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| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from diffusion.unit2mel import load_model_vocoder | |
| class DiffGtMel: | |
| def __init__(self, project_path=None, device=None): | |
| self.project_path = project_path | |
| if device is not None: | |
| self.device = device | |
| else: | |
| self.device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| self.model = None | |
| self.vocoder = None | |
| self.args = None | |
| def flush_model(self, project_path, ddsp_config=None): | |
| if (self.model is None) or (project_path != self.project_path): | |
| model, vocoder, args = load_model_vocoder(project_path, device=self.device) | |
| if self.check_args(ddsp_config, args): | |
| self.model = model | |
| self.vocoder = vocoder | |
| self.args = args | |
| def check_args(self, args1, args2): | |
| if args1.data.block_size != args2.data.block_size: | |
| raise ValueError("DDSP与DIFF模型的block_size不一致") | |
| if args1.data.sampling_rate != args2.data.sampling_rate: | |
| raise ValueError("DDSP与DIFF模型的sampling_rate不一致") | |
| if args1.data.encoder != args2.data.encoder: | |
| raise ValueError("DDSP与DIFF模型的encoder不一致") | |
| return True | |
| def __call__(self, audio, f0, hubert, volume, acc=1, spk_id=1, k_step=0, method='pndm', | |
| spk_mix_dict=None, start_frame=0): | |
| input_mel = self.vocoder.extract(audio, self.args.data.sampling_rate) | |
| out_mel = self.model( | |
| hubert, | |
| f0, | |
| volume, | |
| spk_id=spk_id, | |
| spk_mix_dict=spk_mix_dict, | |
| gt_spec=input_mel, | |
| infer=True, | |
| infer_speedup=acc, | |
| method=method, | |
| k_step=k_step, | |
| use_tqdm=False) | |
| if start_frame > 0: | |
| out_mel = out_mel[:, start_frame:, :] | |
| f0 = f0[:, start_frame:, :] | |
| output = self.vocoder.infer(out_mel, f0) | |
| if start_frame > 0: | |
| output = F.pad(output, (start_frame * self.vocoder.vocoder_hop_size, 0)) | |
| return output | |
| def infer(self, audio, f0, hubert, volume, acc=1, spk_id=1, k_step=0, method='pndm', silence_front=0, | |
| use_silence=False, spk_mix_dict=None): | |
| start_frame = int(silence_front * self.vocoder.vocoder_sample_rate / self.vocoder.vocoder_hop_size) | |
| if use_silence: | |
| audio = audio[:, start_frame * self.vocoder.vocoder_hop_size:] | |
| f0 = f0[:, start_frame:, :] | |
| hubert = hubert[:, start_frame:, :] | |
| volume = volume[:, start_frame:, :] | |
| _start_frame = 0 | |
| else: | |
| _start_frame = start_frame | |
| audio = self.__call__(audio, f0, hubert, volume, acc=acc, spk_id=spk_id, k_step=k_step, | |
| method=method, spk_mix_dict=spk_mix_dict, start_frame=_start_frame) | |
| if use_silence: | |
| if start_frame > 0: | |
| audio = F.pad(audio, (start_frame * self.vocoder.vocoder_hop_size, 0)) | |
| return audio | |