import numpy as np from const import PitchExtractorType from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor import onnxruntime from voice_changer.RVC.pitchExtractor import onnxcrepe class CrepeOnnxPitchExtractor(PitchExtractor): def __init__(self, pitchExtractorType: PitchExtractorType, file: str, gpu: int): self.pitchExtractorType = pitchExtractorType super().__init__() ( onnxProviders, onnxProviderOptions, ) = DeviceManager.get_instance().getOnnxExecutionProvider(gpu) self.onnx_session = onnxruntime.InferenceSession( file, providers=onnxProviders, provider_options=onnxProviderOptions ) def extract(self, audio, pitchf, f0_up_key, sr, window, silence_front=0): n_frames = int(len(audio) // window) + 1 start_frame = int(silence_front * sr / window) real_silence_front = start_frame * window / sr silence_front_offset = int(np.round(real_silence_front * sr)) audio = audio[silence_front_offset:] f0_min = 50 f0_max = 1100 f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700) precision = 10.0 audio_num = audio.cpu() onnx_f0, onnx_pd = onnxcrepe.predict( self.onnx_session, audio_num, sr, precision=precision, fmin=f0_min, fmax=f0_max, batch_size=256, return_periodicity=True, decoder=onnxcrepe.decode.weighted_argmax, ) f0 = onnxcrepe.filter.median(onnx_f0, 3) pd = onnxcrepe.filter.median(onnx_pd, 3) f0[pd < 0.1] = 0 f0 = f0.squeeze() f0 *= pow(2, f0_up_key / 12) pitchf[-f0.shape[0]:] = f0[:pitchf.shape[0]] f0bak = pitchf.copy() f0_mel = 1127.0 * np.log(1.0 + f0bak / 700.0) f0_mel = np.clip( (f0_mel - f0_mel_min) * 254.0 / (f0_mel_max - f0_mel_min) + 1.0, 1.0, 255.0 ) pitch_coarse = f0_mel.astype(int) return pitch_coarse, pitchf