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PaddleSpeech/paddlespeech/t2s/exps/vits/synthesize.py

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# Copyright (c) 2022 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
from pathlib import Path
import jsonlines
import paddle
import soundfile as sf
import yaml
from timer import timer
from yacs.config import CfgNode
from paddlespeech.t2s.datasets.data_table import DataTable
from paddlespeech.t2s.models.vits import VITS
def evaluate(args):
# construct dataset for evaluation
with jsonlines.open(args.test_metadata, 'r') as reader:
test_metadata = list(reader)
# Init body.
with open(args.config) as f:
config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(config)
fields = ["utt_id", "text"]
test_dataset = DataTable(data=test_metadata, fields=fields)
with open(args.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
print("vocab_size:", vocab_size)
odim = config.n_fft // 2 + 1
vits = VITS(idim=vocab_size, odim=odim, **config["model"])
vits.set_state_dict(paddle.load(args.ckpt)["main_params"])
vits.eval()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
N = 0
T = 0
for datum in test_dataset:
utt_id = datum["utt_id"]
phone_ids = paddle.to_tensor(datum["text"])
with timer() as t:
with paddle.no_grad():
out = vits.inference(text=phone_ids)
wav = out["wav"]
wav = wav.numpy()
N += wav.size
T += t.elapse
speed = wav.size / t.elapse
rtf = config.fs / speed
print(
f"{utt_id}, wave: {wav.size}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
)
sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=config.fs)
print(f"{utt_id} done!")
print(f"generation speed: {N / T}Hz, RTF: {config.fs / (N / T) }")
def parse_args():
# parse args and config
parser = argparse.ArgumentParser(description="Synthesize with VITS")
# model
parser.add_argument(
'--config', type=str, default=None, help='Config of VITS.')
parser.add_argument(
'--ckpt', type=str, default=None, help='Checkpoint file of VITS.')
parser.add_argument(
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
# other
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--test_metadata", type=str, help="test metadata.")
parser.add_argument("--output_dir", type=str, help="output dir.")
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
evaluate(args)
if __name__ == "__main__":
main()