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.
212 lines
6.4 KiB
212 lines
6.4 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
|
|
import os
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import paddle
|
|
import soundfile as sf
|
|
import yaml
|
|
from yacs.config import CfgNode
|
|
|
|
from paddlespeech.t2s.exps.syn_utils import get_am_inference
|
|
from paddlespeech.t2s.exps.syn_utils import get_voc_inference
|
|
from paddlespeech.t2s.frontend.zh_frontend import Frontend
|
|
from paddlespeech.vector.exps.ge2e.audio_processor import SpeakerVerificationPreprocessor
|
|
from paddlespeech.vector.models.lstm_speaker_encoder import LSTMSpeakerEncoder
|
|
|
|
|
|
def voice_cloning(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)
|
|
|
|
# speaker encoder
|
|
p = SpeakerVerificationPreprocessor(
|
|
sampling_rate=16000,
|
|
audio_norm_target_dBFS=-30,
|
|
vad_window_length=30,
|
|
vad_moving_average_width=8,
|
|
vad_max_silence_length=6,
|
|
mel_window_length=25,
|
|
mel_window_step=10,
|
|
n_mels=40,
|
|
partial_n_frames=160,
|
|
min_pad_coverage=0.75,
|
|
partial_overlap_ratio=0.5)
|
|
print("Audio Processor Done!")
|
|
|
|
speaker_encoder = LSTMSpeakerEncoder(
|
|
n_mels=40, num_layers=3, hidden_size=256, output_size=256)
|
|
speaker_encoder.set_state_dict(paddle.load(args.ge2e_params_path))
|
|
speaker_encoder.eval()
|
|
print("GE2E Done!")
|
|
|
|
frontend = Frontend(phone_vocab_path=args.phones_dict)
|
|
print("frontend done!")
|
|
|
|
# acoustic model
|
|
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)
|
|
|
|
# vocoder
|
|
voc_inference = get_voc_inference(
|
|
voc=args.voc,
|
|
voc_config=voc_config,
|
|
voc_ckpt=args.voc_ckpt,
|
|
voc_stat=args.voc_stat)
|
|
|
|
output_dir = Path(args.output_dir)
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
input_dir = Path(args.input_dir)
|
|
|
|
sentence = args.text
|
|
|
|
input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
|
|
phone_ids = input_ids["phone_ids"][0]
|
|
|
|
for name in os.listdir(input_dir):
|
|
utt_id = name.split(".")[0]
|
|
ref_audio_path = input_dir / name
|
|
mel_sequences = p.extract_mel_partials(p.preprocess_wav(ref_audio_path))
|
|
# print("mel_sequences: ", mel_sequences.shape)
|
|
with paddle.no_grad():
|
|
spk_emb = speaker_encoder.embed_utterance(
|
|
paddle.to_tensor(mel_sequences))
|
|
# print("spk_emb shape: ", spk_emb.shape)
|
|
|
|
with paddle.no_grad():
|
|
wav = voc_inference(am_inference(phone_ids, spk_emb=spk_emb))
|
|
|
|
sf.write(
|
|
str(output_dir / (utt_id + ".wav")),
|
|
wav.numpy(),
|
|
samplerate=am_config.fs)
|
|
print(f"{utt_id} done!")
|
|
# Randomly generate numbers of 0 ~ 0.2, 256 is the dim of spk_emb
|
|
random_spk_emb = np.random.rand(256) * 0.2
|
|
random_spk_emb = paddle.to_tensor(random_spk_emb, dtype='float32')
|
|
utt_id = "random_spk_emb"
|
|
with paddle.no_grad():
|
|
wav = voc_inference(am_inference(phone_ids, spk_emb=random_spk_emb))
|
|
sf.write(
|
|
str(output_dir / (utt_id + ".wav")),
|
|
wav.numpy(),
|
|
samplerate=am_config.fs)
|
|
print(f"{utt_id} done!")
|
|
|
|
|
|
def parse_args():
|
|
# parse args and config
|
|
parser = argparse.ArgumentParser(description="")
|
|
parser.add_argument(
|
|
'--am',
|
|
type=str,
|
|
default='fastspeech2_csmsc',
|
|
choices=['fastspeech2_aishell3', 'tacotron2_aishell3'],
|
|
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="phone_id_map.txt",
|
|
help="phone vocabulary file.")
|
|
# vocoder
|
|
parser.add_argument(
|
|
'--voc',
|
|
type=str,
|
|
default='pwgan_csmsc',
|
|
choices=['pwgan_aishell3'],
|
|
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."
|
|
)
|
|
parser.add_argument(
|
|
"--text",
|
|
type=str,
|
|
default="每当你觉得，想要批评什么人的时候，你切要记着，这个世界上的人，并非都具备你禀有的条件。",
|
|
help="text to synthesize, a line")
|
|
|
|
parser.add_argument(
|
|
"--ge2e_params_path", type=str, help="ge2e params path.")
|
|
|
|
parser.add_argument(
|
|
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
|
|
|
|
parser.add_argument(
|
|
"--input-dir",
|
|
type=str,
|
|
help="input dir of *.wav, the sample rate will be resample to 16k.")
|
|
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 !")
|
|
|
|
voice_cloning(args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|