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| import csv | |
| import os | |
| from pathlib import Path | |
| from typing import Tuple, Union | |
| from torch import Tensor | |
| from torch.utils.data import Dataset | |
| from torchaudio.datasets.utils import _load_waveform | |
| SAMPLE_RATE = 16000 | |
| class FluentSpeechCommands(Dataset): | |
| """*Fluent Speech Commands* :cite:`fluent` dataset | |
| Args: | |
| root (str of Path): Path to the directory where the dataset is found. | |
| subset (str, optional): subset of the dataset to use. | |
| Options: [``"train"``, ``"valid"``, ``"test"``]. | |
| (Default: ``"train"``) | |
| """ | |
| def __init__(self, root: Union[str, Path], subset: str = "train"): | |
| if subset not in ["train", "valid", "test"]: | |
| raise ValueError("`subset` must be one of ['train', 'valid', 'test']") | |
| root = os.fspath(root) | |
| self._path = os.path.join(root, "fluent_speech_commands_dataset") | |
| if not os.path.isdir(self._path): | |
| raise RuntimeError("Dataset not found.") | |
| subset_path = os.path.join(self._path, "data", f"{subset}_data.csv") | |
| with open(subset_path) as subset_csv: | |
| subset_reader = csv.reader(subset_csv) | |
| data = list(subset_reader) | |
| self.header = data[0] | |
| self.data = data[1:] | |
| def get_metadata(self, n: int) -> Tuple[str, int, str, int, str, 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 | |
| int: | |
| Speaker ID | |
| str: | |
| Transcription | |
| str: | |
| Action | |
| str: | |
| Object | |
| str: | |
| Location | |
| """ | |
| sample = self.data[n] | |
| file_name = sample[self.header.index("path")].split("/")[-1] | |
| file_name = file_name.split(".")[0] | |
| speaker_id, transcription, action, obj, location = sample[2:] | |
| file_path = os.path.join("wavs", "speakers", speaker_id, f"{file_name}.wav") | |
| return file_path, SAMPLE_RATE, file_name, speaker_id, transcription, action, obj, location | |
| def __len__(self) -> int: | |
| return len(self.data) | |
| def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, str, 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 | |
| int: | |
| Speaker ID | |
| str: | |
| Transcription | |
| str: | |
| Action | |
| str: | |
| Object | |
| str: | |
| Location | |
| """ | |
| metadata = self.get_metadata(n) | |
| waveform = _load_waveform(self._path, metadata[0], metadata[1]) | |
| return (waveform,) + metadata[1:] | |