# 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
from pathlib import Path
import paddle
import soundfile as sf
from timer import timer
from paddlespeech.t2s.exps.syn_utils import get_am_output
from paddlespeech.t2s.exps.syn_utils import get_frontend
from paddlespeech.t2s.exps.syn_utils import get_predictor
from paddlespeech.t2s.exps.syn_utils import get_sentences
from paddlespeech.t2s.exps.syn_utils import get_voc_output
from paddlespeech.t2s.utils import str2bool
[docs]def parse_args():
parser = argparse.ArgumentParser(
description="Paddle Infernce with acoustic model & vocoder.")
# acoustic model
parser.add_argument(
'--am',
type=str,
default='fastspeech2_csmsc',
choices=[
'speedyspeech_csmsc',
'fastspeech2_csmsc',
'fastspeech2_aishell3',
'fastspeech2_ljspeech',
'fastspeech2_vctk',
'tacotron2_csmsc',
'fastspeech2_mix',
'fastspeech2_male-zh',
'fastspeech2_male-en',
'fastspeech2_male-mix',
'fastspeech2_canton',
],
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(
"--speaker_dict", type=str, default=None, help="speaker id map 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='pwgan_csmsc',
choices=[
'pwgan_csmsc',
'pwgan_aishell3',
'pwgan_ljspeech',
'pwgan_vctk',
'mb_melgan_csmsc',
'hifigan_csmsc',
'hifigan_aishell3',
'hifigan_ljspeech',
'hifigan_vctk',
'wavernn_csmsc',
'pwgan_male',
'hifigan_male',
],
help='Choose vocoder type of tts task.')
# other
parser.add_argument(
'--lang',
type=str,
default='zh',
help='Choose model language. zh or en or mix')
parser.add_argument(
"--text",
type=str,
help="text to synthesize, a 'utt_id sentence' pair per line")
parser.add_argument(
"--inference_dir", type=str, help="dir to save inference models")
parser.add_argument("--output_dir", type=str, help="output dir")
# inference
parser.add_argument(
"--use_trt",
type=str2bool,
default=False,
help="whether to use TensorRT or not in GPU", )
parser.add_argument(
"--use_mkldnn",
type=str2bool,
default=False,
help="whether to use MKLDNN or not in CPU.", )
parser.add_argument(
"--precision",
type=str,
default='fp32',
choices=['fp32', 'fp16', 'bf16', 'int8'],
help="mode of running")
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
# only inference for models trained with csmsc now
[docs]def main():
args = parse_args()
paddle.set_device(args.device)
# frontend
frontend = get_frontend(
lang=args.lang,
phones_dict=args.phones_dict,
tones_dict=args.tones_dict)
# am_predictor
am_predictor = get_predictor(
model_dir=args.inference_dir,
model_file=args.am + ".pdmodel",
params_file=args.am + ".pdiparams",
device=args.device,
use_trt=args.use_trt,
use_mkldnn=args.use_mkldnn,
cpu_threads=args.cpu_threads,
precision=args.precision)
# model: {model_name}_{dataset}
am_dataset = args.am[args.am.rindex('_') + 1:]
# voc_predictor
voc_predictor = get_predictor(
model_dir=args.inference_dir,
model_file=args.voc + ".pdmodel",
params_file=args.voc + ".pdiparams",
device=args.device,
use_trt=args.use_trt,
use_mkldnn=args.use_mkldnn,
cpu_threads=args.cpu_threads,
precision=args.precision)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
sentences = get_sentences(text_file=args.text, lang=args.lang)
merge_sentences = True
fs = 24000 if am_dataset != 'ljspeech' else 22050
# warmup
for utt_id, sentence in sentences[:3]:
with timer() as t:
mel = get_am_output(
input=sentence,
am_predictor=am_predictor,
am=args.am,
frontend=frontend,
lang=args.lang,
merge_sentences=merge_sentences,
speaker_dict=args.speaker_dict,
spk_id=args.spk_id, )
wav = get_voc_output(voc_predictor=voc_predictor, input=mel)
speed = wav.size / t.elapse
rtf = fs / speed
print(
f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
)
print("warm up done!")
N = 0
T = 0
for utt_id, sentence in sentences:
with timer() as t:
mel = get_am_output(
input=sentence,
am_predictor=am_predictor,
am=args.am,
frontend=frontend,
lang=args.lang,
merge_sentences=merge_sentences,
speaker_dict=args.speaker_dict,
spk_id=args.spk_id, )
wav = get_voc_output(voc_predictor=voc_predictor, input=mel)
N += wav.size
T += t.elapse
speed = wav.size / t.elapse
rtf = fs / speed
sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=fs)
print(
f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
)
print(f"{utt_id} done!")
print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
if __name__ == "__main__":
main()