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