mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
187 lines
6.9 KiB
Python
187 lines
6.9 KiB
Python
import sys
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import os
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from dataclasses import asdict
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import numpy as np
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import torch
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from data.ModelSlot import DDSPSVCModelSlot
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from voice_changer.DDSP_SVC.deviceManager.DeviceManager import DeviceManager
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from voice_changer.VoiceChangerParamsManager import VoiceChangerParamsManager
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if sys.platform.startswith("darwin"):
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baseDir = [x for x in sys.path if x.endswith("Contents/MacOS")]
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if len(baseDir) != 1:
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print("baseDir should be only one ", baseDir)
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sys.exit()
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modulePath = os.path.join(baseDir[0], "DDSP-SVC")
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sys.path.append(modulePath)
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else:
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sys.path.append("DDSP-SVC")
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from .models.diffusion.infer_gt_mel import DiffGtMel
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from voice_changer.utils.VoiceChangerModel import AudioInOut
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from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
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from voice_changer.DDSP_SVC.DDSP_SVCSetting import DDSP_SVCSettings
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from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
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# from Exceptions import NoModeLoadedException
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from voice_changer.DDSP_SVC.SvcDDSP import SvcDDSP
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def phase_vocoder(a, b, fade_out, fade_in):
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fa = torch.fft.rfft(a)
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fb = torch.fft.rfft(b)
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absab = torch.abs(fa) + torch.abs(fb)
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n = a.shape[0]
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if n % 2 == 0:
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absab[1:-1] *= 2
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else:
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absab[1:] *= 2
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phia = torch.angle(fa)
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phib = torch.angle(fb)
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deltaphase = phib - phia
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deltaphase = deltaphase - 2 * np.pi * torch.floor(deltaphase / 2 / np.pi + 0.5)
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w = 2 * np.pi * torch.arange(n // 2 + 1).to(a) + deltaphase
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t = torch.arange(n).unsqueeze(-1).to(a) / n
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result = a * (fade_out**2) + b * (fade_in**2) + torch.sum(absab * torch.cos(w * t + phia), -1) * fade_out * fade_in / n
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return result
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class DDSP_SVC:
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initialLoad: bool = True
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def __init__(self, params: VoiceChangerParams, slotInfo: DDSPSVCModelSlot):
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print("[Voice Changer] [DDSP-SVC] Creating instance ")
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self.deviceManager = DeviceManager.get_instance()
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self.gpu_num = torch.cuda.device_count()
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self.params = params
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self.settings = DDSP_SVCSettings()
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self.svc_model: SvcDDSP = SvcDDSP()
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self.diff_model: DiffGtMel = DiffGtMel()
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self.svc_model.setVCParams(params)
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EmbedderManager.initialize(params)
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self.audio_buffer: AudioInOut | None = None
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self.prevVol = 0.0
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self.slotInfo = slotInfo
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self.initialize()
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def initialize(self):
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self.device = self.deviceManager.getDevice(self.settings.gpu)
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vcparams = VoiceChangerParamsManager.get_instance().params
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modelPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), "model", self.slotInfo.modelFile)
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diffPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), "diff", self.slotInfo.diffModelFile)
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self.svc_model = SvcDDSP()
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self.svc_model.setVCParams(self.params)
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self.svc_model.update_model(modelPath, self.device)
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self.diff_model = DiffGtMel(device=self.device)
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self.diff_model.flush_model(diffPath, ddsp_config=self.svc_model.args)
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def update_settings(self, key: str, val: int | float | str):
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if key in self.settings.intData:
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val = int(val)
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setattr(self.settings, key, val)
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if key == "gpu":
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self.initialize()
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elif key in self.settings.floatData:
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setattr(self.settings, key, float(val))
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elif key in self.settings.strData:
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setattr(self.settings, key, str(val))
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else:
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return False
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return True
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def get_info(self):
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data = asdict(self.settings)
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return data
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def get_processing_sampling_rate(self):
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return self.svc_model.args.data.sampling_rate
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def generate_input(
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self,
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newData: AudioInOut,
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inputSize: int,
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crossfadeSize: int,
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solaSearchFrame: int = 0,
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):
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newData = newData.astype(np.float32) / 32768.0
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# newData = newData.astype(np.float32)
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if self.audio_buffer is not None:
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self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0) # 過去のデータに連結
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else:
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self.audio_buffer = newData
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convertSize = inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
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# if convertSize % self.hop_size != 0: # モデルの出力のホップサイズで切り捨てが発生するので補う。
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# convertSize = convertSize + (self.hop_size - (convertSize % self.hop_size))
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convertOffset = -1 * convertSize
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self.audio_buffer = self.audio_buffer[convertOffset:] # 変換対象の部分だけ抽出
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return (self.audio_buffer,)
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# def _onnx_inference(self, data):
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# if hasattr(self, "onnx_session") is False or self.onnx_session is None:
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# print("[Voice Changer] No onnx session.")
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# raise NoModeLoadedException("ONNX")
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# raise NoModeLoadedException("ONNX")
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def _pyTorch_inference(self, data):
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input_wav = data[0]
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_audio, _model_sr = self.svc_model.infer(
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input_wav,
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self.svc_model.args.data.sampling_rate,
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spk_id=self.settings.dstId,
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threhold=self.settings.threshold,
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pitch_adjust=self.settings.tran,
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use_spk_mix=False,
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spk_mix_dict=None,
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use_enhancer=True if self.settings.useEnhancer == 1 else False,
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pitch_extractor_type=self.settings.f0Detector,
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f0_min=50,
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f0_max=1100,
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# safe_prefix_pad_length=0, # TBD なにこれ？
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safe_prefix_pad_length=self.settings.extraConvertSize / self.svc_model.args.data.sampling_rate,
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diff_model=self.diff_model,
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diff_acc=self.settings.diffAcc, # TBD なにこれ？
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diff_spk_id=self.settings.diffSpkId,
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diff_use=True if self.settings.useDiff == 1 else False,
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# diff_use_dpm=True if self.settings.useDiffDpm == 1 else False, # TBD なにこれ？
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method=self.settings.diffMethod,
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k_step=self.settings.kStep, # TBD なにこれ？
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diff_silence=True if self.settings.useDiffSilence == 1 else False, # TBD なにこれ？
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)
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return _audio.cpu().numpy() * 32768.0
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def inference(self, data):
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if self.slotInfo.isONNX:
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audio = self._onnx_inference(data)
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else:
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audio = self._pyTorch_inference(data)
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return audio
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def __del__(self):
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remove_path = os.path.join("DDSP-SVC")
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sys.path = [x for x in sys.path if x.endswith(remove_path) is False]
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for key in list(sys.modules):
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val = sys.modules.get(key)
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try:
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file_path = val.__file__
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if file_path.find("DDSP-SVC" + os.path.sep) >= 0:
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# print("remove", key, file_path)
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sys.modules.pop(key)
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except: # type:ignore # noqa
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pass
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def get_model_current(self):
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return [
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]
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