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63 lines
2.0 KiB
Python
63 lines
2.0 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 EnumPitchExtractorTypes
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from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
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class CrepePitchExtractor(PitchExtractor):
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pitchExtractorType: EnumPitchExtractorTypes = EnumPitchExtractorTypes.crepe
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def __init__(self):
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super().__init__()
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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, f0_up_key, sr, window, silence_front=0):
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n_frames = int(len(audio) // window) + 1
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start_frame = int(silence_front * sr / window)
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real_silence_front = start_frame * window / sr
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silence_front_offset = int(np.round(real_silence_front * sr))
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audio = audio[silence_front_offset:]
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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f0, pd = torchcrepe.predict(
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audio.unsqueeze(0),
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sr,
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hop_length=window,
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fmin=f0_min,
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fmax=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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f0 = torch.nn.functional.pad(
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f0, (start_frame, n_frames - f0.shape[0] - start_frame)
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)
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f0 *= pow(2, f0_up_key / 12)
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f0bak = f0.detach().cpu().numpy()
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f0_mel = 1127.0 * torch.log(1.0 + f0 / 700.0)
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f0_mel = torch.clip(
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(f0_mel - f0_mel_min) * 254.0 / (f0_mel_max - f0_mel_min) + 1.0, 1.0, 255.0
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)
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f0_coarse = f0_mel.round().detach().cpu().numpy().astype(int)
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return f0_coarse, f0bak
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