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https://github.com/w-okada/voice-changer.git
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68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
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.RVC.deviceManager.DeviceManager import DeviceManager
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import onnxruntime
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from voice_changer.RVC.pitchExtractor import onnxcrepe
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from voice_changer.utils.VoiceChangerModel import AudioInOut
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class CrepeOnnxPitchExtractor(PitchExtractor):
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def __init__(self, pitchExtractorType: PitchExtractorType, file: str, gpu: int):
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self.pitchExtractorType = pitchExtractorType
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super().__init__()
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(
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onnxProviders,
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onnxProviderOptions,
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) = DeviceManager.get_instance().getOnnxExecutionProvider(gpu)
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self.onnx_session = onnxruntime.InferenceSession(
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file, providers=onnxProviders, provider_options=onnxProviderOptions
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)
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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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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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offset_frame_number = silence_front * sr
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start_frame = int(offset_frame_number / hop_size) # frame
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real_silence_front = start_frame * hop_size / sr # 秒
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audio = audio[int(np.round(real_silence_front * sr)):].astype(np.float32)
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precision = (1000 * hop_size / sr)
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onnx_f0, onnx_pd = onnxcrepe.predict(
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self.onnx_session,
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audio,
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sr,
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precision=precision,
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fmin=self.f0_min,
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fmax=self.f0_max,
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batch_size=256,
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return_periodicity=True,
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decoder=onnxcrepe.decode.weighted_argmax,
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)
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f0 = onnxcrepe.filter.median(onnx_f0, 3)
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pd = onnxcrepe.filter.median(onnx_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[: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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