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256 lines
8.3 KiB
256 lines
8.3 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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from pathlib import Path
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import numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from yacs.config import CfgNode
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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from paddlespeech.t2s.modules.normalizer import ZScore
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from paddlespeech.vector.exps.ge2e.audio_processor import SpeakerVerificationPreprocessor
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from paddlespeech.vector.models.lstm_speaker_encoder import LSTMSpeakerEncoder
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model_alias = {
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# acoustic model
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"fastspeech2":
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"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
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"fastspeech2_inference":
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"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
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"tacotron2":
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"paddlespeech.t2s.models.tacotron2:Tacotron2",
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"tacotron2_inference":
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"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
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# voc
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"pwgan":
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"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
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"pwgan_inference":
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"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
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}
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def voice_cloning(args):
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# Init body.
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with open(args.am_config) as f:
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am_config = CfgNode(yaml.safe_load(f))
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with open(args.voc_config) as f:
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voc_config = CfgNode(yaml.safe_load(f))
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(am_config)
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print(voc_config)
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# speaker encoder
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p = SpeakerVerificationPreprocessor(
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sampling_rate=16000,
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audio_norm_target_dBFS=-30,
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vad_window_length=30,
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vad_moving_average_width=8,
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vad_max_silence_length=6,
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mel_window_length=25,
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mel_window_step=10,
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n_mels=40,
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partial_n_frames=160,
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min_pad_coverage=0.75,
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partial_overlap_ratio=0.5)
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print("Audio Processor Done!")
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speaker_encoder = LSTMSpeakerEncoder(
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n_mels=40, num_layers=3, hidden_size=256, output_size=256)
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speaker_encoder.set_state_dict(paddle.load(args.ge2e_params_path))
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speaker_encoder.eval()
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print("GE2E Done!")
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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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# acoustic model
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odim = am_config.n_mels
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# model: {model_name}_{dataset}
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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am_class = dynamic_import(am_name, model_alias)
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am_inference_class = dynamic_import(am_name + '_inference', model_alias)
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if am_name == 'fastspeech2':
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am = am_class(
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idim=vocab_size, odim=odim, spk_num=None, **am_config["model"])
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elif am_name == 'tacotron2':
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am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
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am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(args.am_stat)
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am_mu = paddle.to_tensor(am_mu)
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am_std = paddle.to_tensor(am_std)
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am_normalizer = ZScore(am_mu, am_std)
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am_inference = am_inference_class(am_normalizer, am)
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am_inference.eval()
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print("acoustic model done!")
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# vocoder
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# model: {model_name}_{dataset}
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voc_name = args.voc[:args.voc.rindex('_')]
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voc_class = dynamic_import(voc_name, model_alias)
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voc_inference_class = dynamic_import(voc_name + '_inference', model_alias)
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voc = voc_class(**voc_config["generator_params"])
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voc.set_state_dict(paddle.load(args.voc_ckpt)["generator_params"])
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voc.remove_weight_norm()
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voc.eval()
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voc_mu, voc_std = np.load(args.voc_stat)
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voc_mu = paddle.to_tensor(voc_mu)
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voc_std = paddle.to_tensor(voc_std)
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voc_normalizer = ZScore(voc_mu, voc_std)
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voc_inference = voc_inference_class(voc_normalizer, voc)
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voc_inference.eval()
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print("voc done!")
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frontend = Frontend(phone_vocab_path=args.phones_dict)
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print("frontend done!")
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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input_dir = Path(args.input_dir)
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sentence = args.text
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input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
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phone_ids = input_ids["phone_ids"][0]
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for name in os.listdir(input_dir):
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utt_id = name.split(".")[0]
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ref_audio_path = input_dir / name
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mel_sequences = p.extract_mel_partials(p.preprocess_wav(ref_audio_path))
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# print("mel_sequences: ", mel_sequences.shape)
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with paddle.no_grad():
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spk_emb = speaker_encoder.embed_utterance(
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paddle.to_tensor(mel_sequences))
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# print("spk_emb shape: ", spk_emb.shape)
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with paddle.no_grad():
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wav = voc_inference(am_inference(phone_ids, spk_emb=spk_emb))
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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wav.numpy(),
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samplerate=am_config.fs)
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print(f"{utt_id} done!")
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# Randomly generate numbers of 0 ~ 0.2, 256 is the dim of spk_emb
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random_spk_emb = np.random.rand(256) * 0.2
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random_spk_emb = paddle.to_tensor(random_spk_emb)
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utt_id = "random_spk_emb"
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with paddle.no_grad():
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wav = voc_inference(am_inference(phone_ids, spk_emb=spk_emb))
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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wav.numpy(),
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samplerate=am_config.fs)
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print(f"{utt_id} done!")
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(description="")
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parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=['fastspeech2_aishell3', 'tacotron2_aishell3'],
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help='Choose acoustic model type of tts task.')
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parser.add_argument(
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'--am_config',
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type=str,
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default=None,
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help='Config of acoustic model. Use deault config when it is None.')
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parser.add_argument(
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'--am_ckpt',
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type=str,
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default=None,
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help='Checkpoint file of acoustic model.')
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parser.add_argument(
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"--am_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training acoustic model."
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)
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parser.add_argument(
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"--phones-dict",
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type=str,
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default="phone_id_map.txt",
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help="phone vocabulary file.")
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# vocoder
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parser.add_argument(
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'--voc',
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type=str,
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default='pwgan_csmsc',
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choices=['pwgan_aishell3'],
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help='Choose vocoder type of tts task.')
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parser.add_argument(
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'--voc_config',
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type=str,
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default=None,
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help='Config of voc. Use deault config when it is None.')
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parser.add_argument(
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'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
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parser.add_argument(
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"--voc_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training voc."
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)
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parser.add_argument(
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"--text",
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type=str,
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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。",
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help="text to synthesize, a line")
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parser.add_argument(
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"--ge2e_params_path", type=str, help="ge2e params path.")
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parser.add_argument(
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"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
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parser.add_argument(
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"--input-dir",
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type=str,
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help="input dir of *.wav, the sample rate will be resample to 16k.")
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parser.add_argument("--output-dir", type=str, help="output dir.")
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args = parser.parse_args()
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if args.ngpu == 0:
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paddle.set_device("cpu")
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elif args.ngpu > 0:
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paddle.set_device("gpu")
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else:
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print("ngpu should >= 0 !")
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voice_cloning(args)
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if __name__ == "__main__":
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main()
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