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Source code for torchaudio.datasets.snips

import os
from pathlib import Path
from typing import List, Optional, Tuple, Union

import torch
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform


_SAMPLE_RATE = 16000
_SPEAKERS = [
    "Aditi",
    "Amy",
    "Brian",
    "Emma",
    "Geraint",
    "Ivy",
    "Joanna",
    "Joey",
    "Justin",
    "Kendra",
    "Kimberly",
    "Matthew",
    "Nicole",
    "Raveena",
    "Russell",
    "Salli",
]


def _load_labels(file: Path, subset: str):
    """Load transcirpt, iob, and intent labels for all utterances.

    Args:
        file (Path): The path to the label file.
        subset (str): Subset of the dataset to use. Options: [``"train"``, ``"valid"``, ``"test"``].

    Returns:
        Dictionary of labels, where the key is the filename of the audio,
            and the label is a Tuple of transcript, Inside–outside–beginning (IOB) label, and intention label.
    """
    labels = {}
    with open(file, "r") as f:
        for line in f:
            line = line.strip().split(" ")
            index = line[0]
            trans, iob_intent = " ".join(line[1:]).split("\t")
            trans = " ".join(trans.split(" ")[1:-1])
            iob = " ".join(iob_intent.split(" ")[1:-1])
            intent = iob_intent.split(" ")[-1]
            if subset in index:
                labels[index] = (trans, iob, intent)
    return labels


[docs]class Snips(Dataset): """*Snips* :cite:`coucke2018snips` dataset. Args: 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"``) """ _trans_file = "all.iob.snips.txt" def __init__( self, root: Union[str, Path], subset: str, speakers: Optional[List[str]] = None, audio_format: str = "mp3", ) -> None: if subset not in ["train", "valid", "test"]: raise ValueError('`subset` must be one of ["train", "valid", "test"].') if audio_format not in ["mp3", "wav"]: raise ValueError('`audio_format` must be one of ["mp3", "wav].') root = Path(root) self._path = root / "SNIPS" self.audio_path = self._path / subset if speakers is None: speakers = _SPEAKERS if not os.path.isdir(self._path): raise RuntimeError("Dataset not found.") self.audio_paths = self.audio_path.glob(f"*.{audio_format}") self.data = [] for audio_path in sorted(self.audio_paths): audio_name = str(audio_path.name) speaker = audio_name.split("-")[0] if speaker in speakers: self.data.append(audio_path) transcript_path = self._path / self._trans_file self.labels = _load_labels(transcript_path, subset)
[docs] def get_metadata(self, n: int) -> Tuple[str, int, str, str, str]: """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform, but otherwise returns the same fields as :py:func:`__getitem__`. Args: n (int): The index of the sample to be loaded. Returns: Tuple of the following items: 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. """ audio_path = self.data[n] relpath = os.path.relpath(audio_path, self._path) file_name = audio_path.with_suffix("").name transcript, iob, intent = self.labels[file_name] return relpath, _SAMPLE_RATE, file_name, transcript, iob, intent
[docs] def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, str, str, str]: """Load the n-th sample from the dataset. Args: n (int): The index of the sample to be loaded Returns: Tuple of the following items: 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. """ metadata = self.get_metadata(n) waveform = _load_waveform(self._path, metadata[0], metadata[1]) return (waveform,) + metadata[1:]
def __len__(self) -> int: return len(self.data)

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