mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-24 22:15:02 +03:00
132 lines
4.6 KiB
Python
132 lines
4.6 KiB
Python
from const import EnumEmbedderTypes, EnumInferenceTypes
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from voice_changer.RVC.ModelSlot import ModelSlot
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import torch
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import onnxruntime
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import json
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import os
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def generateModelSlot(slotDir: str):
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modelSlot = ModelSlot()
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if os.path.exists(slotDir) is False:
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return modelSlot
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paramFile = os.path.join(slotDir, "params.json")
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with open(paramFile, "r") as f:
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params = json.load(f)
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modelSlot.modelFile = os.path.join(
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slotDir, os.path.basename(params["files"]["rvcModel"])
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)
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if "rvcFeature" in params["files"]:
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modelSlot.featureFile = os.path.join(
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slotDir, os.path.basename(params["files"]["rvcFeature"])
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)
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else:
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modelSlot.featureFile = None
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if "rvcIndex" in params["files"]:
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modelSlot.indexFile = os.path.join(
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slotDir, os.path.basename(params["files"]["rvcIndex"])
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)
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else:
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modelSlot.indexFile = None
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modelSlot.defaultTrans = params["trans"] if "trans" in params else 0
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modelSlot.name = params["name"] if "name" in params else None
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modelSlot.description = params["description"] if "description" in params else None
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modelSlot.credit = params["credit"] if "credit" in params else None
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modelSlot.termsOfUseUrl = (
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params["termsOfUseUrl"] if "termsOfUseUrl" in params else None
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)
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modelSlot.isONNX = modelSlot.modelFile.endswith(".onnx")
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if modelSlot.isONNX:
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_setInfoByONNX(modelSlot)
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else:
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_setInfoByPytorch(modelSlot)
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return modelSlot
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def _setInfoByPytorch(slot: ModelSlot):
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cpt = torch.load(slot.modelFile, map_location="cpu")
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config_len = len(cpt["config"])
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if config_len == 18:
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slot.f0 = True if cpt["f0"] == 1 else False
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slot.modelType = (
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EnumInferenceTypes.pyTorchRVC
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if slot.f0
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else EnumInferenceTypes.pyTorchRVCNono
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)
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slot.embChannels = 256
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slot.embedder = EnumEmbedderTypes.hubert
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else:
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slot.f0 = True if cpt["f0"] == 1 else False
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slot.modelType = (
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EnumInferenceTypes.pyTorchWebUI
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if slot.f0
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else EnumInferenceTypes.pyTorchWebUINono
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)
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slot.embChannels = cpt["config"][17]
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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 == EnumEmbedderTypes.hubert.value:
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slot.embedder = EnumEmbedderTypes.hubert
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elif slot.embedder == EnumEmbedderTypes.contentvec.value:
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slot.embedder = EnumEmbedderTypes.contentvec
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elif slot.embedder == EnumEmbedderTypes.hubert_jp.value:
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slot.embedder = EnumEmbedderTypes.hubert_jp
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else:
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raise RuntimeError("[Voice Changer][setInfoByONNX] unknown embedder")
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slot.samplingRate = cpt["config"][-1]
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del cpt
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def _setInfoByONNX(slot: ModelSlot):
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tmp_onnx_session = onnxruntime.InferenceSession(
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slot.modelFile, providers=["CPUExecutionProvider"]
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)
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modelmeta = tmp_onnx_session.get_modelmeta()
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try:
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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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if "embedder" not in metadata:
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slot.embedder = EnumEmbedderTypes.hubert
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elif metadata["embedder"] == EnumEmbedderTypes.hubert.value:
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slot.embedder = EnumEmbedderTypes.hubert
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elif metadata["embedder"] == EnumEmbedderTypes.contentvec.value:
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slot.embedder = EnumEmbedderTypes.contentvec
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elif metadata["embedder"] == EnumEmbedderTypes.hubert_jp.value:
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slot.embedder = EnumEmbedderTypes.hubert_jp
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else:
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raise RuntimeError("[Voice Changer][setInfoByONNX] unknown embedder")
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slot.f0 = metadata["f0"]
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slot.modelType = (
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EnumInferenceTypes.onnxRVC if slot.f0 else EnumInferenceTypes.onnxRVCNono
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)
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slot.samplingRate = metadata["samplingRate"]
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slot.deprecated = False
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except Exception as e:
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slot.modelType = EnumInferenceTypes.onnxRVC
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slot.embChannels = 256
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slot.embedder = EnumEmbedderTypes.hubert
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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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