import torchcrepe import torch import numpy as np from const import PitchExtractorType from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractor import PitchExtractor from voice_changer.utils.VoiceChangerModel import AudioInOut class CrepePitchExtractor(PitchExtractor): def __init__(self): super().__init__() self.pitchExtractorType: PitchExtractorType = "crepe" self.f0_min = 50 self.f0_max = 1100 self.uv_interp = True if torch.cuda.is_available(): self.device = torch.device("cuda:" + str(torch.cuda.current_device())) else: self.device = torch.device("cpu") def extract(self, audio: AudioInOut, sr: int, block_size: int, model_sr: int, pitch, f0_up_key, silence_front=0): hop_size = block_size * sr / model_sr audio_t = torch.from_numpy(audio).float().unsqueeze(0).to(self.device) offset_frame_number = silence_front * 16000 start_frame = int(offset_frame_number / hop_size) # frame real_silence_front = start_frame * hop_size / 16000 # 秒 audio_t = audio_t[:, int(np.round(real_silence_front * 16000)):] f0, pd = torchcrepe.predict( audio_t, sr, hop_length=hop_size, fmin=self.f0_min, fmax=self.f0_max, # model="tiny", model="full", batch_size=256, decoder=torchcrepe.decode.weighted_argmax, device=self.device, return_periodicity=True, ) f0 = torchcrepe.filter.median(f0, 3) # 本家だとmeanですが、harvestに合わせmedianフィルタ pd = torchcrepe.filter.median(pd, 3) f0[pd < 0.1] = 0 f0 = f0.squeeze() pitch[-f0.shape[0]:] = f0.cpu()[:pitch.shape[0]] f0 = pitch if self.uv_interp: uv = f0 == 0 if len(f0[~uv]) > 0: f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) f0[f0 < self.f0_min] = self.f0_min f0 = f0 * 2 ** (float(f0_up_key) / 12) return f0