mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-03-13 19:34:02 +03:00
WIP: integrate vcs to new gui 2
This commit is contained in:
wataru
parent
e6b191abd2
commit
fa7894de50
@ -2,7 +2,6 @@ import sys
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import os
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from data.ModelSlot import MMVCv13ModelSlot
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from voice_changer.utils.LoadModelParams import LoadModelParams, LoadModelParams2
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from voice_changer.utils.VoiceChangerModel import AudioInOut
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if sys.platform.startswith("darwin"):
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@ -22,8 +21,10 @@ import numpy as np
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import torch
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import onnxruntime
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from symbols import symbols # type:ignore
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from models import SynthesizerTrn # type:ignore
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# from symbols import symbols # type:ignore
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# from models import SynthesizerTrn # type:ignore
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from voice_changer.MMVCv13.models.models import SynthesizerTrn
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from voice_changer.MMVCv13.models.symbols import symbols
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from voice_changer.MMVCv13.TrainerFunctions import (
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TextAudioSpeakerCollate,
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spectrogram_torch,
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@ -40,21 +41,15 @@ class MMVCv13Settings:
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srcId: int = 0
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dstId: int = 101
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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: list[str] = field(default_factory=lambda: [])
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strData = ["framework"]
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strData: list[str] = field(default_factory=lambda: [])
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class MMVCv13:
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audio_buffer: AudioInOut | None = None
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def __init__(self):
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def __init__(self, slotInfo: MMVCv13ModelSlot):
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print("[Voice Changer] [MMVCv13] Creating instance ")
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self.settings = MMVCv13Settings()
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self.net_g = None
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self.onnx_session = None
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@ -62,43 +57,35 @@ class MMVCv13:
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self.gpu_num = torch.cuda.device_count()
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self.text_norm = torch.LongTensor([0, 6, 0])
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def loadModel(self, props: LoadModelParams):
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params = props.params
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self.audio_buffer: AudioInOut | None = None
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self.slotInfo = slotInfo
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self.initialize()
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self.settings.configFile = params["files"]["mmvcv13Config"]
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self.hps = get_hparams_from_file(self.settings.configFile)
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def initialize(self):
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print("[Voice Changer] [MMVCv13] Initializing... ")
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modelFile = params["files"]["mmvcv13Model"]
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if modelFile.endswith(".onnx"):
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self.settings.pyTorchModelFile = None
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self.settings.onnxModelFile = modelFile
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else:
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self.settings.pyTorchModelFile = modelFile
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self.settings.onnxModelFile = None
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# PyTorchモデル生成
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if self.settings.pyTorchModelFile is not None:
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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)
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self.net_g.eval()
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load_checkpoint(self.settings.pyTorchModelFile, self.net_g, None)
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# ONNXモデル生成
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if self.settings.onnxModelFile is not None:
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# ort_options = onnxruntime.SessionOptions()
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# ort_options.intra_op_num_threads = 8
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# ort_options.execution_mode = ort_options.ExecutionMode.ORT_PARALLEL
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# ort_options.inter_op_num_threads = 8
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self.hps = get_hparams_from_file(self.slotInfo.configFile)
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if self.slotInfo.isONNX:
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providers, options = self.getOnnxExecutionProvider()
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self.onnx_session = onnxruntime.InferenceSession(
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self.settings.onnxModelFile,
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self.slotInfo.modelFile,
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providers=providers,
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provider_options=options,
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)
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return self.get_info()
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else:
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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)
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self.net_g.eval()
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load_checkpoint(self.slotInfo.modelFile, self.net_g, None)
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# その他の設定
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self.settings.srcId = self.slotInfo.srcId
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self.settings.dstId = self.slotInfo.dstId
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print("[Voice Changer] [MMVCv13] Initializing... done")
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def getOnnxExecutionProvider(self):
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availableProviders = onnxruntime.get_available_providers()
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if self.settings.gpu >= 0 and "CUDAExecutionProvider" in availableProviders:
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devNum = torch.cuda.device_count()
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if self.settings.gpu >= 0 and "CUDAExecutionProvider" in availableProviders and devNum > 0:
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return ["CUDAExecutionProvider"], [{"device_id": self.settings.gpu}]
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elif self.settings.gpu >= 0 and "DmlExecutionProvider" in availableProviders:
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return ["DmlExecutionProvider"], [{}]
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@ -111,21 +98,15 @@ class MMVCv13:
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}
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]
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def isOnnx(self):
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if self.settings.onnxModelFile is not None:
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return True
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else:
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return False
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def update_settings(self, key: str, val: int | float | str):
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if key in self.settings.intData:
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val = int(val)
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setattr(self.settings, key, val)
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if key == "gpu" and self.isOnnx():
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if key == "gpu" and self.slotInfo.isONNX:
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providers, options = self.getOnnxExecutionProvider()
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self.onnx_session = onnxruntime.InferenceSession(
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self.settings.onnxModelFile,
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self.slotInfo.modelFile,
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providers=providers,
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provider_options=options,
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)
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@ -150,13 +131,6 @@ class MMVCv13:
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data = asdict(self.settings)
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data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session is not None else []
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files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
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for f in files:
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if data[f] is not 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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@ -211,7 +185,7 @@ class MMVCv13:
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return data
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def _onnx_inference(self, data):
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if hasattr(self, "onnx_session") is False or self.onnx_session is None:
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if 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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@ -254,24 +228,12 @@ class MMVCv13:
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return result
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def inference(self, data):
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if self.isOnnx():
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if self.slotInfo.isONNX:
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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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@classmethod
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def loadModel2(cls, props: LoadModelParams2):
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slotInfo: MMVCv13ModelSlot = MMVCv13ModelSlot()
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for file in props.files:
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if file.kind == "mmvcv13Model":
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slotInfo.modelFile = file.name
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elif file.kind == "mmvcv13Config":
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slotInfo.configFile = file.name
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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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return slotInfo
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def __del__(self):
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del self.net_g
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del self.onnx_session
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19
server/voice_changer/MMVCv13/MMVCv13ModelSlotGenerator.py
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19
server/voice_changer/MMVCv13/MMVCv13ModelSlotGenerator.py
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@ -0,0 +1,19 @@
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import os
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from data.ModelSlot import MMVCv13ModelSlot
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from voice_changer.utils.LoadModelParams import LoadModelParams
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from voice_changer.utils.ModelSlotGenerator import ModelSlotGenerator
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class MMVCv13ModelSlotGenerator(ModelSlotGenerator):
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@classmethod
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def loadModel(cls, props: LoadModelParams):
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slotInfo: MMVCv13ModelSlot = MMVCv13ModelSlot()
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for file in props.files:
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if file.kind == "mmvcv13Model":
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slotInfo.modelFile = file.name
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elif file.kind == "mmvcv13Config":
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slotInfo.configFile = file.name
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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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return slotInfo
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27
server/voice_changer/MMVCv13/models/commons.py
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27
server/voice_changer/MMVCv13/models/commons.py
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@ -0,0 +1,27 @@
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import torch
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size * dilation - dilation) / 2)
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a + input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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def sequence_mask(length, max_length=None):
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if max_length is None:
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max_length = length.max()
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x = torch.arange(max_length, dtype=length.dtype, device=length.device)
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return x.unsqueeze(0) < length.unsqueeze(1)
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166
server/voice_changer/MMVCv13/models/models.py
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166
server/voice_changer/MMVCv13/models/models.py
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@ -0,0 +1,166 @@
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import torch
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from torch import nn
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from torch.nn import functional as F
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from .modules import ResidualCouplingLayer, Flip, WN, ResBlock1, ResBlock2, LRELU_SLOPE
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from torch.nn import Conv1d, ConvTranspose1d
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from torch.nn.utils import weight_norm, remove_weight_norm
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from .commons import init_weights, sequence_mask
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class ResidualCouplingBlock(nn.Module):
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def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0):
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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for i in range(n_flows):
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self.flows.append(ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
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self.flows.append(Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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class PosteriorEncoder(nn.Module):
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def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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# self.randn = torch.randn(1, 1, 1) # ダミーで初期化
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def forward(self, x, x_lengths, g=None):
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x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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# if self.randn.size() != m.size(): # m の形が違う時だけ生成
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self.randn = torch.randn_like(m)
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z = (m + self.randn * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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class Generator(torch.nn.Module):
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def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
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super(Generator, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
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resblock = ResBlock1 if resblock == "1" else ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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self.ups.append(weight_norm(ConvTranspose1d(upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2)))
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
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self.resblocks.append(resblock(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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self.ups.apply(init_weights)
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if gin_channels != 0:
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# self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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gin_channels = 0
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def forward(self, x, g=None):
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x = self.conv_pre(x)
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if g is not None:
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# x = x + self.cond(g)
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g = None
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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, LRELU_SLOPE)
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x = self.ups[i](x)
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xs = None
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for j in range(self.num_kernels):
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if xs is None:
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xs = self.resblocks[i * self.num_kernels + j](x)
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else:
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xs += self.resblocks[i * self.num_kernels + j](x)
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x = xs / self.num_kernels
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x = F.leaky_relu(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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print("Removing weight norm...")
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for l in self.ups:
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remove_weight_norm(l)
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for l in self.resblocks:
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l.remove_weight_norm()
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class SynthesizerTrn(nn.Module):
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"""
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Synthesizer for Training
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"""
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def __init__(self, n_vocab, spec_channels, segment_size, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, n_flow, n_speakers=0, gin_channels=0, use_sdp=True, **kwargs):
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super().__init__()
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self.n_vocab = n_vocab
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self.spec_channels = spec_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.resblock = resblock
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.upsample_rates = upsample_rates
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self.upsample_initial_channel = upsample_initial_channel
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self.upsample_kernel_sizes = upsample_kernel_sizes
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self.segment_size = segment_size
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self.n_speakers = n_speakers
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self.gin_channels = gin_channels
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self.use_sdp = use_sdp
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self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
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self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, n_flows=n_flow, gin_channels=gin_channels)
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if n_speakers > 1:
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self.emb_g = nn.Embedding(n_speakers, gin_channels)
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def forward(self, y, y_lengths, sid_src, sid_tgt):
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return self.voice_conversion(y, y_lengths, sid_src, sid_tgt)
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def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
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assert self.n_speakers > 0, "n_speakers have to be larger than 0."
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g_src = self.emb_g(sid_src).unsqueeze(-1)
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g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
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z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
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z_p = self.flow(z, y_mask, g=g_src)
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z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
||||
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
||||
return o_hat
|
||||
186
server/voice_changer/MMVCv13/models/modules.py
Normal file
186
server/voice_changer/MMVCv13/models/modules.py
Normal file
@ -0,0 +1,186 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from torch.nn import Conv1d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm
|
||||
|
||||
from .commons import init_weights, get_padding, fused_add_tanh_sigmoid_multiply
|
||||
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class WN(torch.nn.Module):
|
||||
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
||||
super(WN, self).__init__()
|
||||
assert kernel_size % 2 == 1
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = (kernel_size,)
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.in_layers = torch.nn.ModuleList()
|
||||
self.res_skip_layers = torch.nn.ModuleList()
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if gin_channels != 0:
|
||||
cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
|
||||
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
|
||||
|
||||
for i in range(n_layers):
|
||||
dilation = dilation_rate**i
|
||||
padding = int((kernel_size * dilation - dilation) / 2)
|
||||
in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding)
|
||||
in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
|
||||
self.in_layers.append(in_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * hidden_channels
|
||||
else:
|
||||
res_skip_channels = hidden_channels
|
||||
|
||||
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
||||
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
|
||||
self.res_skip_layers.append(res_skip_layer)
|
||||
|
||||
def forward(self, x, x_mask, g=None, **kwargs):
|
||||
output = torch.zeros_like(x)
|
||||
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
||||
|
||||
if g is not None:
|
||||
g = self.cond_layer(g)
|
||||
|
||||
for i in range(self.n_layers):
|
||||
x_in = self.in_layers[i](x)
|
||||
if g is not None:
|
||||
cond_offset = i * 2 * self.hidden_channels
|
||||
g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
|
||||
else:
|
||||
g_l = torch.zeros_like(x_in)
|
||||
|
||||
acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
|
||||
acts = self.drop(acts)
|
||||
|
||||
res_skip_acts = self.res_skip_layers[i](acts)
|
||||
if i < self.n_layers - 1:
|
||||
res_acts = res_skip_acts[:, : self.hidden_channels, :]
|
||||
x = (x + res_acts) * x_mask
|
||||
output = output + res_skip_acts[:, self.hidden_channels :, :]
|
||||
else:
|
||||
output = output + res_skip_acts
|
||||
return output * x_mask
|
||||
|
||||
def remove_weight_norm(self):
|
||||
if self.gin_channels != 0:
|
||||
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
||||
for l in self.in_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
for l in self.res_skip_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2])))])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)))])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.convs = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1])))])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class Flip(nn.Module):
|
||||
def forward(self, x, *args, reverse=False, **kwargs):
|
||||
x = torch.flip(x, [1])
|
||||
if not reverse:
|
||||
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class ResidualCouplingLayer(nn.Module):
|
||||
def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=0, mean_only=False):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1, 2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
1
server/voice_changer/MMVCv13/models/readme.txt
Normal file
1
server/voice_changer/MMVCv13/models/readme.txt
Normal file
@ -0,0 +1 @@
|
||||
modules in this folder from https://github.com/isletennos/MMVC_Client.git at 04f3fec4fd82dea6657026ec4e1cd80fb29a415c
|
||||
64
server/voice_changer/MMVCv13/models/symbols.py
Normal file
64
server/voice_changer/MMVCv13/models/symbols.py
Normal file
@ -0,0 +1,64 @@
|
||||
""" The following information was added with reference to https://github.com/jaywalnut310/vits/tree/1eef52ed50743f77fca9ff6773ba673497f6bf9d """
|
||||
""" from https://github.com/keithito/tacotron """
|
||||
|
||||
"""
|
||||
Defines the set of symbols used in text input to the model.
|
||||
"""
|
||||
_pad = "_"
|
||||
_punctuation = ';:,.!?¡¿—…"«»“” '
|
||||
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
|
||||
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
|
||||
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
|
||||
|
||||
# Special symbol ids
|
||||
SPACE_ID = symbols.index(" ")
|
||||
|
||||
symbols = [
|
||||
"A",
|
||||
"E",
|
||||
"I",
|
||||
"N",
|
||||
"O",
|
||||
"U",
|
||||
"a",
|
||||
"b",
|
||||
"by",
|
||||
"ch",
|
||||
"cl",
|
||||
"d",
|
||||
"dy",
|
||||
"e",
|
||||
"f",
|
||||
"g",
|
||||
"gy",
|
||||
"h",
|
||||
"hy",
|
||||
"i",
|
||||
"j",
|
||||
"k",
|
||||
"ky",
|
||||
"m",
|
||||
"my",
|
||||
"n",
|
||||
"ny",
|
||||
"o",
|
||||
"p",
|
||||
"py",
|
||||
"r",
|
||||
"ry",
|
||||
"s",
|
||||
"sh",
|
||||
"t",
|
||||
"ts",
|
||||
"ty",
|
||||
"u",
|
||||
"v",
|
||||
"w",
|
||||
"y",
|
||||
"z",
|
||||
"pau",
|
||||
"sil",
|
||||
]
|
||||
@ -1,8 +1,6 @@
|
||||
import sys
|
||||
import os
|
||||
from data.ModelSlot import MMVCv15ModelSlot
|
||||
|
||||
from voice_changer.utils.LoadModelParams import LoadModelParams, LoadModelParams2
|
||||
from voice_changer.utils.VoiceChangerModel import AudioInOut
|
||||
|
||||
if sys.platform.startswith("darwin"):
|
||||
@ -49,42 +47,29 @@ class MMVCv15Settings:
|
||||
f0Factor: float = 1.0
|
||||
f0Detector: str = "dio" # dio or harvest
|
||||
|
||||
framework: str = "PyTorch" # PyTorch or ONNX
|
||||
pyTorchModelFile: str = ""
|
||||
onnxModelFile: str = ""
|
||||
configFile: str = ""
|
||||
|
||||
# ↓mutableな物だけ列挙
|
||||
intData = ["gpu", "srcId", "dstId"]
|
||||
floatData = ["f0Factor"]
|
||||
strData = ["framework", "f0Detector"]
|
||||
strData = ["f0Detector"]
|
||||
|
||||
|
||||
class MMVCv15:
|
||||
audio_buffer: AudioInOut | None = None
|
||||
|
||||
def __init__(self):
|
||||
def __init__(self, slotInfo: MMVCv15ModelSlot):
|
||||
print("[Voice Changer] [MMVCv15] Creating instance ")
|
||||
self.settings = MMVCv15Settings()
|
||||
self.net_g = None
|
||||
self.onnx_session = None
|
||||
self.onnx_session: onnxruntime.InferenceSession | None = None
|
||||
|
||||
self.gpu_num = torch.cuda.device_count()
|
||||
|
||||
def loadModel(self, props: LoadModelParams):
|
||||
params = props.params
|
||||
self.slotInfo = slotInfo
|
||||
self.audio_buffer: AudioInOut | None = None
|
||||
self.initialize()
|
||||
|
||||
self.settings.configFile = params["files"]["mmvcv15Config"]
|
||||
self.hps = get_hparams_from_file(self.settings.configFile)
|
||||
def initialize(self):
|
||||
print("[Voice Changer] [MMVCv15] Initializing... ")
|
||||
self.hps = get_hparams_from_file(self.slotInfo.configFile)
|
||||
|
||||
modelFile = params["files"]["mmvcv15Model"]
|
||||
if modelFile.endswith(".onnx"):
|
||||
self.settings.pyTorchModelFile = None
|
||||
self.settings.onnxModelFile = modelFile
|
||||
else:
|
||||
self.settings.pyTorchModelFile = modelFile
|
||||
self.settings.onnxModelFile = None
|
||||
|
||||
# PyTorchモデル生成
|
||||
self.net_g = SynthesizerTrn(
|
||||
spec_channels=self.hps.data.filter_length // 2 + 1,
|
||||
segment_size=self.hps.train.segment_size // self.hps.data.hop_length,
|
||||
@ -103,18 +88,12 @@ class MMVCv15:
|
||||
requires_grad_text_enc=self.hps.requires_grad.text_enc,
|
||||
requires_grad_dec=self.hps.requires_grad.dec,
|
||||
)
|
||||
if self.settings.pyTorchModelFile is not None:
|
||||
self.settings.framework = "PyTorch"
|
||||
self.net_g.eval()
|
||||
load_checkpoint(self.settings.pyTorchModelFile, self.net_g, None)
|
||||
|
||||
# ONNXモデル生成
|
||||
self.onxx_input_length = 8192
|
||||
if self.settings.onnxModelFile is not None:
|
||||
self.settings.framework = "ONNX"
|
||||
if self.slotInfo.isONNX:
|
||||
self.onxx_input_length = 8192
|
||||
providers, options = self.getOnnxExecutionProvider()
|
||||
self.onnx_session = onnxruntime.InferenceSession(
|
||||
self.settings.onnxModelFile,
|
||||
self.slotInfo.modelFile,
|
||||
providers=providers,
|
||||
provider_options=options,
|
||||
)
|
||||
@ -123,11 +102,21 @@ class MMVCv15:
|
||||
# print("ONNX INPUT SHAPE", i.name, i.shape)
|
||||
if i.name == "sin":
|
||||
self.onxx_input_length = i.shape[2]
|
||||
return self.get_info()
|
||||
else:
|
||||
self.net_g.eval()
|
||||
load_checkpoint(self.slotInfo.modelFile, self.net_g, None)
|
||||
|
||||
# その他の設定
|
||||
self.settings.srcId = self.slotInfo.srcId
|
||||
self.settings.dstId = self.slotInfo.dstId
|
||||
self.settings.f0Factor = self.slotInfo.f0Factor
|
||||
|
||||
print("[Voice Changer] [MMVCv15] Initializing... done")
|
||||
|
||||
def getOnnxExecutionProvider(self):
|
||||
availableProviders = onnxruntime.get_available_providers()
|
||||
if self.settings.gpu >= 0 and "CUDAExecutionProvider" in availableProviders:
|
||||
devNum = torch.cuda.device_count()
|
||||
if self.settings.gpu >= 0 and "CUDAExecutionProvider" in availableProviders and devNum > 0:
|
||||
return ["CUDAExecutionProvider"], [{"device_id": self.settings.gpu}]
|
||||
elif self.settings.gpu >= 0 and "DmlExecutionProvider" in availableProviders:
|
||||
return ["DmlExecutionProvider"], [{}]
|
||||
@ -140,20 +129,14 @@ class MMVCv15:
|
||||
}
|
||||
]
|
||||
|
||||
def isOnnx(self):
|
||||
if self.settings.onnxModelFile is not None:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def update_settings(self, key: str, val: int | float | str):
|
||||
if key in self.settings.intData:
|
||||
val = int(val)
|
||||
setattr(self.settings, key, val)
|
||||
if key == "gpu" and self.isOnnx():
|
||||
if key == "gpu" and self.slotInfo.isONNX:
|
||||
providers, options = self.getOnnxExecutionProvider()
|
||||
self.onnx_session = onnxruntime.InferenceSession(
|
||||
self.settings.onnxModelFile,
|
||||
self.slotInfo.modelFile,
|
||||
providers=providers,
|
||||
provider_options=options,
|
||||
)
|
||||
@ -174,12 +157,6 @@ class MMVCv15:
|
||||
data = asdict(self.settings)
|
||||
|
||||
data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.settings.onnxModelFile != "" and self.settings.onnxModelFile is not None else []
|
||||
files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
|
||||
for f in files:
|
||||
if data[f] is not None and os.path.exists(data[f]):
|
||||
data[f] = os.path.basename(data[f])
|
||||
else:
|
||||
data[f] = ""
|
||||
|
||||
return data
|
||||
|
||||
@ -241,7 +218,7 @@ class MMVCv15:
|
||||
convertSize = convertSize + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length))
|
||||
|
||||
# ONNX は固定長
|
||||
if self.settings.framework == "ONNX":
|
||||
if self.slotInfo.isONNX:
|
||||
convertSize = self.onxx_input_length
|
||||
|
||||
convertOffset = -1 * convertSize
|
||||
@ -286,10 +263,6 @@ class MMVCv15:
|
||||
return audio1
|
||||
|
||||
def _pyTorch_inference(self, data):
|
||||
if self.settings.pyTorchModelFile == "" or self.settings.pyTorchModelFile is 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:
|
||||
@ -309,7 +282,7 @@ class MMVCv15:
|
||||
|
||||
def inference(self, data):
|
||||
try:
|
||||
if self.isOnnx():
|
||||
if self.slotInfo.isONNX:
|
||||
audio = self._onnx_inference(data)
|
||||
else:
|
||||
audio = self._pyTorch_inference(data)
|
||||
@ -318,18 +291,6 @@ class MMVCv15:
|
||||
print(_e)
|
||||
raise ONNXInputArgumentException()
|
||||
|
||||
@classmethod
|
||||
def loadModel2(cls, props: LoadModelParams2):
|
||||
slotInfo: MMVCv15ModelSlot = MMVCv15ModelSlot()
|
||||
for file in props.files:
|
||||
if file.kind == "mmvcv15Model":
|
||||
slotInfo.modelFile = file.name
|
||||
elif file.kind == "mmvcv15Config":
|
||||
slotInfo.configFile = file.name
|
||||
slotInfo.isONNX = slotInfo.modelFile.endswith(".onnx")
|
||||
slotInfo.name = os.path.splitext(os.path.basename(slotInfo.modelFile))[0]
|
||||
return slotInfo
|
||||
|
||||
def __del__(self):
|
||||
del self.net_g
|
||||
del self.onnx_session
|
||||
|
||||
19
server/voice_changer/MMVCv15/MMVCv15ModelSlotGenerator.py
Normal file
19
server/voice_changer/MMVCv15/MMVCv15ModelSlotGenerator.py
Normal file
@ -0,0 +1,19 @@
|
||||
import os
|
||||
|
||||
from data.ModelSlot import MMVCv15ModelSlot
|
||||
from voice_changer.utils.LoadModelParams import LoadModelParams
|
||||
from voice_changer.utils.ModelSlotGenerator import ModelSlotGenerator
|
||||
|
||||
|
||||
class MMVCv15ModelSlotGenerator(ModelSlotGenerator):
|
||||
@classmethod
|
||||
def loadModel(cls, props: LoadModelParams):
|
||||
slotInfo: MMVCv15ModelSlot = MMVCv15ModelSlot()
|
||||
for file in props.files:
|
||||
if file.kind == "mmvcv15Model":
|
||||
slotInfo.modelFile = file.name
|
||||
elif file.kind == "mmvcv15Config":
|
||||
slotInfo.configFile = file.name
|
||||
slotInfo.isONNX = slotInfo.modelFile.endswith(".onnx")
|
||||
slotInfo.name = os.path.splitext(os.path.basename(slotInfo.modelFile))[0]
|
||||
return slotInfo
|
||||
@ -35,26 +35,17 @@ from Exceptions import DeviceCannotSupportHalfPrecisionException
|
||||
|
||||
|
||||
class RVC(VoiceChangerModel):
|
||||
initialLoad: bool = True
|
||||
settings: RVCSettings = RVCSettings()
|
||||
|
||||
pipeline: Pipeline | None = None
|
||||
|
||||
deviceManager = DeviceManager.get_instance()
|
||||
|
||||
audio_buffer: AudioInOut | None = None
|
||||
prevVol: float = 0
|
||||
params: VoiceChangerParams
|
||||
currentSlot: int = 0
|
||||
needSwitch: bool = False
|
||||
|
||||
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()
|
||||
|
||||
@ -116,14 +116,14 @@ class VoiceChangerManager(ServerDeviceCallbacks):
|
||||
slotInfo = RVCModelSlotGenerator.loadModel(params)
|
||||
self.modelSlotManager.save_model_slot(params.slot, slotInfo)
|
||||
elif params.voiceChangerType == "MMVCv13":
|
||||
from voice_changer.MMVCv13.MMVCv13 import MMVCv13
|
||||
from voice_changer.MMVCv13.MMVCv13ModelSlotGenerator import MMVCv13ModelSlotGenerator
|
||||
|
||||
slotInfo = MMVCv13.loadModel(params)
|
||||
slotInfo = MMVCv13ModelSlotGenerator.loadModel(params)
|
||||
self.modelSlotManager.save_model_slot(params.slot, slotInfo)
|
||||
elif params.voiceChangerType == "MMVCv15":
|
||||
from voice_changer.MMVCv15.MMVCv15 import MMVCv15
|
||||
from voice_changer.MMVCv15.MMVCv15ModelSlotGenerator import MMVCv15ModelSlotGenerator
|
||||
|
||||
slotInfo = MMVCv15.loadModel(params)
|
||||
slotInfo = MMVCv15ModelSlotGenerator.loadModel(params)
|
||||
self.modelSlotManager.save_model_slot(params.slot, slotInfo)
|
||||
elif params.voiceChangerType == "so-vits-svc-40":
|
||||
from voice_changer.SoVitsSvc40.SoVitsSvc40 import SoVitsSvc40
|
||||
@ -174,7 +174,20 @@ class VoiceChangerManager(ServerDeviceCallbacks):
|
||||
self.voiceChangerModel = RVC(self.params, slotInfo)
|
||||
self.voiceChanger = VoiceChanger(self.params)
|
||||
self.voiceChanger.setModel(self.voiceChangerModel)
|
||||
elif slotInfo.voiceChangerType == "MMVCv13":
|
||||
print("................MMVCv13")
|
||||
from voice_changer.MMVCv13.MMVCv13 import MMVCv13
|
||||
|
||||
self.voiceChangerModel = MMVCv13(slotInfo)
|
||||
self.voiceChanger = VoiceChanger(self.params)
|
||||
self.voiceChanger.setModel(self.voiceChangerModel)
|
||||
elif slotInfo.voiceChangerType == "MMVCv15":
|
||||
print("................MMVCv15")
|
||||
from voice_changer.MMVCv15.MMVCv15 import MMVCv15
|
||||
|
||||
self.voiceChangerModel = MMVCv15(slotInfo)
|
||||
self.voiceChanger = VoiceChanger(self.params)
|
||||
self.voiceChanger.setModel(self.voiceChangerModel)
|
||||
else:
|
||||
print(f"[Voice Changer] unknown voice changer model: {slotInfo.voiceChangerType}")
|
||||
del self.voiceChangerModel
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user