# 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 numpy as np
import paddle
import soundfile as sf
from timer import timer
from paddlespeech.t2s.exps.syn_utils import get_frontend
from paddlespeech.t2s.exps.syn_utils import get_sentences
from paddlespeech.t2s.exps.syn_utils import get_sess
from paddlespeech.t2s.exps.syn_utils import run_frontend
from paddlespeech.t2s.utils import str2bool
def ort_predict(args):
# frontend
frontend = get_frontend(
lang=args.lang,
phones_dict=args.phones_dict,
tones_dict=args.tones_dict)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
sentences = get_sentences(text_file=args.text, lang=args.lang)
am_name = args.am[:args.am.rindex('_')]
am_dataset = args.am[args.am.rindex('_') + 1:]
fs = 24000 if am_dataset != 'ljspeech' else 22050
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)
merge_sentences = True
# frontend warmup
# Loading model cost 0.5+ seconds
if args.lang == 'zh':
frontend.get_input_ids(
"你好，欢迎使用飞桨框架进行深度学习研究！", merge_sentences=merge_sentences)
else:
frontend.get_input_ids(
"hello, thank you, thank you very much",
merge_sentences=merge_sentences)
# am warmup
spk_id = [args.spk_id]
for T in [27, 38, 54]:
am_input_feed = {}
if am_name == 'fastspeech2':
if args.lang == 'en':
phone_ids = np.random.randint(1, 78, size=(T, ))
else:
phone_ids = np.random.randint(1, 266, size=(T, ))
am_input_feed.update({'text': phone_ids})
if am_dataset in {"aishell3", "vctk", "mix"}:
am_input_feed.update({'spk_id': spk_id})
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, input_feed={"logmel": data})
print("warm up done!")
N = 0
T = 0
merge_sentences = False
get_tone_ids = False
if am_name == 'speedyspeech':
get_tone_ids = True
am_input_feed = {}
for utt_id, sentence in sentences:
with timer() as t:
frontend_dict = run_frontend(
frontend=frontend,
text=sentence,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=args.lang)
phone_ids = frontend_dict['phone_ids']
flags = 0
for i in range(len(phone_ids)):
part_phone_ids = phone_ids[i].numpy()
if am_name == 'fastspeech2':
am_input_feed.update({'text': part_phone_ids})
if am_dataset in {"aishell3", "vctk", "mix"}:
am_input_feed.update({'spk_id': spk_id})
elif am_name == 'speedyspeech':
part_tone_ids = frontend_dict['tone_ids'][i].numpy()
am_input_feed.update({
'phones': part_phone_ids,
'tones': part_tone_ids
})
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})
wav = wav[0]
if flags == 0:
wav_all = wav
flags = 1
else:
wav_all = np.concatenate([wav_all, wav])
wav = wav_all
N += len(wav)
T += t.elapse
speed = len(wav) / t.elapse
rtf = fs / speed
sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=fs)
print(
f"{utt_id}, mel: {mel.shape}, wave: {len(wav)}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
)
print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
def parse_args():
parser = argparse.ArgumentParser(description="Infernce with onnxruntime.")
# acoustic model
parser.add_argument(
'--am',
type=str,
default='fastspeech2_csmsc',
choices=[
'fastspeech2_csmsc',
'fastspeech2_aishell3',
'fastspeech2_ljspeech',
'fastspeech2_vctk',
'speedyspeech_csmsc',
'fastspeech2_mix',
],
help='Choose acoustic model type of tts task.')
parser.add_argument(
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
parser.add_argument(
"--tones_dict", type=str, default=None, help="tone vocabulary file.")
parser.add_argument(
'--spk_id',
type=int,
default=0,
help='spk id for multi speaker acoustic model')
# voc
parser.add_argument(
'--voc',
type=str,
default='hifigan_csmsc',
choices=[
'pwgan_csmsc',
'pwgan_aishell3',
'pwgan_ljspeech',
'pwgan_vctk',
'hifigan_csmsc',
'hifigan_aishell3',
'hifigan_ljspeech',
'hifigan_vctk',
'mb_melgan_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(
"--text",
type=str,
help="text to synthesize, a 'utt_id sentence' pair per line")
parser.add_argument("--output_dir", type=str, help="output dir")
parser.add_argument(
'--lang',
type=str,
default='zh',
help='Choose model language. zh or en')
# 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
def main():
args = parse_args()
paddle.set_device(args.device)
ort_predict(args)
if __name__ == "__main__":
main()