import sys import os from dataclasses import asdict import numpy as np import torch import torchaudio from data.ModelSlot import RVCModelSlot # avoiding parse arg error in RVC sys.argv = ["MMVCServerSIO.py"] if sys.platform.startswith("darwin"): baseDir = [x for x in sys.path if x.endswith("Contents/MacOS")] if len(baseDir) != 1: print("baseDir should be only one ", baseDir) sys.exit() modulePath = os.path.join(baseDir[0], "RVC") sys.path.append(modulePath) else: sys.path.append("RVC") from voice_changer.RVC.RVCSettings import RVCSettings from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager from voice_changer.utils.VoiceChangerModel import AudioInOut, VoiceChangerModel from voice_changer.utils.VoiceChangerParams import VoiceChangerParams from voice_changer.RVC.onnxExporter.export2onnx import export2onnx from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager from voice_changer.RVC.pipeline.PipelineGenerator import createPipeline from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager from voice_changer.RVC.pipeline.Pipeline import Pipeline from Exceptions import DeviceCannotSupportHalfPrecisionException class RVC(VoiceChangerModel): def __init__(self, params: VoiceChangerParams, slotInfo: RVCModelSlot): print("[Voice Changer] [RVC] Creating instance ") self.deviceManager = DeviceManager.get_instance() EmbedderManager.initialize(params) self.settings = RVCSettings() self.params = params self.pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector) self.pipeline: Pipeline | None = None self.audio_buffer: AudioInOut | None = None self.prevVol = 0.0 self.slotInfo = slotInfo self.initialize() def initialize(self): print("[Voice Changer] [RVC] Initializing... ") # pipelineの生成 self.pipeline = createPipeline(self.slotInfo, self.settings.gpu, self.settings.f0Detector) # その他の設定 self.settings.tran = self.slotInfo.defaultTune self.settings.indexRatio = self.slotInfo.defaultIndexRatio self.settings.protect = self.slotInfo.defaultProtect print("[Voice Changer] [RVC] Initializing... done") def update_settings(self, key: str, val: int | float | str): print("[Voice Changer][RVC]: update_settings", key, val) if key in self.settings.intData: setattr(self.settings, key, int(val)) if key == "gpu": self.deviceManager.setForceTensor(False) self.initialize() elif key in self.settings.floatData: setattr(self.settings, key, float(val)) elif key in self.settings.strData: setattr(self.settings, key, str(val)) if key == "f0Detector" and self.pipeline is not None: pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector) self.pipeline.setPitchExtractor(pitchExtractor) else: return False return True def get_info(self): data = asdict(self.settings) if self.pipeline is not None: pipelineInfo = self.pipeline.getPipelineInfo() data["pipelineInfo"] = pipelineInfo return data def get_processing_sampling_rate(self): return self.slotInfo.samplingRate def generate_input( self, newData: AudioInOut, inputSize: int, crossfadeSize: int, solaSearchFrame: int = 0, ): 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) if self.audio_buffer is not None: # 過去のデータに連結 self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0) else: self.audio_buffer = newData convertSize = inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize 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。 convertSize = convertSize + (128 - (convertSize % 128)) # バッファがたまっていない場合はzeroで補う if self.audio_buffer.shape[0] < convertSize: self.audio_buffer = np.concatenate([np.zeros([convertSize]), self.audio_buffer]) convertOffset = -1 * convertSize self.audio_buffer = self.audio_buffer[convertOffset:] # 変換対象の部分だけ抽出 if self.pipeline is not None: device = self.pipeline.device else: device = torch.device("cpu") audio_buffer = torch.from_numpy(self.audio_buffer).to(device=device, dtype=torch.float32) # 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) cropOffset = -1 * (inputSize + crossfadeSize) cropEnd = -1 * (crossfadeSize) crop = audio_buffer[cropOffset:cropEnd] vol = torch.sqrt(torch.square(crop).mean()).detach().cpu().numpy() vol = max(vol, self.prevVol * 0.0) self.prevVol = vol return (audio_buffer, convertSize, vol) def inference(self, data): audio = data[0] convertSize = data[1] vol = data[2] if vol < self.settings.silentThreshold: return np.zeros(convertSize).astype(np.int16) audio = torchaudio.functional.resample(audio, self.slotInfo.samplingRate, 16000, rolloff=0.99) repeat = 1 if self.settings.rvcQuality else 0 sid = 0 f0_up_key = self.settings.tran index_rate = self.settings.indexRatio protect = self.settings.protect if_f0 = 1 if self.slotInfo.f0 else 0 embOutputLayer = self.slotInfo.embOutputLayer useFinalProj = self.slotInfo.useFinalProj try: audio_out = self.pipeline.exec( sid, audio, f0_up_key, index_rate, if_f0, self.settings.extraConvertSize / self.slotInfo.samplingRate, # extaraDataSizeの秒数。RVCのモデルのサンプリングレートで処理(★１)。 embOutputLayer, useFinalProj, repeat, protect, ) result = audio_out.detach().cpu().numpy() * np.sqrt(vol) return result except DeviceCannotSupportHalfPrecisionException as e: print("[Device Manager] Device cannot support half precision. Fallback to float....") self.deviceManager.setForceTensor(True) self.prepareModel(self.settings.modelSlotIndex) raise e return def __del__(self): del self.pipeline print("---------- REMOVING ---------------") remove_path = os.path.join("RVC") sys.path = [x for x in sys.path if x.endswith(remove_path) is False] for key in list(sys.modules): val = sys.modules.get(key) try: file_path = val.__file__ if file_path.find("RVC" + os.path.sep) >= 0: # print("remove", key, file_path) sys.modules.pop(key) except Exception: # type:ignore # print(e) pass def export2onnx(self): modelSlot = self.slotInfo if modelSlot.isONNX: print("[Voice Changer] export2onnx, No pyTorch filepath.") return {"status": "ng", "path": ""} output_file_simple = export2onnx(self.settings.gpu, modelSlot) return { "status": "ok", "path": f"/tmp/{output_file_simple}", "filename": output_file_simple, } def get_model_current(self): return [ { "key": "defaultTune", "val": self.settings.tran, }, { "key": "defaultIndexRatio", "val": self.settings.indexRatio, }, { "key": "defaultProtect", "val": self.settings.protect, }, ]