Source code for torchaudio.datasets.ljspeech
import os
import csv
from typing import List, Tuple
import torchaudio
from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader
from torch import Tensor
from torch.utils.data import Dataset
URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"
FOLDER_IN_ARCHIVE = "wavs"
_CHECKSUMS = {
"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2":
"be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5"
}
def load_ljspeech_item(line: List[str], path: str, ext_audio: str) -> Tuple[Tensor, int, str, str]:
assert len(line) == 3
fileid, transcript, normalized_transcript = line
fileid_audio = fileid + ext_audio
fileid_audio = os.path.join(path, fileid_audio)
# Load audio
waveform, sample_rate = torchaudio.load(fileid_audio)
return (
waveform,
sample_rate,
transcript,
normalized_transcript,
)
[docs]class LJSPEECH(Dataset):
"""Create a Dataset for LJSpeech-1.1.
Args:
root (str): Path to the directory where the dataset is found or downloaded.
url (str, optional): The URL to download the dataset from.
(default: ``"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"``)
folder_in_archive (str, optional):
The top-level directory of the dataset. (default: ``"wavs"``)
download (bool, optional):
Whether to download the dataset if it is not found at root path. (default: ``False``).
"""
_ext_audio = ".wav"
_ext_archive = '.tar.bz2'
def __init__(self,
root: str,
url: str = URL,
folder_in_archive: str = FOLDER_IN_ARCHIVE,
download: bool = False) -> None:
basename = os.path.basename(url)
archive = os.path.join(root, basename)
basename = basename.split(self._ext_archive)[0]
folder_in_archive = os.path.join(basename, folder_in_archive)
self._path = os.path.join(root, folder_in_archive)
self._metadata_path = os.path.join(root, basename, 'metadata.csv')
if download:
if not os.path.isdir(self._path):
if not os.path.isfile(archive):
checksum = _CHECKSUMS.get(url, None)
download_url(url, root, hash_value=checksum)
extract_archive(archive)
with open(self._metadata_path, "r", newline='') as metadata:
walker = unicode_csv_reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE)
self._walker = list(walker)
[docs] def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
tuple: ``(waveform, sample_rate, transcript, normalized_transcript)``
"""
line = self._walker[n]
return load_ljspeech_item(line, self._path, self._ext_audio)
def __len__(self) -> int:
return len(self._walker)