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65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
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.DiffusionSVC.pitchExtractor.rmvpe.rmvpe import RMVPE
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from scipy.ndimage import zoom
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class RMVPEPitchExtractor(PitchExtractor):
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def __init__(self, file: str, gpu: int):
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super().__init__()
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self.pitchExtractorType: PitchExtractorType = "rmvpe"
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self.f0_min = 50
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self.f0_max = 1100
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self.sapmle_rate = 16000
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self.uv_interp = True
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if torch.cuda.is_available() and gpu >= 0:
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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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self.rmvpe = RMVPE(model_path=file, is_half=False, device=self.device)
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def extract(self, audio: torch.Tensor, pitch, f0_up_key, window, silence_front=0):
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start_frame = int(silence_front * self.sapmle_rate / window)
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real_silence_front = start_frame * window / self.sapmle_rate
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audio = audio[int(np.round(real_silence_front * self.sapmle_rate)):]
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silented_frames = int(audio.size(0) // window) + 1
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f0 = self.rmvpe.infer_from_audio_t(audio, thred=0.03)
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# f0, pd = torchcrepe.predict(
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# audio.unsqueeze(0),
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# self.sapmle_rate,
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# hop_length=window,
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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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resize_factor = silented_frames / len(f0)
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f0 = zoom(f0, resize_factor, order=0)
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pitch[-f0.shape[0]:] = f0[: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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