比较与torchaudio.datasets.GTZAN的差异

class torchaudio.datasets.GTZAN(
    root: str,
    url: str = 'http://opihi.cs.uvic.ca/sound/genres.tar.gz',
    folder_in_archive: str = 'genres',
    download: bool = False,
    subset: str = None)

class mindspore.dataset.GTZANDataset(
    dataset_dir,
    usage=None,
    num_samples=None,
    num_parallel_workers=None,
    shuffle=None,
    sampler=None,
    num_shards=None,
    shard_id=None,
    cache=None)

# PyTorch
import torchaudio.datasets as datasets
from torch.utils.data import DataLoader

root = "/path/to/dataset_directory/"
dataset = datasets.GTZAN(root, url='http://opihi.cs.uvic.ca/sound/genres.tar.gz')
dataloader = DataLoader(dataset)

# MindSpore
import mindspore.dataset as ds

# Download GTZAN dataset files, unzip into the following structure
# .
# └── /path/to/dataset_directory/
#      ├── blues
#      │    ├──blues.00000.wav
#      │    ├──blues.00001.wav
#      │    ├──blues.00002.wav
#      │    ├──...
#      ├── disco
#      │    ├──disco.00000.wav
#      │    ├──disco.00001.wav
#      │    ├──disco.00002.wav
#      │    └──...
#      └──...
root = "/path/to/dataset_directory/"
ms_dataloader = ds.GTZANDataset(root, usage='all')