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