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.
263 lines
8.6 KiB
263 lines
8.6 KiB
# 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
|
|
|
|
|
|
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)
|
|
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"}:
|
|
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'
|
|
],
|
|
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',
|
|
],
|
|
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.")
|
|
|
|
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()
|