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PaddleSpeech/paddlespeech/t2s/exps/synthesize_e2e.py

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# 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
import yaml
from timer import timer
from yacs.config import CfgNode
from paddlespeech.t2s.exps.syn_utils import am_to_static
from paddlespeech.t2s.exps.syn_utils import get_am_inference
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_voc_inference
from paddlespeech.t2s.exps.syn_utils import run_frontend
from paddlespeech.t2s.exps.syn_utils import voc_to_static
from paddlespeech.t2s.utils import str2bool
def evaluate(args):
# Init body.
with open(args.am_config) as f:
am_config = CfgNode(yaml.safe_load(f))
with open(args.voc_config) as f:
voc_config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(am_config)
print(voc_config)
sentences = get_sentences(text_file=args.text, lang=args.lang)
# frontend
frontend = get_frontend(
lang=args.lang,
phones_dict=args.phones_dict,
tones_dict=args.tones_dict,
use_rhy=args.use_rhy)
print("frontend done!")
# acoustic model
am_name = args.am[:args.am.rindex('_')]
am_dataset = args.am[args.am.rindex('_') + 1:]
am_inference = get_am_inference(
am=args.am,
am_config=am_config,
am_ckpt=args.am_ckpt,
am_stat=args.am_stat,
phones_dict=args.phones_dict,
tones_dict=args.tones_dict,
speaker_dict=args.speaker_dict)
print("acoustic model done!")
# vocoder
voc_inference = get_voc_inference(
voc=args.voc,
voc_config=voc_config,
voc_ckpt=args.voc_ckpt,
voc_stat=args.voc_stat)
print("voc done!")
# whether dygraph to static
if args.inference_dir:
# acoustic model
am_inference = am_to_static(
am_inference=am_inference,
am=args.am,
inference_dir=args.inference_dir,
speaker_dict=args.speaker_dict)
# vocoder
voc_inference = voc_to_static(
voc_inference=voc_inference,
voc=args.voc,
inference_dir=args.inference_dir)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
merge_sentences = False
# Avoid not stopping at the end of a sub sentence when tacotron2_ljspeech dygraph to static graph
# but still not stopping in the end (NOTE by yuantian01 Feb 9 2022)
if am_name == 'tacotron2':
merge_sentences = True
get_tone_ids = False
if am_name == 'speedyspeech':
get_tone_ids = True
N = 0
T = 0
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']
with paddle.no_grad():
flags = 0
for i in range(len(phone_ids)):
part_phone_ids = phone_ids[i]
# acoustic model
if am_name == 'fastspeech2':
# multi speaker
if am_dataset in {"aishell3", "vctk", "mix", "canton"}:
spk_id = paddle.to_tensor(args.spk_id)
mel = am_inference(part_phone_ids, spk_id)
else:
mel = am_inference(part_phone_ids)
elif am_name == 'speedyspeech':
part_tone_ids = frontend_dict['tone_ids'][i]
if am_dataset in {"aishell3", "vctk", "mix"}:
spk_id = paddle.to_tensor(args.spk_id)
mel = am_inference(part_phone_ids, part_tone_ids,
spk_id)
else:
mel = am_inference(part_phone_ids, part_tone_ids)
elif am_name == 'tacotron2':
mel = am_inference(part_phone_ids)
# vocoder
wav = voc_inference(mel)
if flags == 0:
wav_all = wav
flags = 1
else:
wav_all = paddle.concat([wav_all, wav])
wav = wav_all.numpy()
N += wav.size
T += t.elapse
speed = wav.size / t.elapse
rtf = am_config.fs / speed
print(
f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
)
sf.write(
str(output_dir / (utt_id + ".wav")), wav, samplerate=am_config.fs)
print(f"{utt_id} done!")
print(f"generation speed: {N / T}Hz, RTF: {am_config.fs / (N / T) }")
def parse_args():
# parse args and config
parser = argparse.ArgumentParser(
description="Synthesize with acoustic model & vocoder")
# acoustic model
parser.add_argument(
'--am',
type=str,
default='fastspeech2_csmsc',
choices=[
'speedyspeech_csmsc',
'speedyspeech_aishell3',
'fastspeech2_csmsc',
'fastspeech2_ljspeech',
'fastspeech2_aishell3',
'fastspeech2_vctk',
'tacotron2_csmsc',
'tacotron2_ljspeech',
'fastspeech2_mix',
'fastspeech2_canton',
'fastspeech2_male-zh',
'fastspeech2_male-en',
'fastspeech2_male-mix',
],
help='Choose acoustic model type of tts task.')
parser.add_argument(
'--am_config', type=str, default=None, help='Config of acoustic model.')
parser.add_argument(
'--am_ckpt',
type=str,
default=None,
help='Checkpoint file of acoustic model.')
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')
# vocoder
parser.add_argument(
'--voc',
type=str,
default='pwgan_csmsc',
choices=[
'pwgan_csmsc',
'pwgan_ljspeech',
'pwgan_aishell3',
'pwgan_vctk',
'mb_melgan_csmsc',
'style_melgan_csmsc',
'hifigan_csmsc',
'hifigan_ljspeech',
'hifigan_aishell3',
'hifigan_vctk',
'wavernn_csmsc',
'pwgan_male',
'hifigan_male',
],
help='Choose vocoder type of tts task.')
parser.add_argument(
'--voc_config', type=str, default=None, help='Config of voc.')
parser.add_argument(
'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
parser.add_argument(
"--voc_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training voc."
)
# other
parser.add_argument(
'--lang',
type=str,
default='zh',
help='Choose model language. zh or en or mix')
parser.add_argument(
"--inference_dir",
type=str,
default=None,
help="dir to save inference models")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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(
"--use_rhy",
type=str2bool,
default=False,
help="run rhythm frontend or not")
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
evaluate(args)
if __name__ == "__main__":
main()