mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
253 lines
9.9 KiB
Python
253 lines
9.9 KiB
Python
import sys
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import os
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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], "MMVC_Client_v15", "python")
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sys.path.append(modulePath)
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else:
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sys.path.append("MMVC_Client_v15/python")
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from dataclasses import dataclass, asdict
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import numpy as np
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import torch
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import onnxruntime
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import pyworld as pw
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from models import SynthesizerTrn
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from voice_changer.MMVCv15.client_modules import convert_continuos_f0, spectrogram_torch, get_hparams_from_file, load_checkpoint
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providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
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@dataclass
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class MMVCv15Settings():
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gpu: int = 0
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srcId: int = 0
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dstId: int = 101
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f0Factor: float = 1.0
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f0Detector: str = "dio" # dio or harvest
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framework: str = "PyTorch" # PyTorch or ONNX
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pyTorchModelFile: str = ""
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onnxModelFile: str = ""
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configFile: str = ""
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# ↓mutableな物だけ列挙
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intData = ["gpu", "srcId", "dstId"]
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floatData = ["f0Factor"]
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strData = ["framework", "f0Detector"]
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class MMVCv15:
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def __init__(self):
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self.settings = MMVCv15Settings()
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self.net_g = None
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self.onnx_session = None
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self.gpu_num = torch.cuda.device_count()
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def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None):
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self.settings.configFile = config
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self.hps = get_hparams_from_file(config)
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if pyTorch_model_file != None:
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self.settings.pyTorchModelFile = pyTorch_model_file
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else:
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self.settings.pyTorchModelFile = ""
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if onnx_model_file:
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self.settings.onnxModelFile = onnx_model_file
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else:
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self.settings.onnxModelFile = ""
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print("self.settings.onnxModelFile::", self.settings.onnxModelFile)
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# PyTorchモデル生成
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self.net_g = SynthesizerTrn(
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spec_channels=self.hps.data.filter_length // 2 + 1,
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segment_size=self.hps.train.segment_size // self.hps.data.hop_length,
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inter_channels=self.hps.model.inter_channels,
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hidden_channels=self.hps.model.hidden_channels,
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upsample_rates=self.hps.model.upsample_rates,
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upsample_initial_channel=self.hps.model.upsample_initial_channel,
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upsample_kernel_sizes=self.hps.model.upsample_kernel_sizes,
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n_flow=self.hps.model.n_flow,
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dec_out_channels=1,
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dec_kernel_size=7,
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n_speakers=self.hps.data.n_speakers,
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gin_channels=self.hps.model.gin_channels,
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requires_grad_pe=self.hps.requires_grad.pe,
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requires_grad_flow=self.hps.requires_grad.flow,
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requires_grad_text_enc=self.hps.requires_grad.text_enc,
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requires_grad_dec=self.hps.requires_grad.dec
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)
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if pyTorch_model_file != None:
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self.net_g.eval()
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load_checkpoint(pyTorch_model_file, self.net_g, None)
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# ONNXモデル生成
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self.onxx_input_length = 8192
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if onnx_model_file != None:
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ort_options = onnxruntime.SessionOptions()
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ort_options.intra_op_num_threads = 8
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self.onnx_session = onnxruntime.InferenceSession(
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onnx_model_file,
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providers=providers
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)
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inputs_info = self.onnx_session.get_inputs()
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for i in inputs_info:
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# print("ONNX INPUT SHAPE", i.name, i.shape)
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if i.name == "sin":
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self.onxx_input_length = i.shape[2]
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return self.get_info()
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def update_setteings(self, key: str, val: any):
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if key == "onnxExecutionProvider" and self.settings.onnxModelFile != "": # self.onnx_session != 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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self.settings.gpu = 0
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provider_options = [{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=[val], provider_options=provider_options)
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else:
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self.onnx_session.set_providers(providers=[val])
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elif key in self.settings.intData:
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setattr(self.settings, key, int(val))
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if key == "gpu" and val >= 0 and val < self.gpu_num and self.settings.onnxModelFile != "": # self.onnx_session != None:
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providers = self.onnx_session.get_providers()
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print("Providers:", providers)
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if "CUDAExecutionProvider" in providers:
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provider_options = [{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options)
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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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data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.settings.onnxModelFile != "" else []
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files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
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for f in files:
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if data[f] != None and os.path.exists(data[f]):
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data[f] = os.path.basename(data[f])
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else:
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data[f] = ""
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return data
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def get_processing_sampling_rate(self):
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return self.hps.data.sampling_rate
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def _get_f0(self, detector: str, newData: any):
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audio_norm_np = newData.astype(np.float64)
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if detector == "dio":
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_f0, _time = pw.dio(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5)
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f0 = pw.stonemask(audio_norm_np, _f0, _time, self.hps.data.sampling_rate)
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else:
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f0, t = pw.harvest(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5, f0_floor=71.0, f0_ceil=1000.0)
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f0 = convert_continuos_f0(f0, int(audio_norm_np.shape[0] / self.hps.data.hop_length))
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f0 = torch.from_numpy(f0.astype(np.float32))
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return f0
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def _get_spec(self, newData: any):
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audio = torch.FloatTensor(newData)
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audio_norm = audio.unsqueeze(0) # unsqueeze
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spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
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self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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return spec
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def generate_input(self, newData: any, inputSize: int, crossfadeSize: int):
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newData = newData.astype(np.float32) / self.hps.data.max_wav_value
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if hasattr(self, "audio_buffer"):
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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
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if convertSize < 8192:
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convertSize = 8192
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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。
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convertSize = convertSize + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length))
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# ONNX は固定長
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if self.settings.framework == "ONNX":
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convertSize = self.onxx_input_length
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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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f0 = self._get_f0(self.settings.f0Detector, self.audio_buffer) # torch
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f0 = (f0 * self.settings.f0Factor).unsqueeze(0).unsqueeze(0)
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spec = self._get_spec(self.audio_buffer) # torch
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sid = torch.LongTensor([int(self.settings.srcId)])
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return [spec, f0, sid]
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def _onnx_inference(self, data):
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if self.settings.onnxModelFile == "":
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print("[Voice Changer] No ONNX session.")
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return np.zeros(1).astype(np.int16)
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spec, f0, sid_src = data
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spec = spec.unsqueeze(0)
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spec_lengths = torch.tensor([spec.size(2)])
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sid_tgt1 = torch.LongTensor([self.settings.dstId])
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sin, d = self.net_g.make_sin_d(f0)
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(d0, d1, d2, d3) = d
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audio1 = self.onnx_session.run(
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["audio"],
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{
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"specs": spec.numpy(),
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"lengths": spec_lengths.numpy(),
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"sin": sin.numpy(),
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"d0": d0.numpy(),
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"d1": d1.numpy(),
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"d2": d2.numpy(),
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"d3": d3.numpy(),
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"sid_src": sid_src.numpy(),
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"sid_tgt": sid_tgt1.numpy()
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})[0][0, 0] * self.hps.data.max_wav_value
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return audio1
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def _pyTorch_inference(self, data):
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if self.settings.pyTorchModelFile == "":
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print("[Voice Changer] No pyTorch session.")
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return np.zeros(1).astype(np.int16)
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if self.settings.gpu < 0 or self.gpu_num == 0:
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dev = torch.device("cpu")
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else:
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dev = torch.device("cuda", index=self.settings.gpu)
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with torch.no_grad():
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spec, f0, sid_src = data
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spec = spec.unsqueeze(0).to(dev)
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spec_lengths = torch.tensor([spec.size(2)]).to(dev)
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f0 = f0.to(dev)
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sid_src = sid_src.to(dev)
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sid_target = torch.LongTensor([self.settings.dstId]).to(dev)
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audio1 = self.net_g.to(dev).voice_conversion(spec, spec_lengths, f0, sid_src, sid_target)[0, 0].data * self.hps.data.max_wav_value
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result = audio1.float().cpu().numpy()
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return result
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def inference(self, data):
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if self.settings.framework == "ONNX":
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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 destroy(self):
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del self.net_g
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del self.onnx_session
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