109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
import csv
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import os
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from pathlib import Path
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from typing import Tuple, Union
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from torch import Tensor
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from torch.utils.data import Dataset
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from torchaudio.datasets.utils import _load_waveform
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SAMPLE_RATE = 16000
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class FluentSpeechCommands(Dataset):
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"""*Fluent Speech Commands* :cite:`fluent` dataset
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Args:
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root (str of Path): Path to the directory where the dataset is found.
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subset (str, optional): subset of the dataset to use.
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Options: [``"train"``, ``"valid"``, ``"test"``].
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(Default: ``"train"``)
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"""
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def __init__(self, root: Union[str, Path], subset: str = "train"):
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if subset not in ["train", "valid", "test"]:
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raise ValueError("`subset` must be one of ['train', 'valid', 'test']")
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root = os.fspath(root)
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self._path = os.path.join(root, "fluent_speech_commands_dataset")
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if not os.path.isdir(self._path):
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raise RuntimeError("Dataset not found.")
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subset_path = os.path.join(self._path, "data", f"{subset}_data.csv")
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with open(subset_path) as subset_csv:
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subset_reader = csv.reader(subset_csv)
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data = list(subset_reader)
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self.header = data[0]
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self.data = data[1:]
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def get_metadata(self, n: int) -> Tuple[str, int, str, int, str, str, str, str]:
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"""Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
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but otherwise returns the same fields as :py:func:`__getitem__`.
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Args:
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n (int): The index of the sample to be loaded
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Returns:
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Tuple of the following items;
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str:
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Path to audio
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int:
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Sample rate
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str:
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File name
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int:
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Speaker ID
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str:
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Transcription
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str:
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Action
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str:
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Object
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str:
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Location
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"""
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sample = self.data[n]
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file_name = sample[self.header.index("path")].split("/")[-1]
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file_name = file_name.split(".")[0]
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speaker_id, transcription, action, obj, location = sample[2:]
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file_path = os.path.join("wavs", "speakers", speaker_id, f"{file_name}.wav")
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return file_path, SAMPLE_RATE, file_name, speaker_id, transcription, action, obj, location
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def __len__(self) -> int:
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return len(self.data)
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def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, str, str, str, str]:
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"""Load the n-th sample from the dataset.
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Args:
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n (int): The index of the sample to be loaded
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Returns:
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Tuple of the following items;
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Tensor:
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Waveform
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int:
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Sample rate
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str:
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File name
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int:
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Speaker ID
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str:
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Transcription
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str:
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Action
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str:
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Object
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str:
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Location
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"""
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metadata = self.get_metadata(n)
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waveform = _load_waveform(self._path, metadata[0], metadata[1])
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return (waveform,) + metadata[1:]
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