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