A collection of useful audio datasets and transforms for PyTorch.
pip install audio-data-pytorch
Load one or multiple folders of .wav files as dataset.
from audio_data_pytorch import WAVDataset
dataset = WAVDataset(path=['my/path1', 'my/path2'])WAVDataset(
path: Union[str, Sequence[str]], # Path or list of paths from which to load files
recursive: bool = False # Recursively load files from provided paths
sample_rate: bool = False, # Specify sample rate to convert files to on read
optimized_random_crop_size: int = None, # Load small portions of files randomly
transforms: Optional[Callable] = None, # Transforms to apply to audio files
check_silence: bool = True # Discards silent samples if true
)A WebDataset extension for audio data. Assumes that the .tar file comes with pairs of .wav (or .flac) and .json data.
from audio_data_pytorch import AudioWebDataset
dataset = AudioWebDataset(
urls='mywebdataset.tar'
)
waveform, info = next(iter(dataset))
print(waveform.shape) # torch.Size([2, 480000])
print(info.keys()) # dict_keys(['text'])dataset = AudioWebDataset(
urls: Union[str, Sequence[str]],
transforms: Optional[Callable] = None, # Transforms to apply to audio files
batch_size: Optional[int] = None, # Why is batch_size here? See https://webdataset.github.io/webdataset/gettingstarted/#webdataset-and-dataloader
shuffle: int = 128, # Shuffle in groups of 128
**kwargs, # Forwarded to WebDataset class
)An unsupervised dataset for LJSpeech with voice-only data.
from audio_data_pytorch import LJSpeechDataset
dataset = LJSpeechDataset(root='./data')
dataset[0] # (1, 158621)
dataset[1] # (1, 153757)LJSpeechDataset(
root: str = "./data", # The root where the dataset will be downloaded
transforms: Optional[Callable] = None, # Transforms to apply to audio files
)Wrapper for the LibriSpeech dataset (EN only). Requires pip install datasets. Note that this dataset requires several GBs of storage.
from audio_data_pytorch import LibriSpeechDataset
dataset = LibriSpeechDataset(
root="./data",
)
dataset[0] # (1, 222336)LibriSpeechDataset(
root: str = "./data", # The root where the dataset will be downloaded
with_info: bool = False, # Whether to return info (i.e. text, sampling rate, speaker_id)
transforms: Optional[Callable] = None, # Transforms to apply to audio files
)Multilanguage wrapper for the Common Voice. Requires pip install datasets. Note that each language requires several GBs of storage, and that you have to confirm access for each distinct version you use e.g. here, to validate your Huggingface access token. You can provide a list of languages and to avoid an unbalanced dataset the values will be interleaved by downsampling the majority language to have the same number of samples as the minority language.
from audio_data_pytorch import CommonVoiceDataset
dataset = CommonVoiceDataset(
auth_token="hf_xxx",
version=1,
root="../data",
languages=['it']
)CommonVoiceDataset(
auth_token: str, # Your Huggingface access token
version: int, # Common Voice dataset version
sub_version: int = 0, # Subversion: common_voice_{version}_{sub_version}
root: str = "./data", # The root where the dataset will be downloaded
languages: Sequence[str] = ['en'], # List of languages to include in the dataset
with_info: bool = False, # Whether to return info (i.e. text, sampling rate, age, gender, accent, locale)
transforms: Optional[Callable] = None, # Transforms to apply to audio files
)A wrapper around yt-dlp that automatically downloads the audio source of Youtube videos. Requires pip install yt-dlp.
from audio_data_pytorch import YoutubeDataset
dataset = YoutubeDataset(
root='./data',
urls=[
"https://www.youtube.com/watch?v=dQw4w9WgXcQ",
"https://www.youtube.com/watch?v=BZ-_KQezKmU",
],
crop_length=10 # Crop source in 10s chunks (optional but suggested)
)
dataset[0] # (2, 480000)dataset = YoutubeDataset(
urls: Sequence[str], # The list of youtube urls
root: str = "./data", # The root where the dataset will be downloaded
crop_length: Optional[int] = None, # Crops the source into chunks of `crop_length` seconds
with_sample_rate: bool = False, # Returns sample rate as second argument
transforms: Optional[Callable] = None, # Transforms to apply to audio files
)A wrapper for the Clotho dataset extending AudioWebDataset. Requires pip install py7zr to decompress .7z archive.
from audio_data_pytorch import ClothoDataset, Crop, Stereo, Mono
dataset = ClothoDataset(
root='./data/',
preprocess_sample_rate=48000, # Added to all files during preprocessing
preprocess_transforms=nn.Sequential(Crop(48000*10), Stereo()), # Added to all files during preprocessing
transforms=Mono() # Added dynamically at iteration time
)dataset = ClothoDataset(
root: str, # Path where the dataset is saved
split: str = 'train', # Dataset split, one of: 'train', 'valid'
preprocess_sample_rate: Optional[int] = None, # Preprocesses dataset to this sample rate
preprocess_transforms: Optional[Callable] = None, # Preprocesses dataset with the provided transfomrs
reset: bool = False, # Re-compute preprocessing if `true`
**kwargs # Forwarded to `AudioWebDataset`
)Extends WAVDataset with artist and genres read from ID3 tags and returned as string arrays or optionally mapped to integers stored in a json file at metadata_mapping_path.
from audio_data_pytorch import MetaDataset
dataset = MetaDataset(
path: Union[str, Sequence[str]], # Path or list of paths from which to load files
metadata_mapping_path: Optional[str] = None, # Path where mapping from artist/genres to numbers will be saved
)
waveform, artists, genres = next(iter(dataset))
# Convert an artist ID back to a string
artist_name = dataset.mappings['artists'].invert[insert_artist_id]
# Convert a genre ID back to a string
genre_name = dataset.mappings['genres'].invert[insert_genre_id]
# If given a metadata_mapping_path, metadata is returned as an int Tensor
waveform, artist_genre_tensor = next(iter(dataset))dataset = MetaDataset(
path: Union[str, Sequence[str]], # Path or list of paths from which to load files
metadata_mapping_path: Optional[str] = None, # Path where mapping from artist/genres to numbers will be saved
max_artists: int = 4, # Max number of artists to return
max_genres: int = 4, # Max number of artists to return
**kwargs # Forwarded to `WAVDataset`
)You can use the following individual transforms, or merge them with nn.Sequential():
from audio_data_pytorch import Crop
crop = Crop(size=22050*2, start=0) # Crop 2 seconds at 22050 Hz from the start of the file
from audio_data_pytorch import RandomCrop
random_crop = RandomCrop(size=22050*2) # Crop 2 seconds at 22050 Hz from a random position
from audio_data_pytorch import Resample
resample = Resample(source=48000, target=22050), # Resamples from 48kHz to 22kHz
from audio_data_pytorch import Mono
overlap = Mono() # Overap channels by sum to get mono soruce (C, N) -> (1, N)
from audio_data_pytorch import Stereo
stereo = Stereo() # Duplicate channels (1, N) -> (2, N) or (2, N) -> (2, N)
from audio_data_pytorch import Scale
scale = Scale(scale=0.8) # Scale waveform amplitude by 0.8
from audio_data_pytorch import Loudness
loudness = Loudness(sampling_rate=22050, target=-20) # Normalize loudness to -20dB, requires `pip install pyloudnorm`Or use this wrapper to apply a subset of them in one go, API:
from audio_data_pytorch import AllTransform
transform = AllTransform(
source_rate: Optional[int] = None,
target_rate: Optional[int] = None,
crop_size: Optional[int] = None,
random_crop_size: Optional[int] = None,
loudness: Optional[int] = None,
scale: Optional[float] = None,
mono: bool = False,
stereo: bool = False,
)