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