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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) | |