音频数据集

作者： Moto Hira

torchaudio提供对公共、可公开访问的便捷访问 数据。请参阅官方文档以获取 可用的数据集。

import torch
import torchaudio

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

2.4.0
2.4.0

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()

在这里，我们将展示如何使用数据集。

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, 563kB/s]
 11%|#1        | 512k/4.49M [00:00<00:02, 1.52MB/s]
 42%|####1     | 1.88M/4.49M [00:00<00:00, 4.70MB/s]
100%|##########| 4.49M/4.49M [00:00<00:00, 7.54MB/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)

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)

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)