# 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()