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Snips

class torchaudio.datasets.Snips(root: Union[str, Path], subset: str, speakers: Optional[List[str]] = None, audio_format: str = 'mp3')[source]

Snips [Coucke et al., 2018] dataset.

Parameters:
  • root (str or Path) – Root directory where the dataset’s top level directory is found.

  • subset (str) – Subset of the dataset to use. Options: ["train", "valid", "test"].

  • speakers (List[str] or None, optional) – The speaker list to include in the dataset. If None, include all speakers in the subset. (Default: None)

  • audio_format (str, optional) – The extension of the audios. Options: ["mp3", "wav"]. (Default: "mp3")

__getitem__

Snips.__getitem__(n: int) Tuple[Tensor, int, str, str, str][source]

Load the n-th sample from the dataset.

Parameters:

n (int) – The index of the sample to be loaded

Returns:

Tensor:

Waveform

int:

Sample rate

str:

File name

str:

Transcription of audio

str:

Inside–outside–beginning (IOB) label of transcription

str:

Intention label of the audio.

Return type:

Tuple of the following items

get_metadata

Snips.get_metadata(n: int) Tuple[str, int, str, str, str][source]

Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform, but otherwise returns the same fields as __getitem__().

Parameters:

n (int) – The index of the sample to be loaded.

Returns:

str:

Path to audio

int:

Sample rate

str:

File name

str:

Transcription of audio

str:

Inside–outside–beginning (IOB) label of transcription

str:

Intention label of the audio.

Return type:

Tuple of the following items

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