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import os
from typing import Tuple
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio._internal import download_url_to_file
from torchaudio.datasets.utils import _extract_zip
URL = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"
_CHECKSUMS = {
"https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip": "f96258be9fdc2cbff6559541aae7ea4f59df3fcaf5cf963aae5ca647357e359c" # noqa: E501
}
SampleType = Tuple[Tensor, int, str, str, str]
class VCTK_092(Dataset):
"""*VCTK 0.92* :cite:`yamagishi2019vctk` dataset
Args:
root (str): Root directory where the dataset's top level directory is found.
mic_id (str, optional): Microphone ID. Either ``"mic1"`` or ``"mic2"``. (default: ``"mic2"``)
download (bool, optional):
Whether to download the dataset if it is not found at root path. (default: ``False``).
url (str, optional): The URL to download the dataset from.
(default: ``"https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"``)
audio_ext (str, optional): Custom audio extension if dataset is converted to non-default audio format.
Note:
* All the speeches from speaker ``p315`` will be skipped due to the lack of the corresponding text files.
* All the speeches from ``p280`` will be skipped for ``mic_id="mic2"`` due to the lack of the audio files.
* Some of the speeches from speaker ``p362`` will be skipped due to the lack of the audio files.
* See Also: https://datashare.is.ed.ac.uk/handle/10283/3443
"""
def __init__(
self,
root: str,
mic_id: str = "mic2",
download: bool = False,
url: str = URL,
audio_ext=".flac",
):
if mic_id not in ["mic1", "mic2"]:
raise RuntimeError(f'`mic_id` has to be either "mic1" or "mic2". Found: {mic_id}')
archive = os.path.join(root, "VCTK-Corpus-0.92.zip")
self._path = os.path.join(root, "VCTK-Corpus-0.92")
self._txt_dir = os.path.join(self._path, "txt")
self._audio_dir = os.path.join(self._path, "wav48_silence_trimmed")
self._mic_id = mic_id
self._audio_ext = audio_ext
if download:
if not os.path.isdir(self._path):
if not os.path.isfile(archive):
checksum = _CHECKSUMS.get(url, None)
download_url_to_file(url, archive, hash_prefix=checksum)
_extract_zip(archive, self._path)
if not os.path.isdir(self._path):
raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
# Extracting speaker IDs from the folder structure
self._speaker_ids = sorted(os.listdir(self._txt_dir))
self._sample_ids = []
"""
Due to some insufficient data complexity in the 0.92 version of this dataset,
we start traversing the audio folder structure in accordance with the text folder.
As some of the audio files are missing of either ``mic_1`` or ``mic_2`` but the
text is present for the same, we first check for the existence of the audio file
before adding it to the ``sample_ids`` list.
Once the ``audio_ids`` are loaded into memory we can quickly access the list for
different parameters required by the user.
"""
for speaker_id in self._speaker_ids:
if speaker_id == "p280" and mic_id == "mic2":
continue
utterance_dir = os.path.join(self._txt_dir, speaker_id)
for utterance_file in sorted(f for f in os.listdir(utterance_dir) if f.endswith(".txt")):
utterance_id = os.path.splitext(utterance_file)[0]
audio_path_mic = os.path.join(
self._audio_dir,
speaker_id,
f"{utterance_id}_{mic_id}{self._audio_ext}",
)
if speaker_id == "p362" and not os.path.isfile(audio_path_mic):
continue
self._sample_ids.append(utterance_id.split("_"))
def _load_text(self, file_path) -> str:
with open(file_path) as file_path:
return file_path.readlines()[0]
def _load_audio(self, file_path) -> Tuple[Tensor, int]:
return torchaudio.load(file_path)
def _load_sample(self, speaker_id: str, utterance_id: str, mic_id: str) -> SampleType:
transcript_path = os.path.join(self._txt_dir, speaker_id, f"{speaker_id}_{utterance_id}.txt")
audio_path = os.path.join(
self._audio_dir,
speaker_id,
f"{speaker_id}_{utterance_id}_{mic_id}{self._audio_ext}",
)
# Reading text
transcript = self._load_text(transcript_path)
# Reading FLAC
waveform, sample_rate = self._load_audio(audio_path)
return (waveform, sample_rate, transcript, speaker_id, utterance_id)
def __getitem__(self, n: int) -> SampleType:
"""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:
Transcript
str:
Speaker ID
std:
Utterance ID
"""
speaker_id, utterance_id = self._sample_ids[n]
return self._load_sample(speaker_id, utterance_id, self._mic_id)
def __len__(self) -> int:
return len(self._sample_ids)
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