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PaddleSpeech/paddlespeech/t2s/exps/voice_cloning.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
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()