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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:]
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