import sys import os 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], "MMVC_Client_v13", "python") sys.path.append(modulePath) else: modulePath = os.path.join("MMVC_Client_v13", "python") sys.path.append(modulePath) from dataclasses import dataclass, asdict import numpy as np import torch import onnxruntime import pyworld as pw from symbols import symbols from models import SynthesizerTrn from voice_changer.MMVCv13.TrainerFunctions import TextAudioSpeakerCollate, spectrogram_torch, load_checkpoint, get_hparams_from_file from Exceptions import NoModeLoadedException providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"] @dataclass class MMVCv13Settings(): gpu: int = 0 srcId: int = 0 dstId: int = 101 framework: str = "PyTorch" # PyTorch or ONNX pyTorchModelFile: str = "" onnxModelFile: str = "" configFile: str = "" # ↓mutableな物だけ列挙 intData = ["gpu", "srcId", "dstId"] floatData = [] strData = ["framework"] class MMVCv13: def __init__(self): self.settings = MMVCv13Settings() self.net_g = None self.onnx_session = None self.gpu_num = torch.cuda.device_count() self.text_norm = torch.LongTensor([0, 6, 0]) def loadModel(self, props): self.settings.configFile = props["files"]["configFilename"] self.hps = get_hparams_from_file(self.settings.configFile) self.settings.pyTorchModelFile = props["files"]["pyTorchModelFilename"] self.settings.onnxModelFile = props["files"]["onnxModelFilename"] # PyTorchモデル生成 if self.settings.pyTorchModelFile != None: self.net_g = SynthesizerTrn( len(symbols), self.hps.data.filter_length // 2 + 1, self.hps.train.segment_size // self.hps.data.hop_length, n_speakers=self.hps.data.n_speakers, **self.hps.model) self.net_g.eval() load_checkpoint(self.settings.pyTorchModelFile, self.net_g, None) # ONNXモデル生成 if self.settings.onnxModelFile != None: ort_options = onnxruntime.SessionOptions() ort_options.intra_op_num_threads = 8 self.onnx_session = onnxruntime.InferenceSession( self.settings.onnxModelFile, providers=providers ) return self.get_info() def update_settings(self, key: str, val: any): if key == "onnxExecutionProvider" and self.onnx_session != None: if val == "CUDAExecutionProvider": if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num: self.settings.gpu = 0 provider_options = [{'device_id': self.settings.gpu}] self.onnx_session.set_providers(providers=[val], provider_options=provider_options) else: self.onnx_session.set_providers(providers=[val]) elif key in self.settings.intData: setattr(self.settings, key, int(val)) if key == "gpu" and val >= 0 and val < self.gpu_num and self.onnx_session != None: providers = self.onnx_session.get_providers() print("Providers:", providers) if "CUDAExecutionProvider" in providers: provider_options = [{'device_id': self.settings.gpu}] self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options) elif key in self.settings.floatData: setattr(self.settings, key, float(val)) elif key in self.settings.strData: setattr(self.settings, key, str(val)) else: return False return True def get_info(self): data = asdict(self.settings) data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session != None else [] files = ["configFile", "pyTorchModelFile", "onnxModelFile"] for f in files: if data[f] != None and os.path.exists(data[f]): data[f] = os.path.basename(data[f]) else: data[f] = "" return data def get_processing_sampling_rate(self): if hasattr(self, "hps") == False: raise NoModeLoadedException("config") return self.hps.data.sampling_rate def _get_spec(self, audio: any): spec = spectrogram_torch(audio, self.hps.data.filter_length, self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length, center=False) spec = torch.squeeze(spec, 0) return spec def generate_input(self, newData: any, inputSize: int, crossfadeSize: int, solaSearchFrame: int = 0): newData = newData.astype(np.float32) / self.hps.data.max_wav_value if hasattr(self, "audio_buffer"): self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0) # 過去のデータに連結 else: self.audio_buffer = newData convertSize = inputSize + crossfadeSize + solaSearchFrame if convertSize < 8192: convertSize = 8192 if convertSize % self.hps.data.hop_length != 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 + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length)) self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出 audio = torch.FloatTensor(self.audio_buffer) audio_norm = audio.unsqueeze(0) # unsqueeze spec = self._get_spec(audio_norm) sid = torch.LongTensor([int(self.settings.srcId)]) data = (self.text_norm, spec, audio_norm, sid) data = TextAudioSpeakerCollate()([data]) return data def _onnx_inference(self, data): if hasattr(self, "onnx_session") == False or self.onnx_session == None: print("[Voice Changer] No ONNX session.") raise NoModeLoadedException("ONNX") x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data] sid_tgt1 = torch.LongTensor([self.settings.dstId]) # if spec.size()[2] >= 8: audio1 = self.onnx_session.run( ["audio"], { "specs": spec.numpy(), "lengths": spec_lengths.numpy(), "sid_src": sid_src.numpy(), "sid_tgt": sid_tgt1.numpy() })[0][0, 0] * self.hps.data.max_wav_value return audio1 def _pyTorch_inference(self, data): if hasattr(self, "net_g") == False or self.net_g == None: print("[Voice Changer] No pyTorch session.") raise NoModeLoadedException("pytorch") if self.settings.gpu < 0 or self.gpu_num == 0: dev = torch.device("cpu") else: dev = torch.device("cuda", index=self.settings.gpu) with torch.no_grad(): x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.to(dev) for x in data] sid_target = torch.LongTensor([self.settings.dstId]).to(dev) audio1 = (self.net_g.to(dev).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_target)[0, 0].data * self.hps.data.max_wav_value) result = audio1.float().cpu().numpy() return result def inference(self, data): if self.settings.framework == "ONNX": audio = self._onnx_inference(data) else: audio = self._pyTorch_inference(data) return audio def __del__(self): del self.net_g del self.onnx_session remove_path = os.path.join("MMVC_Client_v13", "python") sys.path = [x for x in sys.path if x.endswith(remove_path) == False] for key in list(sys.modules): val = sys.modules.get(key) try: file_path = val.__file__ if file_path.find(remove_path + os.path.sep) >= 0: print("remove", key, file_path) sys.modules.pop(key) except Exception as e: pass