# 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 numpy as np
import paddle
import soundfile as sf
from timer import timer
from paddlespeech.t2s.exps.syn_utils import get_sess
from paddlespeech.t2s.exps.syn_utils import get_test_dataset
from paddlespeech.t2s.utils import str2bool
[docs]def ort_predict(args):
# construct dataset for evaluation
with jsonlines.open(args.test_metadata, 'r') as reader:
test_metadata = list(reader)
am_name = args.am[:args.am.rindex('_')]
am_dataset = args.am[args.am.rindex('_') + 1:]
test_dataset = get_test_dataset(test_metadata=test_metadata, am=args.am)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
fs = 24000 if am_dataset != 'ljspeech' else 22050
# am
am_sess = get_sess(
model_path=str(Path(args.inference_dir) / (args.am + '.onnx')),
device=args.device,
cpu_threads=args.cpu_threads,
use_trt=args.use_trt)
# vocoder
voc_sess = get_sess(
model_path=str(Path(args.inference_dir) / (args.voc + '.onnx')),
device=args.device,
cpu_threads=args.cpu_threads,
use_trt=args.use_trt)
# am warmup
for T in [27, 38, 54]:
am_input_feed = {}
if am_name == 'fastspeech2':
phone_ids = np.random.randint(1, 266, size=(T, ))
am_input_feed.update({'text': phone_ids})
elif am_name == 'speedyspeech':
phone_ids = np.random.randint(1, 92, size=(T, ))
tone_ids = np.random.randint(1, 5, size=(T, ))
am_input_feed.update({'phones': phone_ids, 'tones': tone_ids})
am_sess.run(None, input_feed=am_input_feed)
# voc warmup
for T in [227, 308, 544]:
data = np.random.rand(T, 80).astype("float32")
voc_sess.run(None, {"logmel": data})
print("warm up done!")
N = 0
T = 0
am_input_feed = {}
for example in test_dataset:
utt_id = example['utt_id']
if am_name == 'fastspeech2':
phone_ids = example["text"]
am_input_feed.update({'text': phone_ids})
elif am_name == 'speedyspeech':
phone_ids = example["phones"]
tone_ids = example["tones"]
am_input_feed.update({'phones': phone_ids, 'tones': tone_ids})
with timer() as t:
mel = am_sess.run(output_names=None, input_feed=am_input_feed)
mel = mel[0]
wav = voc_sess.run(output_names=None, input_feed={'logmel': mel})
N += len(wav[0])
T += t.elapse
speed = len(wav[0]) / t.elapse
rtf = fs / speed
sf.write(
str(output_dir / (utt_id + ".wav")),
np.array(wav)[0],
samplerate=fs)
print(
f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
)
print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
[docs]def parse_args():
parser = argparse.ArgumentParser(description="Infernce with onnxruntime.")
# acoustic model
parser.add_argument(
'--am',
type=str,
default='fastspeech2_csmsc',
choices=['fastspeech2_csmsc', 'speedyspeech_csmsc'],
help='Choose acoustic model type of tts task.')
# voc
parser.add_argument(
'--voc',
type=str,
default='hifigan_csmsc',
choices=['hifigan_csmsc', 'mb_melgan_csmsc', 'pwgan_csmsc'],
help='Choose vocoder type of tts task.')
# other
parser.add_argument(
"--inference_dir", type=str, help="dir to save inference models")
parser.add_argument("--test_metadata", type=str, help="test metadata.")
parser.add_argument("--output_dir", type=str, help="output dir")
# inference
parser.add_argument(
"--use_trt",
type=str2bool,
default=False,
help="Whether to use inference engin TensorRT.", )
parser.add_argument(
"--device",
default="gpu",
choices=["gpu", "cpu"],
help="Device selected for inference.", )
parser.add_argument('--cpu_threads', type=int, default=1)
args, _ = parser.parse_known_args()
return args
[docs]def main():
args = parse_args()
paddle.set_device(args.device)
ort_predict(args)
if __name__ == "__main__":
main()