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- import torch
- import numpy as np
- import shutil
- import os
-
- from utils import load_data, get_Tacotron2, get_WaveGlow
- from utils import process_text, load_data
- from data import ljspeech
- import hparams as hp
- import waveglow
- import audio as Audio
-
-
- def preprocess_ljspeech(filename):
- in_dir = filename
- out_dir = hp.mel_ground_truth
- if not os.path.exists(out_dir):
- os.makedirs(out_dir, exist_ok=True)
- metadata = ljspeech.build_from_path(in_dir, out_dir)
- write_metadata(metadata, out_dir)
-
- shutil.move(os.path.join(hp.mel_ground_truth, "train.txt"),
- os.path.join("data", "train.txt"))
-
-
- def write_metadata(metadata, out_dir):
- with open(os.path.join(out_dir, 'train.txt'), 'w', encoding='utf-8') as f:
- for m in metadata:
- f.write(m + '\n')
-
-
- def main():
- path = os.path.join("data", "LJSpeech-1.1")
- preprocess_ljspeech(path)
-
- text_path = os.path.join("data", "train.txt")
- texts = process_text(text_path)
-
- if not os.path.exists(hp.alignment_path):
- os.mkdir(hp.alignment_path)
- else:
- return
-
- tacotron2 = get_Tacotron2()
-
- num = 0
- for ind, text in enumerate(texts[num:]):
- print(ind)
-
- character = text[0:len(text)-1]
- mel_gt_name = os.path.join(
- hp.mel_ground_truth, "ljspeech-mel-%05d.npy" % (ind+num+1))
- mel_gt_target = np.load(mel_gt_name)
- _, _, D = load_data(character, mel_gt_target, tacotron2)
-
- np.save(os.path.join(hp.alignment_path, str(
- ind+num) + ".npy"), D, allow_pickle=False)
-
-
- if __name__ == "__main__":
- main()
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