WIP: refactoring
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wataru
parent
8121c3a849
commit
3ab5c9f3b8
@ -6,15 +6,17 @@ from voice_changer.RVC.ModelWrapper import ModelWrapper
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from Exceptions import NoModeLoadedException
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from voice_changer.RVC.RVCSettings import RVCSettings
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from voice_changer.utils.LoadModelParams import LoadModelParams
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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 dataclasses import asdict
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from typing import cast
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import numpy as np
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import torch
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from fairseq import checkpoint_utils
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from const import TMP_DIR
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from const import TMP_DIR # type:ignore
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# avoiding parse arg error in RVC
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@ -35,7 +37,10 @@ from .models import SynthesizerTrnMsNSFsid as SynthesizerTrnMsNSFsid_webui
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from .models import SynthesizerTrnMsNSFsidNono as SynthesizerTrnMsNSFsidNono_webui
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from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
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from voice_changer.RVC.custom_vc_infer_pipeline import VC
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from infer_pack.models import ( # type:ignore
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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)
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providers = [
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"OpenVINOExecutionProvider",
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@ -46,6 +51,8 @@ providers = [
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class RVC:
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audio_buffer: AudioInOut | None = None
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def __init__(self, params: VoiceChangerParams):
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self.initialLoad = True
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self.settings = RVCSettings()
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@ -234,7 +241,7 @@ class RVC:
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"[Voice Changer] Switching model..done",
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)
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def update_settings(self, key: str, val: any):
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def update_settings(self, key: str, val: int | float | str):
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if key == "onnxExecutionProvider" and self.onnx_session is not None:
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if val == "CUDAExecutionProvider":
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if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num:
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@ -251,10 +258,11 @@ class RVC:
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self.onnx_session.set_providers(providers=[val])
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if hasattr(self, "hubert_onnx"):
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self.hubert_onnx.set_providers(providers=[val])
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elif key == "onnxExecutionProvider" and self.onnx_session == None:
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elif key == "onnxExecutionProvider" and self.onnx_session is None:
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print("Onnx is not enabled. Please load model.")
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return False
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elif key in self.settings.intData:
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val = cast(int, val)
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if (
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key == "gpu"
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and val >= 0
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@ -303,14 +311,17 @@ class RVC:
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return self.settings.modelSamplingRate
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def generate_input(
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self, newData: any, inputSize: int, crossfadeSize: int, solaSearchFrame: int = 0
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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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if hasattr(self, "audio_buffer"):
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self.audio_buffer = np.concatenate(
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[self.audio_buffer, newData], 0
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) # 過去のデータに連結
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if self.audio_buffer is not None:
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# 過去のデータに連結
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self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)
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else:
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self.audio_buffer = newData
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@ -321,11 +332,13 @@ class RVC:
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if convertSize % 128 != 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 + (128 - (convertSize % 128))
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self.audio_buffer = self.audio_buffer[-1 * convertSize :] # 変換対象の部分だけ抽出
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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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crop = self.audio_buffer[
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-1 * (inputSize + crossfadeSize) : -1 * (crossfadeSize)
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] # 出力部分だけ切り出して音量を確認。(solaとの関係性について、現状は無考慮)
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# 出力部分だけ切り出して音量を確認。(TODO:段階的消音にする)
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cropOffset = -1 * (inputSize + crossfadeSize)
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cropEnd = -1 * (crossfadeSize)
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crop = self.audio_buffer[cropOffset:cropEnd]
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rms = np.sqrt(np.square(crop).mean(axis=0))
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vol = max(rms, self.prevVol * 0.0)
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self.prevVol = vol
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@ -333,7 +346,7 @@ class RVC:
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return (self.audio_buffer, convertSize, vol)
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def _onnx_inference(self, data):
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if hasattr(self, "onnx_session") == False or self.onnx_session == None:
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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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@ -361,13 +374,12 @@ class RVC:
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times = [0, 0, 0]
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f0_up_key = self.settings.tran
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f0_method = self.settings.f0Detector
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file_index = self.index_file if self.index_file != None else ""
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file_big_npy = self.feature_file if self.feature_file != None else ""
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file_index = self.index_file if self.index_file is not None else ""
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file_big_npy = self.feature_file if self.feature_file is not None else ""
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index_rate = self.settings.indexRatio
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if_f0 = 1 if self.settings.modelSlots[self.currentSlot].f0 else 0
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f0_file = None
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f0 = self.settings.modelSlots[self.currentSlot].f0
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embChannels = self.settings.modelSlots[self.currentSlot].embChannels
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audio_out = vc.pipeline(
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self.hubert_model,
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@ -427,8 +439,8 @@ class RVC:
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times = [0, 0, 0]
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f0_up_key = self.settings.tran
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f0_method = self.settings.f0Detector
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file_index = self.index_file if self.index_file != None else ""
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file_big_npy = self.feature_file if self.feature_file != None else ""
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file_index = self.index_file if self.index_file is not None else ""
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file_big_npy = self.feature_file if self.feature_file is not None else ""
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index_rate = self.settings.indexRatio
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if_f0 = 1 if self.settings.modelSlots[self.currentSlot].f0 else 0
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f0_file = None
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@ -482,7 +494,7 @@ class RVC:
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del self.onnx_session
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remove_path = os.path.join("RVC")
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sys.path = [x for x in sys.path if x.endswith(remove_path) == False]
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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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@ -492,20 +504,21 @@ class RVC:
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print("remove", key, file_path)
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sys.modules.pop(key)
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except Exception as e:
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print(e)
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pass
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def export2onnx(self):
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if hasattr(self, "net_g") == False or self.net_g == None:
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if hasattr(self, "net_g") is False or self.net_g is None:
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print("[Voice Changer] export2onnx, No pyTorch session.")
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return {"status": "ng", "path": f""}
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return {"status": "ng", "path": ""}
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pyTorchModelFile = self.settings.modelSlots[
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self.settings.modelSlotIndex
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].pyTorchModelFile # inference前にexportできるようにcurrentSlotではなくslot
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if pyTorchModelFile == None:
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if pyTorchModelFile is None:
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print("[Voice Changer] export2onnx, No pyTorch filepath.")
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return {"status": "ng", "path": f""}
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return {"status": "ng", "path": ""}
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import voice_changer.RVC.export2onnx as onnxExporter
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output_file = os.path.splitext(os.path.basename(pyTorchModelFile))[0] + ".onnx"
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