mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-02 16:23:58 +03:00
WIP:common sample
This commit is contained in:
wataru
parent
24b55116f2
commit
435699d387
@ -107,9 +107,9 @@ def getSampleJsonAndModelIds(mode: RVCSampleMode):
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0003_t2.json",
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0003_o2.json",
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], [
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("TokinaShigure_o", {"useIndex": True}),
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("KikotoMahiro_o", {"useIndex": False}),
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("Amitaro_o", {"useIndex": False}),
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# ("TokinaShigure_o", {"useIndex": True}),
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# ("KikotoMahiro_o", {"useIndex": False}),
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# ("Amitaro_o", {"useIndex": False}),
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("Tsukuyomi-chan_o", {"useIndex": False}),
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]
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elif mode == "testOfficial":
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@ -1,28 +0,0 @@
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from const import EnumInferenceTypes, EnumEmbedderTypes
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from dataclasses import dataclass
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@dataclass
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class ModelSlot:
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modelFile: str = ""
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indexFile: str = ""
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defaultTune: int = 0
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defaultIndexRatio: int = 1
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defaultProtect: float = 0.5
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isONNX: bool = False
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modelType: str = EnumInferenceTypes.pyTorchRVC.value
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samplingRate: int = -1
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f0: bool = True
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embChannels: int = 256
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embOutputLayer: int = 9
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useFinalProj: bool = True
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deprecated: bool = False
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embedder: str = EnumEmbedderTypes.hubert.value
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name: str = ""
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description: str = ""
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credit: str = ""
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termsOfUseUrl: str = ""
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sampleId: str = ""
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iconFile: str = ""
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@ -1,10 +1,11 @@
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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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from data.ModelSlot import 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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@ -15,22 +16,14 @@ def _setInfoByPytorch(slot: ModelSlot):
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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 = (
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EnumInferenceTypes.pyTorchRVC.value
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if slot.f0
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else EnumInferenceTypes.pyTorchRVCNono.value
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)
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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 = EnumEmbedderTypes.hubert.value
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print("[Voice Changer] Official Model(pyTorch) : v1")
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else:
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slot.modelType = (
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EnumInferenceTypes.pyTorchRVCv2.value
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if slot.f0
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else EnumInferenceTypes.pyTorchRVCv2Nono.value
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)
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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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@ -40,37 +33,21 @@ def _setInfoByPytorch(slot: ModelSlot):
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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 = (
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EnumInferenceTypes.pyTorchWebUI.value
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if slot.f0
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else EnumInferenceTypes.pyTorchWebUINono.value
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)
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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 = (
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cpt["embedder_output_layer"] if "embedder_output_layer" in cpt else 9
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)
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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 (
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slot.embChannels == 256
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and slot.embOutputLayer == 9
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and slot.useFinalProj is True
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):
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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 (
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slot.embChannels == 768
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and slot.embOutputLayer == 12
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and slot.useFinalProj is False
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):
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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(
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f"[Voice Changer] DDPN Model(pyTorch): ch:{slot.embChannels}, L:{slot.embOutputLayer}, FP:{slot.useFinalProj}"
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)
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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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@ -91,9 +68,7 @@ def _setInfoByPytorch(slot: ModelSlot):
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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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tmp_onnx_session = onnxruntime.InferenceSession(slot.modelFile, providers=["CPUExecutionProvider"])
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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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@ -101,16 +76,8 @@ def _setInfoByONNX(slot: ModelSlot):
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# slot.modelType = metadata["modelType"]
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slot.embChannels = metadata["embChannels"]
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slot.embOutputLayer = (
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metadata["embOutputLayer"] if "embOutputLayer" in metadata else 9
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)
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slot.useFinalProj = (
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metadata["useFinalProj"]
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if "useFinalProj" in metadata
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else True
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if slot.embChannels == 256
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else False
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)
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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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@ -118,22 +85,12 @@ def _setInfoByONNX(slot: ModelSlot):
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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 (
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slot.embChannels == 256
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and slot.embOutputLayer == 9
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and slot.useFinalProj is True
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):
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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 (
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slot.embChannels == 768
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and slot.embOutputLayer == 12
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and slot.useFinalProj is False
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):
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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(
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f"[Voice Changer] ONNX Model: ch:{slot.embChannels}, L:{slot.embOutputLayer}, FP:{slot.useFinalProj}"
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)
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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 = EnumEmbedderTypes.hubert.value
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@ -149,11 +106,7 @@ def _setInfoByONNX(slot: ModelSlot):
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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.value
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if slot.f0
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else EnumInferenceTypes.onnxRVCNono.value
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)
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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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@ -5,10 +5,8 @@ from typing import cast
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import numpy as np
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import torch
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import torchaudio
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from data.ModelSlot import loadAllSlotInfo
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from data.ModelSlot import RVCModelSlot, loadAllSlotInfo
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from utils.downloader.SampleDownloader import getSampleInfos
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from voice_changer.RVC.ModelSlot import ModelSlot
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from voice_changer.RVC.SampleDownloader import downloadModelFiles
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# avoiding parse arg error in RVC
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@ -102,41 +100,16 @@ class RVC:
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def loadModel(self, props: LoadModelParams):
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target_slot_idx = props.slot
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params = props.params
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slotInfo: ModelSlot = ModelSlot()
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slotInfo: RVCModelSlot = RVCModelSlot()
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print("loadModel", params)
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# サンプルが指定されたときはダウンロードしてメタデータをでっちあげる
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if len(params["sampleId"]) > 0:
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sampleId = params["sampleId"]
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sampleInfo = self.getSampleInfo(sampleId)
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useIndex = params["rvcIndexDownload"]
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if sampleInfo is None:
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print("[Voice Changer] sampleInfo is None")
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return
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modelPath, indexPath, iconPath = downloadModelFiles(sampleInfo, useIndex)
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slotInfo.modelFile = modelPath
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if indexPath is not None:
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slotInfo.indexFile = indexPath
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if iconPath is not None:
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slotInfo.iconFile = iconPath
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slotInfo.sampleId = sampleInfo.id
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slotInfo.credit = sampleInfo.credit
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slotInfo.description = sampleInfo.description
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slotInfo.name = sampleInfo.name
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slotInfo.termsOfUseUrl = sampleInfo.termsOfUseUrl
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# slotInfo.samplingRate = sampleInfo.sampleRate
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# slotInfo.modelType = sampleInfo.modelType
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# slotInfo.f0 = sampleInfo.f0
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else:
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slotInfo.modelFile = params["files"]["rvcModel"]
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slotInfo.indexFile = params["files"]["rvcIndex"] if "rvcIndex" in params["files"] else None
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slotInfo.modelFile = params["files"]["rvcModel"]
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slotInfo.indexFile = params["files"]["rvcIndex"] if "rvcIndex" in params["files"] else None
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slotInfo.defaultTune = params["defaultTune"]
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slotInfo.defaultIndexRatio = params["defaultIndexRatio"]
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slotInfo.defaultProtect = params["defaultProtect"]
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slotInfo.voiceChangerType = "RVC"
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slotInfo.isONNX = slotInfo.modelFile.endswith(".onnx")
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if slotInfo.isONNX:
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@ -1,164 +0,0 @@
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import asdict
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import os
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from const import RVC_MODEL_DIRNAME, TMP_DIR
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from Downloader import download, download_no_tqdm
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from ModelSample import RVCModelSample, getModelSamples
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import json
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from voice_changer.RVC.ModelSlot import ModelSlot
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from voice_changer.RVC.ModelSlotGenerator import _setInfoByONNX, _setInfoByPytorch
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def checkRvcModelExist(model_dir: str):
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rvcModelDir = os.path.join(model_dir, RVC_MODEL_DIRNAME)
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if not os.path.exists(rvcModelDir):
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return False
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return True
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def downloadInitialSampleModels(sampleJsons: list[str], sampleModelIds: list[str], model_dir: str):
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sampleModels = getModelSamples(sampleJsons, "RVC")
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if sampleModels is None:
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return
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downloadParams = []
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slot_count = 0
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line_num = 0
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for initSampleId in sampleModelIds:
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# 初期サンプルをサーチ
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match = False
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for sample in sampleModels:
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if sample.id == initSampleId[0]:
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match = True
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break
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if match is False:
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print(f"[Voice Changer] initiail sample not found. {initSampleId[0]}")
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continue
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# 検出されたら、、、
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slotInfo: ModelSlot = ModelSlot()
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# sampleParams: Any = {"files": {}}
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slotDir = os.path.join(model_dir, RVC_MODEL_DIRNAME, str(slot_count))
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os.makedirs(slotDir, exist_ok=True)
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modelFilePath = os.path.join(
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slotDir,
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os.path.basename(sample.modelUrl),
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)
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downloadParams.append(
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{
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"url": sample.modelUrl,
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"saveTo": modelFilePath,
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"position": line_num,
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}
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)
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slotInfo.modelFile = modelFilePath
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line_num += 1
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if initSampleId[1] is True and hasattr(sample, "indexUrl") and sample.indexUrl != "":
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indexPath = os.path.join(
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slotDir,
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os.path.basename(sample.indexUrl),
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)
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downloadParams.append(
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{
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"url": sample.indexUrl,
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"saveTo": indexPath,
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"position": line_num,
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}
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)
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slotInfo.indexFile = indexPath
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line_num += 1
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if hasattr(sample, "icon") and sample.icon != "":
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iconPath = os.path.join(
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slotDir,
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os.path.basename(sample.icon),
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)
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downloadParams.append(
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{
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"url": sample.icon,
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"saveTo": iconPath,
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"position": line_num,
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}
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)
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slotInfo.iconFile = iconPath
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line_num += 1
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slotInfo.sampleId = sample.id
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slotInfo.credit = sample.credit
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slotInfo.description = sample.description
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slotInfo.name = sample.name
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slotInfo.termsOfUseUrl = sample.termsOfUseUrl
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slotInfo.defaultTune = 0
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slotInfo.defaultIndexRatio = 0.0
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slotInfo.defaultProtect = 0.5
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slotInfo.isONNX = slotInfo.modelFile.endswith(".onnx")
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# この時点ではまだファイルはダウンロードされていない
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# if slotInfo.isONNX:
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# _setInfoByONNX(slotInfo)
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# else:
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# _setInfoByPytorch(slotInfo)
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json.dump(asdict(slotInfo), open(os.path.join(slotDir, "params.json"), "w"))
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slot_count += 1
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# ダウンロード
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print("[Voice Changer] Downloading model files...")
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with ThreadPoolExecutor() as pool:
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pool.map(download, downloadParams)
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# メタデータ作成
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print("[Voice Changer] Generating metadata...")
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for slotId in range(slot_count):
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slotDir = os.path.join(model_dir, RVC_MODEL_DIRNAME, str(slotId))
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jsonDict = json.load(open(os.path.join(slotDir, "params.json")))
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slotInfo = ModelSlot(**jsonDict)
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if slotInfo.isONNX:
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_setInfoByONNX(slotInfo)
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else:
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_setInfoByPytorch(slotInfo)
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json.dump(asdict(slotInfo), open(os.path.join(slotDir, "params.json"), "w"))
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def downloadModelFiles(sampleInfo: RVCModelSample, useIndex: bool = True):
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downloadParams = []
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modelPath = os.path.join(TMP_DIR, os.path.basename(sampleInfo.modelUrl))
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downloadParams.append(
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{
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"url": sampleInfo.modelUrl,
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"saveTo": modelPath,
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"position": 0,
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}
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)
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indexPath = None
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if useIndex is True and hasattr(sampleInfo, "indexUrl") and sampleInfo.indexUrl != "":
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print("[Voice Changer] Download sample with index.")
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indexPath = os.path.join(TMP_DIR, os.path.basename(sampleInfo.indexUrl))
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downloadParams.append(
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{
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"url": sampleInfo.indexUrl,
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"saveTo": indexPath,
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"position": 1,
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}
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)
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iconPath = None
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if hasattr(sampleInfo, "icon") and sampleInfo.icon != "":
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iconPath = os.path.join(TMP_DIR, os.path.basename(sampleInfo.icon))
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downloadParams.append(
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{
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"url": sampleInfo.icon,
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"saveTo": iconPath,
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"position": 2,
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}
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)
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print("[Voice Changer] Downloading model files...", end="")
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with ThreadPoolExecutor() as pool:
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pool.map(download_no_tqdm, downloadParams)
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print("")
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return modelPath, indexPath, iconPath
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@ -4,7 +4,7 @@ import torch
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from onnxsim import simplify
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import onnx
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from const import TMP_DIR, EnumInferenceTypes
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from voice_changer.RVC.ModelSlot import ModelSlot
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from data.ModelSlot import ModelSlot
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from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
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from voice_changer.RVC.onnxExporter.SynthesizerTrnMs256NSFsid_ONNX import (
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SynthesizerTrnMs256NSFsid_ONNX,
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@ -30,9 +30,7 @@ def export2onnx(gpu: int, modelSlot: ModelSlot):
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modelFile = modelSlot.modelFile
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output_file = os.path.splitext(os.path.basename(modelFile))[0] + ".onnx"
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output_file_simple = (
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os.path.splitext(os.path.basename(modelFile))[0] + "_simple.onnx"
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)
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output_file_simple = os.path.splitext(os.path.basename(modelFile))[0] + "_simple.onnx"
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output_path = os.path.join(TMP_DIR, output_file)
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output_path_simple = os.path.join(TMP_DIR, output_file_simple)
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metadata = {
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@ -52,9 +50,7 @@ def export2onnx(gpu: int, modelSlot: ModelSlot):
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if gpuMomory > 0:
|
||||
_export2onnx(modelFile, output_path, output_path_simple, True, metadata)
|
||||
else:
|
||||
print(
|
||||
"[Voice Changer] Warning!!! onnx export with float32. maybe size is doubled."
|
||||
)
|
||||
print("[Voice Changer] Warning!!! onnx export with float32. maybe size is doubled.")
|
||||
_export2onnx(modelFile, output_path, output_path_simple, False, metadata)
|
||||
return output_file_simple
|
||||
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
import os
|
||||
import traceback
|
||||
import faiss
|
||||
from data.ModelSlot import RVCModelSlot
|
||||
|
||||
from voice_changer.RVC.ModelSlot import ModelSlot
|
||||
from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
|
||||
from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
|
||||
from voice_changer.RVC.inferencer.InferencerManager import InferencerManager
|
||||
@ -10,15 +10,13 @@ from voice_changer.RVC.pipeline.Pipeline import Pipeline
|
||||
from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
|
||||
|
||||
|
||||
def createPipeline(modelSlot: ModelSlot, gpu: int, f0Detector: str):
|
||||
def createPipeline(modelSlot: RVCModelSlot, gpu: int, f0Detector: str):
|
||||
dev = DeviceManager.get_instance().getDevice(gpu)
|
||||
half = DeviceManager.get_instance().halfPrecisionAvailable(gpu)
|
||||
|
||||
# Inferencer 生成
|
||||
try:
|
||||
inferencer = InferencerManager.getInferencer(
|
||||
modelSlot.modelType, modelSlot.modelFile, gpu
|
||||
)
|
||||
inferencer = InferencerManager.getInferencer(modelSlot.modelType, modelSlot.modelFile, gpu)
|
||||
except Exception as e:
|
||||
print("[Voice Changer] exception! loading inferencer", e)
|
||||
traceback.print_exc()
|
||||
@ -54,7 +52,7 @@ def createPipeline(modelSlot: ModelSlot, gpu: int, f0Detector: str):
|
||||
return pipeline
|
||||
|
||||
|
||||
def _loadIndex(modelSlot: ModelSlot):
|
||||
def _loadIndex(modelSlot: RVCModelSlot):
|
||||
# Indexのロード
|
||||
print("[Voice Changer] Loading index...")
|
||||
# ファイル指定がない場合はNone
|
||||
|
||||
Loading…
Reference in New Issue
Block a user