# 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 and redirect to train_sp 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. Use deault config when it is None.') 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. Use deault config when it is None.') 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()