mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-24 05:55:01 +03:00
346 lines
13 KiB
Python
346 lines
13 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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import torchaudio
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from data.ModelSlot import RVCModelSlot
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# avoiding parse arg error in RVC
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sys.argv = ["MMVCServerSIO.py"]
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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], "RVC")
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sys.path.append(modulePath)
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else:
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sys.path.append("RVC")
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from voice_changer.RVC.ModelSlotGenerator import (
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_setInfoByONNX,
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_setInfoByPytorch,
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)
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from voice_changer.RVC.RVCSettings import RVCSettings
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from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
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from voice_changer.utils.LoadModelParams import LoadModelParams2
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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.RVC.onnxExporter.export2onnx import export2onnx
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from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
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from voice_changer.RVC.pipeline.PipelineGenerator import createPipeline
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from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
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from voice_changer.RVC.pipeline.Pipeline import Pipeline
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from Exceptions import DeviceCannotSupportHalfPrecisionException, NoModeLoadedException
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class RVC:
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initialLoad: bool = True
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settings: RVCSettings = RVCSettings()
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pipeline: Pipeline | None = None
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deviceManager = DeviceManager.get_instance()
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audio_buffer: AudioInOut | None = None
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prevVol: float = 0
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params: VoiceChangerParams
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currentSlot: int = 0
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needSwitch: bool = False
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def __init__(self, params: VoiceChangerParams, slotInfo: RVCModelSlot):
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print("[Voice Changer][RVC] Creating instance ")
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EmbedderManager.initialize(params)
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self.params = params
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self.pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector)
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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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print("[Voice Changer][RVC] Initializing... ")
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# pipelineの生成
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self.pipeline = createPipeline(self.slotInfo, self.settings.gpu, self.settings.f0Detector)
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# その他の設定
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self.trans = self.slotInfo.defaultTune
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self.index_ratio = self.slotInfo.defaultIndexRatio
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self.protect = self.slotInfo.defaultProtect
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self.samplingRate = self.slotInfo.samplingRate
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print("[Voice Changer][RVC] Initializing... done")
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@classmethod
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def loadModel2(cls, props: LoadModelParams2):
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slotInfo: RVCModelSlot = RVCModelSlot()
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for file in props.files:
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if file.kind == "rvcModel":
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slotInfo.modelFile = file.name
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elif file.kind == "rvcIndex":
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slotInfo.indexFile = file.name
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slotInfo.defaultTune = 0
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slotInfo.defaultIndexRatio = 0
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slotInfo.defaultProtect = 0.5
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slotInfo.isONNX = slotInfo.modelFile.endswith(".onnx")
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slotInfo.name = os.path.splitext(os.path.basename(slotInfo.modelFile))[0]
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# slotInfo.iconFile = "/assets/icons/noimage.png"
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if slotInfo.isONNX:
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_setInfoByONNX(slotInfo)
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else:
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_setInfoByPytorch(slotInfo)
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return slotInfo
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def update_settings(self, key: str, val: int | float | str):
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print("[Voice Changer][RVC]: update_settings", key, val)
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if key in self.settings.intData:
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setattr(self.settings, key, int(val))
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if key == "gpu":
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self.deviceManager.setForceTensor(False)
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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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if key == "f0Detector" and self.pipeline is not None:
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pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector)
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self.pipeline.setPitchExtractor(pitchExtractor)
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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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if self.pipeline is not None:
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pipelineInfo = self.pipeline.getPipelineInfo()
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data["pipelineInfo"] = pipelineInfo
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return data
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def get_processing_sampling_rate(self):
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return self.settings.modelSamplingRate
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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 # RVCのモデルのサンプリングレートで入ってきている。（extraDataLength, Crossfade等も同じSRで処理）(★１)
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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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convertSize = inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
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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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# バッファがたまっていない場合はzeroで補う
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if self.audio_buffer.shape[0] < convertSize:
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self.audio_buffer = np.concatenate([np.zeros([convertSize]), self.audio_buffer])
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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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if self.pipeline is not None:
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device = self.pipeline.device
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else:
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device = torch.device("cpu")
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audio_buffer = torch.from_numpy(self.audio_buffer).to(device=device, dtype=torch.float32)
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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 = audio_buffer[cropOffset:cropEnd]
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vol = torch.sqrt(torch.square(crop).mean()).detach().cpu().numpy()
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vol = max(vol, self.prevVol * 0.0)
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self.prevVol = vol
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return (audio_buffer, convertSize, vol)
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def inference(self, data):
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# if self.settings.modelSlotIndex < 0:
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# print(
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# "[Voice Changer] wait for loading model...",
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# self.settings.modelSlotIndex,
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# self.currentSlot,
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# )
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# raise NoModeLoadedException("model_common")
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audio = data[0]
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convertSize = data[1]
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vol = data[2]
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if vol < self.settings.silentThreshold:
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return np.zeros(convertSize).astype(np.int16)
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audio = torchaudio.functional.resample(audio, self.settings.modelSamplingRate, 16000, rolloff=0.99)
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repeat = 1 if self.settings.rvcQuality else 0
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sid = 0
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f0_up_key = self.settings.tran
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index_rate = self.settings.indexRatio
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protect = self.settings.protect
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# if_f0 = 1 if self.settings.modelSlots[self.currentSlot].f0 else 0
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# embOutputLayer = self.settings.modelSlots[self.currentSlot].embOutputLayer
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# useFinalProj = self.settings.modelSlots[self.currentSlot].useFinalProj
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if_f0 = 1 if self.slotInfo.f0 else 0
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embOutputLayer = self.slotInfo.embOutputLayer
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useFinalProj = self.slotInfo.useFinalProj
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try:
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audio_out = self.pipeline.exec(
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sid,
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audio,
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f0_up_key,
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index_rate,
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if_f0,
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self.settings.extraConvertSize / self.settings.modelSamplingRate, # extaraDataSizeの秒数。RVCのモデルのサンプリングレートで処理(★１)。
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embOutputLayer,
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useFinalProj,
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repeat,
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protect,
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)
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result = audio_out.detach().cpu().numpy() * np.sqrt(vol)
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return result
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except DeviceCannotSupportHalfPrecisionException as e:
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print("[Device Manager] Device cannot support half precision. Fallback to float....")
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self.deviceManager.setForceTensor(True)
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self.prepareModel(self.settings.modelSlotIndex)
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raise e
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return
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def __del__(self):
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del self.pipeline
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print("---------- REMOVING ---------------")
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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) 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("RVC" + 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 Exception: # type:ignore
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# print(e)
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pass
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def export2onnx(self):
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allModelSlots = self.modelSlotManager.getAllSlotInfo()
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modelSlot = allModelSlots[self.settings.modelSlotIndex]
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if modelSlot.isONNX:
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print("[Voice Changer] export2onnx, No pyTorch filepath.")
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return {"status": "ng", "path": ""}
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output_file_simple = export2onnx(self.settings.gpu, modelSlot)
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return {
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"status": "ok",
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"path": f"/tmp/{output_file_simple}",
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"filename": output_file_simple,
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}
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def merge_models(self, request: str):
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print("[Voice Changer] MergeRequest:", request)
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# req: MergeModelRequest = MergeModelRequest.from_json(request)
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# merged = merge_model(req)
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# targetSlot = 0
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# if req.slot < 0:
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# # 最後尾のスロット番号を格納先とする。
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# allModelSlots = self.modelSlotManager.getAllSlotInfo()
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# targetSlot = len(allModelSlots) - 1
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# else:
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# targetSlot = req.slot
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# # いったんは、アップロードフォルダに格納する。（歴史的経緯）
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# # 後続のloadmodelを呼び出すことで永続化モデルフォルダに移動させられる。
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# storeDir = os.path.join(UPLOAD_DIR, f"{targetSlot}")
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# print("[Voice Changer] store merged model to:", storeDir)
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# os.makedirs(storeDir, exist_ok=True)
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# storeFile = os.path.join(storeDir, "merged.pth")
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# torch.save(merged, storeFile)
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# # loadmodelを呼び出して永続化モデルフォルダに移動させる。
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# params = {
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# "defaultTune": req.defaultTune,
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# "defaultIndexRatio": req.defaultIndexRatio,
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# "defaultProtect": req.defaultProtect,
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# "sampleId": "",
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# "files": {"rvcModel": storeFile},
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# }
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# props: LoadModelParams = LoadModelParams(slot=targetSlot, isHalf=True, params=params)
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# self.loadModel(props)
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# self.prepareModel(targetSlot)
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# self.settings.modelSlotIndex = targetSlot
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# self.currentSlot = self.settings.modelSlotIndex
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# def update_model_default(self):
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# # {"slot":9,"key":"name","val":"dogsdododg"}
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# self.modelSlotManager.update_model_info(
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# json.dumps(
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# {
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# "slot": self.currentSlot,
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# "key": "defaultTune",
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# "val": self.settings.tran,
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# }
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# )
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# )
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# self.modelSlotManager.update_model_info(
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# json.dumps(
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# {
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# "slot": self.currentSlot,
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# "key": "defaultIndexRatio",
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# "val": self.settings.indexRatio,
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# }
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# )
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# )
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# self.modelSlotManager.update_model_info(
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# json.dumps(
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# {
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# "slot": self.currentSlot,
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# "key": "defaultProtect",
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# "val": self.settings.protect,
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# }
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# )
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# )
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def get_model_current(self):
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return [
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{
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"key": "defaultTune",
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"val": self.settings.tran,
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},
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{
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"key": "defaultIndexRatio",
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"val": self.settings.indexRatio,
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},
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{
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"key": "defaultProtect",
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"val": self.settings.protect,
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},
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]
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# def update_model_info(self, newData: str):
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# self.modelSlotManager.update_model_info(newData)
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# def upload_model_assets(self, params: str):
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# self.modelSlotManager.store_model_assets(params)
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