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import argparse |
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import os |
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from typing import Tuple |
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from scipy.io import wavfile |
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from torchaudio.datasets import LIBRITTS |
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from tqdm import tqdm |
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def load_libritts_item( |
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fileid: str, |
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path: str, |
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ext_audio: str, |
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ext_original_txt: str, |
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ext_normalized_txt: str, |
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) -> Tuple[int, int, str, str, int, int, str]: |
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speaker_id, chapter_id, segment_id, utterance_id = fileid.split("_") |
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utterance_id = fileid |
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normalized_text = utterance_id + ext_normalized_txt |
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normalized_text = os.path.join(path, speaker_id, chapter_id, normalized_text) |
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original_text = utterance_id + ext_original_txt |
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original_text = os.path.join(path, speaker_id, chapter_id, original_text) |
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file_audio = utterance_id + ext_audio |
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file_audio = os.path.join(path, speaker_id, chapter_id, file_audio) |
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sample_rate, wav = wavfile.read(file_audio) |
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n_frames = wav.shape[0] |
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with open(normalized_text, "r") as ft: |
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normalized_text = ft.readline() |
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return ( |
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n_frames, |
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sample_rate, |
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None, |
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normalized_text, |
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int(speaker_id), |
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int(chapter_id), |
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utterance_id, |
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) |
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class LIBRITTS_16K(LIBRITTS): |
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def __getitem__(self, n: int) -> Tuple[int, int, str, str, int, int, str]: |
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"""Load the n-th sample from the dataset. |
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Args: |
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n (int): The index of the sample to be loaded |
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Returns: |
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(Tensor, int, str, str, str, int, int, str): |
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``(waveform_length, sample_rate, original_text, normalized_text, speaker_id, chapter_id, utterance_id)`` |
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""" |
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fileid = self._walker[n] |
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return load_libritts_item( |
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fileid, |
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self._path, |
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self._ext_audio, |
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self._ext_original_txt, |
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self._ext_normalized_txt, |
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) |
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def get_parser(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"root", metavar="DIR", help="root directory containing wav files to index" |
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) |
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parser.add_argument( |
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"--dest", default=".", type=str, metavar="DIR", help="output directory" |
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) |
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parser.add_argument( |
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"--split", required=True, type=str, help="dataset splits" |
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) |
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parser.add_argument( |
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"--wav-root", default=None, type=str, metavar="DIR", help="saved waveform root directory for tsv" |
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) |
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parser.add_argument( |
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"--spkemb-npy-dir", required=True, type=str, help="speaker embedding directory" |
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) |
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return parser |
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def main(args): |
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dest_dir = args.dest |
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wav_root = args.wav_root |
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if not os.path.exists(dest_dir): |
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os.makedirs(dest_dir) |
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dataset = LIBRITTS_16K(os.path.dirname(args.root), url=args.split, folder_in_archive=os.path.basename(args.root)) |
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tsv_f = open(os.path.join(dest_dir, f"{args.split}.tsv"), "w") |
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txt_f = open(os.path.join(dest_dir, f"{args.split}.txt"), "w") |
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print(wav_root, file=tsv_f) |
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for n_frames, sr, ori_text, norm_text, spk_id, chap_id, utt_id in tqdm(dataset, desc="tsv/txt/wav"): |
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assert sr == 16000, f"sampling rate {sr} != 16000" |
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utt_file = os.path.join(args.split, f"{spk_id}", f"{chap_id}", f"{utt_id}.wav") |
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spk_file = os.path.join(args.spkemb_npy_dir, f"{spk_id}-{chap_id}-{utt_id}.npy") |
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assert os.path.exists(os.path.join(wav_root, utt_file)) |
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assert os.path.exists(os.path.join(wav_root, spk_file)) |
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print(f"{utt_file}\t{n_frames}\t{spk_file}", file=tsv_f) |
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print(norm_text, file=txt_f) |
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tsv_f.close() |
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txt_f.close() |
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if __name__ == "__main__": |
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parser = get_parser() |
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args = parser.parse_args() |
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main(args) |
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