Source code for torchaudio.datasets.vctk

import os
from typing import Tuple

import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from import Dataset
from torchaudio.datasets.utils import _extract_zip

URL = ""
    "": "f96258be9fdc2cbff6559541aae7ea4f59df3fcaf5cf963aae5ca647357e359c"  # noqa: E501

SampleType = Tuple[Tensor, int, str, str, str]

[docs]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: ``""``) 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: """ 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, "") 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)
[docs] 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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