You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
231 lines
8.4 KiB
231 lines
8.4 KiB
# 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 soundfile as sf
|
|
from timer import timer
|
|
|
|
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_am_sublayer_output
|
|
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_predictor
|
|
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_streaming_am_output
|
|
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_voc_output
|
|
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 run_frontend
|
|
from paddlespeech.t2s.utils import str2bool
|
|
|
|
|
|
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=['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.")
|
|
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', 'mb_melgan_csmsc', 'hifigan_csmsc'],
|
|
help='Choose vocoder type of tts task.')
|
|
# other
|
|
parser.add_argument(
|
|
'--lang',
|
|
type=str,
|
|
default='zh',
|
|
help='Choose model language. zh or en')
|
|
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
|
|
|
|
# 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
|
|
|
|
|
|
# only inference for models trained with csmsc now
|
|
def main():
|
|
args = parse_args()
|
|
|
|
# frontend
|
|
frontend = get_frontend(
|
|
lang=args.lang,
|
|
phones_dict=args.phones_dict,
|
|
tones_dict=args.tones_dict)
|
|
|
|
# am_predictor
|
|
am_encoder_infer_predictor = get_lite_predictor(
|
|
model_dir=args.inference_dir,
|
|
model_file=args.am + "_am_encoder_infer" + "_x86.nb")
|
|
am_decoder_predictor = get_lite_predictor(
|
|
model_dir=args.inference_dir,
|
|
model_file=args.am + "_am_decoder" + "_x86.nb")
|
|
am_postnet_predictor = get_lite_predictor(
|
|
model_dir=args.inference_dir,
|
|
model_file=args.am + "_am_postnet" + "_x86.nb")
|
|
am_mu, am_std = np.load(args.am_stat)
|
|
# model: {model_name}_{dataset}
|
|
am_dataset = args.am[args.am.rindex('_') + 1:]
|
|
|
|
# voc_predictor
|
|
voc_predictor = get_lite_predictor(
|
|
model_dir=args.inference_dir, model_file=args.voc + "_x86.nb")
|
|
|
|
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:
|
|
normalized_mel = get_lite_streaming_am_output(
|
|
input=sentence,
|
|
am_encoder_infer_predictor=am_encoder_infer_predictor,
|
|
am_decoder_predictor=am_decoder_predictor,
|
|
am_postnet_predictor=am_postnet_predictor,
|
|
frontend=frontend,
|
|
lang=args.lang,
|
|
merge_sentences=merge_sentences, )
|
|
mel = denorm(normalized_mel, am_mu, am_std)
|
|
wav = get_lite_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
|
|
block_size = args.block_size
|
|
pad_size = args.pad_size
|
|
get_tone_ids = False
|
|
for utt_id, sentence in sentences:
|
|
with timer() as t:
|
|
# frontend
|
|
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']
|
|
phones = phone_ids[0].numpy()
|
|
# acoustic model
|
|
orig_hs = get_lite_am_sublayer_output(
|
|
am_encoder_infer_predictor, input=phones)
|
|
|
|
if args.am_streaming:
|
|
hss = get_chunks(orig_hs, block_size, pad_size)
|
|
chunk_num = len(hss)
|
|
mel_list = []
|
|
for i, hs in enumerate(hss):
|
|
am_decoder_output = get_lite_am_sublayer_output(
|
|
am_decoder_predictor, input=hs)
|
|
am_postnet_output = get_lite_am_sublayer_output(
|
|
am_postnet_predictor,
|
|
input=np.transpose(am_decoder_output, (0, 2, 1)))
|
|
am_output_data = am_decoder_output + np.transpose(
|
|
am_postnet_output, (0, 2, 1))
|
|
normalized_mel = am_output_data[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 = get_lite_am_sublayer_output(
|
|
am_decoder_predictor, input=orig_hs)
|
|
am_postnet_output = get_lite_am_sublayer_output(
|
|
am_postnet_predictor,
|
|
input=np.transpose(am_decoder_output, (0, 2, 1)))
|
|
am_output_data = am_decoder_output + np.transpose(
|
|
am_postnet_output, (0, 2, 1))
|
|
normalized_mel = am_output_data[0]
|
|
mel = denorm(normalized_mel, am_mu, am_std)
|
|
# vocoder
|
|
wav = get_lite_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=24000)
|
|
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()
|