets"
YESNO_DATASET_PATH = os.path.join(_SAMPLE_DIR, "yes_no")
os.makedirs(YESNO_DATASET_PATH, exist_ok=True)
def plot_specgram(waveform, sample_rate, title="Spectrogram"):
waveform = waveform.numpy()
figure, ax = plt.subplots()
ax.specgram(waveform[0], Fs=sample_rate)
figure.suptitle(title)
figure.tight_layout()
Here, we show how to use the
torchaudio.datasets.YESNO
dataset.
dataset = torchaudio.datasets.YESNO(YESNO_DATASET_PATH, download=True)
0%| | 0.00/4.49M [00:00<?, ?B/s]
3%|2 | 128k/4.49M [00:00<00:08, 553kB/s]
11%|#1 | 512k/4.49M [00:00<00:02, 1.49MB/s]
42%|####1 | 1.88M/4.49M [00:00<00:00, 4.61MB/s]
100%|##########| 4.49M/4.49M [00:00<00:00, 7.39MB/s]
i = 1
waveform, sample_rate, label = dataset[i]
plot_specgram(waveform, sample_rate, title=f"Sample {i}: {label}")
IPython.display.Audio(waveform, rate=sample_rate)
![Sample 1: [0, 0, 0, 1, 0, 0, 0, 1]](../_images/sphx_glr_audio_datasets_tutorial_001.png)
/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)
i = 3
waveform, sample_rate, label = dataset[i]
plot_specgram(waveform, sample_rate, title=f"Sample {i}: {label}")
IPython.display.Audio(waveform, rate=sample_rate)
![Sample 3: [0, 0, 1, 0, 0, 0, 1, 0]](../_images/sphx_glr_audio_datasets_tutorial_002.png)
/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)
i = 5
waveform, sample_rate, label = dataset[i]
plot_specgram(waveform, sample_rate, title=f"Sample {i}: {label}")
IPython.display.Audio(waveform, rate=sample_rate)
![Sample 5: [0, 0, 1, 0, 0, 1, 1, 1]](../_images/sphx_glr_audio_datasets_tutorial_003.png)
/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)
Total running time of the script: ( 0 minutes 1.799 seconds)