This repository has been archived by the owner on Sep 11, 2022. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 83
/
synthesize.py
83 lines (71 loc) · 2.75 KB
/
synthesize.py
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
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import argparse
from pathlib import Path
import numpy as np
import soundfile as sf
import paddle
from parakeet.models.waveflow import ConditionalWaveFlow
from parakeet.utils import layer_tools
from config import get_cfg_defaults
def main(config, args):
paddle.set_device(args.device)
model = ConditionalWaveFlow.from_pretrained(config, args.checkpoint_path)
layer_tools.recursively_remove_weight_norm(model)
model.eval()
mel_dir = Path(args.input).expanduser()
output_dir = Path(args.output).expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
for file_path in mel_dir.glob("*.npy"):
mel = np.load(str(file_path))
with paddle.amp.auto_cast():
audio = model.predict(mel)
audio_path = output_dir / (os.path.splitext(file_path.name)[0] + ".wav")
sf.write(audio_path, audio, config.data.sample_rate)
print("[synthesize] {} -> {}".format(file_path, audio_path))
if __name__ == "__main__":
config = get_cfg_defaults()
parser = argparse.ArgumentParser(
description="generate mel spectrogram with TransformerTTS.")
parser.add_argument(
"--config",
type=str,
metavar="FILE",
help="extra config to overwrite the default config")
parser.add_argument(
"--checkpoint_path", type=str, help="path of the checkpoint to load.")
parser.add_argument(
"--input",
type=str,
help="path of directory containing mel spectrogram (in .npy format)")
parser.add_argument("--output", type=str, help="path to save outputs")
parser.add_argument(
"--device", type=str, default="cpu", help="device type to use.")
parser.add_argument(
"--opts",
nargs=argparse.REMAINDER,
help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
)
parser.add_argument(
"-v", "--verbose", action="store_true", help="print msg")
args = parser.parse_args()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
print(args)
main(config, args)