mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
182 lines
7.8 KiB
Python
182 lines
7.8 KiB
Python
import os
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from const import EnumInferenceTypes
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from dataclasses import asdict
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import torch
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import onnxruntime
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import json
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from data.ModelSlot import RVCModelSlot
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from voice_changer.VoiceChangerParamsManager import VoiceChangerParamsManager
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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 RVCModelSlotGenerator(ModelSlotGenerator):
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@classmethod
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def loadModel(cls, props: LoadModelParams):
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vcparams = VoiceChangerParamsManager.get_instance().params
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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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print("RVC:: slotInfo.modelFile", slotInfo.modelFile)
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# slotInfo.iconFile = "/assets/icons/noimage.png"
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modelPath = os.path.join(vcparams.model_dir, str(props.slot), os.path.basename(slotInfo.modelFile))
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if slotInfo.isONNX:
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slotInfo = cls._setInfoByONNX(modelPath, slotInfo)
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else:
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slotInfo = cls._setInfoByPytorch(modelPath, slotInfo)
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return slotInfo
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@classmethod
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def _setInfoByPytorch(cls, modelPath: str, slot: RVCModelSlot):
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cpt = torch.load(modelPath, map_location="cpu")
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config_len = len(cpt["config"])
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version = cpt.get("version", "v1")
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slot = RVCModelSlot(**asdict(slot))
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if version == "voras_beta":
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slot.f0 = True if cpt["f0"] == 1 else False
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slot.modelType = EnumInferenceTypes.pyTorchVoRASbeta.value
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slot.embChannels = 768
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slot.embOutputLayer = cpt["embedder_output_layer"] if "embedder_output_layer" in cpt else 9
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slot.useFinalProj = False
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slot.embedder = cpt["embedder_name"]
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if slot.embedder.endswith("768"):
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slot.embedder = slot.embedder[:-3]
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# if slot.embedder == "hubert":
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# slot.embedder = "hubert"
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# elif slot.embedder == "contentvec":
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# slot.embedder = "contentvec"
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# elif slot.embedder == "hubert_jp":
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# slot.embedder = "hubert_jp"
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else:
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raise RuntimeError("[Voice Changer][setInfoByPytorch] unknown embedder")
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elif config_len == 18:
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# Original RVC
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slot.f0 = True if cpt["f0"] == 1 else False
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version = cpt.get("version", "v1")
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if version is None or version == "v1":
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slot.modelType = EnumInferenceTypes.pyTorchRVC.value if slot.f0 else EnumInferenceTypes.pyTorchRVCNono.value
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slot.embChannels = 256
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slot.embOutputLayer = 9
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slot.useFinalProj = True
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slot.embedder = "hubert_base"
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print("[Voice Changer] Official Model(pyTorch) : v1")
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else:
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slot.modelType = EnumInferenceTypes.pyTorchRVCv2.value if slot.f0 else EnumInferenceTypes.pyTorchRVCv2Nono.value
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slot.embChannels = 768
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slot.embOutputLayer = 12
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slot.useFinalProj = False
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slot.embedder = "hubert_base"
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print("[Voice Changer] Official Model(pyTorch) : v2")
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else:
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# DDPN RVC
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slot.f0 = True if cpt["f0"] == 1 else False
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slot.modelType = EnumInferenceTypes.pyTorchWebUI.value if slot.f0 else EnumInferenceTypes.pyTorchWebUINono.value
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slot.embChannels = cpt["config"][17]
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slot.embOutputLayer = cpt["embedder_output_layer"] if "embedder_output_layer" in cpt else 9
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if slot.embChannels == 256:
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slot.useFinalProj = True
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else:
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slot.useFinalProj = False
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# DDPNモデルの情報を表示
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if slot.embChannels == 256 and slot.embOutputLayer == 9 and slot.useFinalProj is True:
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print("[Voice Changer] DDPN Model(pyTorch) : Official v1 like")
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elif slot.embChannels == 768 and slot.embOutputLayer == 12 and slot.useFinalProj is False:
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print("[Voice Changer] DDPN Model(pyTorch): Official v2 like")
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else:
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print(f"[Voice Changer] DDPN Model(pyTorch): ch:{slot.embChannels}, L:{slot.embOutputLayer}, FP:{slot.useFinalProj}")
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slot.embedder = cpt["embedder_name"]
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if slot.embedder.endswith("768"):
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slot.embedder = slot.embedder[:-3]
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if "speaker_info" in cpt.keys():
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for k, v in cpt["speaker_info"].items():
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slot.speakers[int(k)] = str(v)
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slot.samplingRate = cpt["config"][-1]
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del cpt
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return slot
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@classmethod
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def _setInfoByONNX(cls, modelPath: str, slot: RVCModelSlot):
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tmp_onnx_session = onnxruntime.InferenceSession(modelPath, providers=["CPUExecutionProvider"])
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modelmeta = tmp_onnx_session.get_modelmeta()
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try:
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slot = RVCModelSlot(**asdict(slot))
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metadata = json.loads(modelmeta.custom_metadata_map["metadata"])
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# slot.modelType = metadata["modelType"]
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slot.embChannels = metadata["embChannels"]
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slot.embOutputLayer = metadata["embOutputLayer"] if "embOutputLayer" in metadata else 9
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slot.useFinalProj = metadata["useFinalProj"] if "useFinalProj" in metadata else True if slot.embChannels == 256 else False
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if slot.embChannels == 256:
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slot.useFinalProj = True
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else:
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slot.useFinalProj = False
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# ONNXモデルの情報を表示
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if slot.embChannels == 256 and slot.embOutputLayer == 9 and slot.useFinalProj is True:
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print("[Voice Changer] ONNX Model: Official v1 like")
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elif slot.embChannels == 768 and slot.embOutputLayer == 12 and slot.useFinalProj is False:
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print("[Voice Changer] ONNX Model: Official v2 like")
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else:
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print(f"[Voice Changer] ONNX Model: ch:{slot.embChannels}, L:{slot.embOutputLayer}, FP:{slot.useFinalProj}")
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if "embedder" not in metadata:
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slot.embedder = "hubert_base"
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else:
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slot.embedder = metadata["embedder"]
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slot.f0 = metadata["f0"]
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slot.modelType = EnumInferenceTypes.onnxRVC.value if slot.f0 else EnumInferenceTypes.onnxRVCNono.value
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slot.samplingRate = metadata["samplingRate"]
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slot.deprecated = False
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if slot.embChannels == 256:
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if metadata["version"] == "2.1":
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slot.version = "v1.1" # 1.1はclipをonnx内部で実施. realtimeをdisable
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else:
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slot.version = "v1"
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elif metadata["version"] == "2":
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slot.version = "v2"
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elif metadata["version"] == "2.1": # 2.1はclipをonnx内部で実施. realtimeをdisable
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slot.version = "v2.1"
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except Exception as e:
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slot.modelType = EnumInferenceTypes.onnxRVC.value
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slot.embChannels = 256
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slot.embedder = "hubert_base"
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slot.f0 = True
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slot.samplingRate = 48000
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slot.deprecated = True
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print("[Voice Changer] setInfoByONNX", e)
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print("[Voice Changer] ############## !!!! CAUTION !!!! ####################")
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print("[Voice Changer] This onnxfie is depricated. Please regenerate onnxfile.")
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print("[Voice Changer] ############## !!!! CAUTION !!!! ####################")
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del tmp_onnx_session
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return slot
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