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58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
from torchaudio.transforms import Resample
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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.DiffusionSVC.pitchExtractor.rmvpe.rmvpe import RMVPE
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from scipy.ndimage import zoom
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from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
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from voice_changer.utils.VoiceChangerModel import AudioInOut
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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.uv_interp = True
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self.input_sr = -1
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self.device = DeviceManager.get_instance().getDevice(gpu)
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self.rmvpe = RMVPE(model_path=file, is_half=False, device=self.device)
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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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if sr != self.input_sr:
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self.resamle = Resample(sr, 16000, dtype=torch.int16).to(self.device)
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self.input_sr = sr
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audio_t = torch.from_numpy(audio).float().unsqueeze(0).to(self.device)
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audio_t = self.resamle(audio_t)
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hop_size = 160 # RMVPE固定
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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 = self.rmvpe.infer_from_audio_t(audio_t.squeeze(), thred=0.03)
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desired_hop_size = block_size * 16000 / model_sr
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desired_f0_length = int(audio_t.shape[1] // desired_hop_size) + 1
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resize_factor = desired_f0_length / 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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