# 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 denorm
from paddlespeech.t2s.exps.syn_utils import get_chunks
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
[docs]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
# streaming acoustic model
am_encoder_infer_sess = get_sess(
model_path=str(
Path(args.inference_dir) /
(args.am + '_am_encoder_infer' + '.onnx')),
device=args.device,
cpu_threads=args.cpu_threads,
use_trt=args.use_trt)
am_decoder_sess = get_sess(
model_path=str(
Path(args.inference_dir) / (args.am + '_am_decoder' + '.onnx')),
device=args.device,
cpu_threads=args.cpu_threads,
use_trt=args.use_trt)
am_postnet_sess = get_sess(
model_path=str(
Path(args.inference_dir) / (args.am + '_am_postnet' + '.onnx')),
device=args.device,
cpu_threads=args.cpu_threads,
use_trt=args.use_trt)
am_mu, am_std = np.load(args.am_stat)
# 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)
# 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=True)
else:
print("lang should in be 'zh' here!")
# am warmup
for T in [27, 38, 54]:
phone_ids = np.random.randint(1, 266, size=(T, ))
am_encoder_infer_sess.run(None, input_feed={'text': phone_ids})
am_decoder_input = np.random.rand(1, T * 15, 384).astype('float32')
am_decoder_sess.run(None, input_feed={'xs': am_decoder_input})
am_postnet_input = np.random.rand(1, 80, T * 15).astype('float32')
am_postnet_sess.run(None, input_feed={'xs': am_postnet_input})
# 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 = True
get_tone_ids = False
block_size = args.block_size
pad_size = args.pad_size
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']
# merge_sentences=True here, so we only use the first item of phone_ids
phone_ids = phone_ids[0].numpy()
orig_hs = am_encoder_infer_sess.run(
None, input_feed={'text': phone_ids})
if args.am_streaming:
hss = get_chunks(orig_hs[0], block_size, pad_size)
chunk_num = len(hss)
mel_list = []
for i, hs in enumerate(hss):
am_decoder_output = am_decoder_sess.run(
None, input_feed={'xs': hs})
am_postnet_output = am_postnet_sess.run(
None,
input_feed={
'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
})
am_output_data = am_decoder_output + np.transpose(
am_postnet_output[0], (0, 2, 1))
normalized_mel = am_output_data[0][0]
sub_mel = denorm(normalized_mel, am_mu, am_std)
# clip output part of pad
if i == 0:
sub_mel = sub_mel[:-pad_size]
elif i == chunk_num - 1:
# 最后一块的右侧一定没有 pad 够
sub_mel = sub_mel[pad_size:]
else:
# 倒数几块的右侧也可能没有 pad 够
sub_mel = sub_mel[pad_size:(block_size + pad_size) -
sub_mel.shape[0]]
mel_list.append(sub_mel)
mel = np.concatenate(mel_list, axis=0)
else:
am_decoder_output = am_decoder_sess.run(
None, input_feed={'xs': orig_hs[0]})
am_postnet_output = am_postnet_sess.run(
None,
input_feed={
'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
})
am_output_data = am_decoder_output + np.transpose(
am_postnet_output[0], (0, 2, 1))
normalized_mel = am_output_data[0]
mel = denorm(normalized_mel, am_mu, am_std)
mel = mel[0]
# vocoder
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'],
help='Choose acoustic model type of tts task.')
parser.add_argument(
"--am_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
)
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.")
# 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(
"--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)
# streaming related
parser.add_argument(
"--am_streaming",
type=str2bool,
default=False,
help="whether use streaming acoustic model")
parser.add_argument(
"--block_size", type=int, default=42, help="block size of am streaming")
parser.add_argument(
"--pad_size", type=int, default=12, help="pad size of am streaming")
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()