You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
109 lines
3.4 KiB
109 lines
3.4 KiB
# Copyright (c) 2021 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 argparse
|
|
import os
|
|
from pathlib import Path
|
|
|
|
import jsonlines
|
|
import numpy as np
|
|
import paddle
|
|
import soundfile as sf
|
|
import yaml
|
|
from paddle import distributed as dist
|
|
from timer import timer
|
|
from yacs.config import CfgNode
|
|
|
|
from paddlespeech.t2s.datasets.data_table import DataTable
|
|
from paddlespeech.t2s.models.wavernn import WaveRNN
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="Synthesize with WaveRNN.")
|
|
|
|
parser.add_argument("--config", type=str, help="Vocoder config file.")
|
|
parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
|
|
parser.add_argument("--test-metadata", type=str, help="dev data.")
|
|
parser.add_argument("--output-dir", type=str, help="output dir.")
|
|
parser.add_argument(
|
|
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
with open(args.config) as f:
|
|
config = CfgNode(yaml.safe_load(f))
|
|
|
|
print("========Args========")
|
|
print(yaml.safe_dump(vars(args)))
|
|
print("========Config========")
|
|
print(config)
|
|
print(
|
|
f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}"
|
|
)
|
|
|
|
if args.ngpu == 0:
|
|
paddle.set_device("cpu")
|
|
elif args.ngpu > 0:
|
|
paddle.set_device("gpu")
|
|
else:
|
|
print("ngpu should >= 0 !")
|
|
|
|
model = WaveRNN(
|
|
hop_length=config.n_shift, sample_rate=config.fs, **config["model"])
|
|
state_dict = paddle.load(args.checkpoint)
|
|
model.set_state_dict(state_dict["main_params"])
|
|
|
|
model.eval()
|
|
|
|
with jsonlines.open(args.test_metadata, 'r') as reader:
|
|
metadata = list(reader)
|
|
test_dataset = DataTable(
|
|
metadata,
|
|
fields=['utt_id', 'feats'],
|
|
converters={
|
|
'utt_id': None,
|
|
'feats': np.load,
|
|
})
|
|
output_dir = Path(args.output_dir)
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
N = 0
|
|
T = 0
|
|
for example in test_dataset:
|
|
utt_id = example['utt_id']
|
|
mel = example['feats']
|
|
mel = paddle.to_tensor(mel) # (T, C)
|
|
with timer() as t:
|
|
with paddle.no_grad():
|
|
wav = model.generate(
|
|
c=mel,
|
|
batched=config.inference.gen_batched,
|
|
target=config.inference.target,
|
|
overlap=config.inference.overlap,
|
|
mu_law=config.mu_law,
|
|
gen_display=True)
|
|
wav = wav.numpy()
|
|
N += wav.size
|
|
T += t.elapse
|
|
speed = wav.size / t.elapse
|
|
rtf = config.fs / speed
|
|
print(
|
|
f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
|
|
)
|
|
sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=config.fs)
|
|
print(f"generation speed: {N / T}Hz, RTF: {config.fs / (N / T) }")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|