-
Notifications
You must be signed in to change notification settings - Fork 23
Expand file tree
/
Copy pathconvert.py
More file actions
86 lines (67 loc) · 3.04 KB
/
convert.py
File metadata and controls
86 lines (67 loc) · 3.04 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
import hydra
import hydra.utils as utils
import json
from pathlib import Path
import torch
import numpy as np
import librosa
from tqdm import tqdm
import pyloudnorm
from preprocess import preemphasis
from model import Encoder, Vocoder
@hydra.main(config_path="config/convert.yaml")
def convert(cfg):
dataset_path = Path(utils.to_absolute_path("datasets")) / cfg.dataset.path
with open(dataset_path / "speakers.json") as file:
speakers = sorted(json.load(file))
synthesis_list_path = Path(utils.to_absolute_path(cfg.synthesis_list))
with open(synthesis_list_path) as file:
synthesis_list = json.load(file)
in_dir = Path(utils.to_absolute_path(cfg.in_dir))
out_dir = Path(utils.to_absolute_path(cfg.out_dir))
out_dir.mkdir(exist_ok=True, parents=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = Encoder(**cfg.model.encoder)
vocoder = Vocoder(**cfg.model.vocoder)
encoder.to(device)
vocoder.to(device)
print("Load checkpoint from: {}:".format(cfg.cpc_checkpoint))
checkpoint_path = utils.to_absolute_path(cfg.cpc_checkpoint)
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
encoder.load_state_dict(checkpoint["encoder"])
print("Load checkpoint from: {}:".format(cfg.vocoder_checkpoint))
checkpoint_path = utils.to_absolute_path(cfg.vocoder_checkpoint)
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
vocoder.load_state_dict(checkpoint["vocoder"])
encoder.eval()
vocoder.eval()
meter = pyloudnorm.Meter(cfg.preprocessing.sr)
for wav_path, speaker_id, out_filename in tqdm(synthesis_list):
wav_path = in_dir / wav_path
wav, _ = librosa.load(
wav_path.with_suffix(".wav"),
sr=cfg.preprocessing.sr)
ref_loudness = meter.integrated_loudness(wav)
wav = wav / np.abs(wav).max() * 0.999
mel = librosa.feature.melspectrogram(
preemphasis(wav, cfg.preprocessing.preemph),
sr=cfg.preprocessing.sr,
n_fft=cfg.preprocessing.n_fft,
n_mels=cfg.preprocessing.n_mels,
hop_length=cfg.preprocessing.hop_length,
win_length=cfg.preprocessing.win_length,
fmin=cfg.preprocessing.fmin,
power=1)
logmel = librosa.amplitude_to_db(mel, top_db=cfg.preprocessing.top_db)
logmel = logmel / cfg.preprocessing.top_db + 1
mel = torch.FloatTensor(logmel).unsqueeze(0).to(device)
speaker = torch.LongTensor([speakers.index(speaker_id)]).to(device)
with torch.no_grad():
_, _, indices = encoder.encode(mel)
output = vocoder.generate(indices, speaker)
output_loudness = meter.integrated_loudness(output)
output = pyloudnorm.normalize.loudness(output, output_loudness, ref_loudness)
path = out_dir / out_filename
librosa.output.write_wav(path.with_suffix(".wav"), output.astype(np.float32), sr=cfg.preprocessing.sr)
if __name__ == "__main__":
convert()