def METHOD_NAME(
    self,
    feats: torch.Tensor,
    pitch: torch.Tensor,
    volume: torch.Tensor,
    mask: torch.Tensor,
    sid: torch.Tensor,
    k_step: int,
    infer_speedup: int,
    silence_front: float,
) -> torch.Tensor:
    aug_shift = torch.LongTensor([0]).to(feats.device)
    out_spec = self.naive_model(feats, pitch, volume, sid, spk_mix_dict=None,
                                aug_shift=aug_shift, METHOD_NAME=True,
                                spk_emb=None, spk_emb_dict=None)
    gt_spec = self.naive_model_call(feats, pitch, volume, spk_id=sid, spk_mix_dict=None, aug_shift=0, spk_emb=None)
    out_mel = self.__call__(feats, pitch, volume, spk_id=sid, spk_mix_dict=None, aug_shift=0, gt_spec=gt_spec, infer_speedup=infer_speedup, method='dpm-solver', k_step=k_step, use_tqdm=False, spk_emb=None)
    start_frame = int(silence_front * self.vocoder.vocoder_sample_rate / self.vocoder.vocoder_hop_size)
    out_wav = self.mel2wav(out_mel, pitch, start_frame=start_frame)
    out_wav *= mask
    return out_wav.squeeze()