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WIP: directml

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This commit is contained in:
wataru 2023-05-04 23:50:52 +09:00
parent f48319c350
commit 5f3dd62f81
11 changed files with 227 additions and 137 deletions
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4
client/demo/dist/assets/gui_settings/RVC.json vendored
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@ -165,6 +165,10 @@
{
"name": "silenceFront",
"options": {}
},
{
"name": "enableDirectML",
"options": {}
}
]
},

15
client/demo/dist/index.js vendored
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File diff suppressed because one or more lines are too long

4
client/demo/public/assets/gui_settings/RVC.json
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@ -165,6 +165,10 @@
{
"name": "silenceFront",
"options": {}
},
{
"name": "enableDirectML",
"options": {}
}
]
},

4
client/demo/src/components/demo/002_ComponentGenerator.tsx
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@ -45,6 +45,7 @@ import { ONNXExportRow, ONNXExportRowProps } from "./components/205_ONNXExportRo
import { ONNXExecutorRow, ONNXExecutorRowProps } from "./components/206_ONNXExecutorRow"
import { MergeLabRow, MergeLabRowProps } from "./components/a01_MergeLab.Row"
import { ModelSwitchRow, ModelSwitchRowProps } from "./components/204v2_ModelSwitchRow"
import { EnableDirectMLRow, EnableDirectMLRowProps } from "./components/813_EnableDirectMLRow"
export const catalog: { [key: string]: (props: any) => JSX.Element } = {}
@ -117,6 +118,9 @@ const initialize = () => {
addToCatalog("trancateNumThreshold", (props: TrancateNumTresholdRowProps) => { return <TrancateNumTresholdRow {...props} /> })
addToCatalog("rvcQuality", (props: RVCQualityRowProps) => { return <RVCQualityRow {...props} /> })
addToCatalog("silenceFront", (props: SilenceFrontRowProps) => { return <SilenceFrontRow {...props} /> })
addToCatalog("enableDirectML", (props: EnableDirectMLRowProps) => { return <EnableDirectMLRow {...props} /> })
addToCatalog("mergeLab", (props: MergeLabRowProps) => { return <MergeLabRow {...props} /> })

31
client/demo/src/components/demo/components/813_EnableDirectMLRow.tsx Normal file
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@ -0,0 +1,31 @@
import React, { useMemo } from "react"
import { useAppState } from "../../../001_provider/001_AppStateProvider"
export type EnableDirectMLRowProps = {
}
export const EnableDirectMLRow = (_props: EnableDirectMLRowProps) => {
const appState = useAppState()
const enableDirctMLRow = useMemo(() => {
const onEnableDirectMLChanged = (val: number) => {
appState.serverSetting.updateServerSettings({
...appState.serverSetting.serverSetting,
enableDirectML: val
})
}
return (
<div className="body-row split-3-7 left-padding-1 guided">
<div className="body-item-title left-padding-1">DirectML(experimental)</div>
<div className="body-input-container">
<select value={appState.serverSetting.serverSetting.enableDirectML} onChange={(e) => { onEnableDirectMLChanged(Number(e.target.value)) }}>
<option value="0" >off</option>
<option value="1" >on</option>
</select>
</div>
</div>
)
}, [appState.serverSetting.serverSetting, appState.serverSetting.updateServerSettings])
return enableDirctMLRow
}

5
client/lib/src/const.ts
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@ -99,6 +99,8 @@ export const ServerSettingKey = {
"enhancerTune": "enhancerTune",
"inputSampleRate": "inputSampleRate",
"enableDirectML": "enableDirectML",
} as const
export type ServerSettingKey = typeof ServerSettingKey[keyof typeof ServerSettingKey]
@ -136,6 +138,7 @@ export type VoiceChangerServerSetting = {
enhancerTune: number // DDSP-SVC
inputSampleRate: InputSampleRate
enableDirectML: number
}
type ModelSlot = {
@ -204,7 +207,7 @@ export const DefaultServerSetting: ServerInfo = {
enableEnhancer: 0,
enhancerTune: 0,
enableDirectML: 0,
//
status: "ok",
configFile: "",

185
server/voice_changer/RVC/RVC.py
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@ -1,10 +1,12 @@
import sys
import os
from Exceptions import NoModeLoadedException
from voice_changer.RVC.ModelSlot import ModelSlot
from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
import json
import resampy
from dataclasses import asdict
from typing import cast
import numpy as np
import torch
from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
# avoiding parse arg error in RVC
sys.argv = ["MMVCServerSIO.py"]
@ -18,34 +20,25 @@ if sys.platform.startswith("darwin"):
sys.path.append(modulePath)
else:
sys.path.append("RVC")
import json
import resampy
from voice_changer.RVC.modelMerger.MergeModel import merge_model
from voice_changer.RVC.modelMerger.MergeModelRequest import MergeModelRequest
from voice_changer.RVC.ModelSlotGenerator import generateModelSlot
from voice_changer.RVC.RVCSettings import RVCSettings
from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
from voice_changer.RVC.inferencer.InferencerManager import InferencerManager
from voice_changer.utils.LoadModelParams import FilePaths, LoadModelParams
from voice_changer.utils.VoiceChangerModel import AudioInOut
from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
from voice_changer.RVC.onnxExporter.export2onnx import export2onnx
from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
from voice_changer.RVC.pipeline.PipelineGenerator import createPipeline
from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
from voice_changer.RVC.pipeline.Pipeline import Pipeline
from dataclasses import asdict
from typing import cast
import numpy as np
import torch
# from fairseq import checkpoint_utils
import traceback
import faiss
from Exceptions import NoModeLoadedException
from const import UPLOAD_DIR
from voice_changer.RVC.Pipeline import Pipeline
providers = [
"OpenVINOExecutionProvider",
"CUDAExecutionProvider",
@ -96,121 +89,9 @@ class RVC:
self.initialLoad = False
elif target_slot_idx == self.currentSlot:
self.prepareModel(target_slot_idx)
self.needSwitch = True
return self.get_info()
def createPipeline(self, modelSlot: ModelSlot):
dev = self.deviceManager.getDevice(self.settings.gpu)
half = self.deviceManager.halfPrecisionAvailable(self.settings.gpu)
# ファイル名特定(Inferencer)
inferencerFilename = (
modelSlot.onnxModelFile if modelSlot.isONNX else modelSlot.pyTorchModelFile
)
# Inferencer 生成
try:
inferencer = InferencerManager.getInferencer(
modelSlot.modelType,
inferencerFilename,
half,
dev,
)
except Exception as e:
print("[Voice Changer] exception! loading inferencer", e)
traceback.print_exc()
# Embedder 生成
try:
embedder = EmbedderManager.getEmbedder(
modelSlot.embedder,
# emmbedderFilename,
half,
dev,
)
except Exception as e:
print("[Voice Changer] exception! loading embedder", e)
traceback.print_exc()
# pitchExtractor
pitchExtractor = PitchExtractorManager.getPitchExtractor(
self.settings.f0Detector
)
# index, feature
index, feature = self.loadIndex(modelSlot)
pipeline = Pipeline(
embedder,
inferencer,
pitchExtractor,
index,
feature,
modelSlot.samplingRate,
dev,
half,
)
return pipeline
def loadIndex(self, modelSlot: ModelSlot):
# Indexのロード
print("[Voice Changer] Loading index...")
# ファイル指定がない場合はNone
if modelSlot.featureFile is None or modelSlot.indexFile is None:
return None, None
# ファイル指定があってもファイルがない場合はNone
if (
os.path.exists(modelSlot.featureFile) is not True
or os.path.exists(modelSlot.indexFile) is not True
):
return None, None
try:
index = faiss.read_index(modelSlot.indexFile)
feature = np.load(modelSlot.featureFile)
except:
print("[Voice Changer] load index failed. Use no index.")
traceback.print_exc()
return None, None
return index, feature
def prepareModel(self, slot: int):
if slot < 0:
return self.get_info()
modelSlot = self.settings.modelSlots[slot]
inferencerFilename = (
modelSlot.onnxModelFile if modelSlot.isONNX else modelSlot.pyTorchModelFile
)
if inferencerFilename == "":
return self.get_info()
print("[Voice Changer] Prepare Model of slot:", slot)
# pipelineの生成
self.next_pipeline = self.createPipeline(modelSlot)
# その他の設定
self.next_trans = modelSlot.defaultTrans
self.next_samplingRate = modelSlot.samplingRate
self.next_framework = "ONNX" if modelSlot.isONNX else "PyTorch"
self.needSwitch = True
print("[Voice Changer] Prepare done.")
return self.get_info()
def switchModel(self):
print("[Voice Changer] Switching model..")
self.pipeline = self.next_pipeline
self.settings.tran = self.next_trans
self.settings.modelSamplingRate = self.next_samplingRate
self.settings.framework = self.next_framework
print(
"[Voice Changer] Switching model..done",
)
def update_settings(self, key: str, val: int | float | str):
if key in self.settings.intData:
# 設定前処理
@ -238,6 +119,12 @@ class RVC:
else:
print("CHAGE TO NEW PIPELINE", half)
self.prepareModel(self.settings.modelSlotIndex)
if key == "enableDirectML":
if self.pipeline is not None and val == 0:
self.pipeline.setDirectMLEnable(False)
elif self.pipeline is not None and val == 1:
self.pipeline.setDirectMLEnable(True)
elif key in self.settings.floatData:
setattr(self.settings, key, float(val))
elif key in self.settings.strData:
@ -251,6 +138,42 @@ class RVC:
return False
return True
def prepareModel(self, slot: int):
if slot < 0:
return self.get_info()
modelSlot = self.settings.modelSlots[slot]
inferencerFilename = (
modelSlot.onnxModelFile if modelSlot.isONNX else modelSlot.pyTorchModelFile
)
if inferencerFilename == "":
return self.get_info()
print("[Voice Changer] Prepare Model of slot:", slot)
# pipelineの生成
self.next_pipeline = createPipeline(
modelSlot, self.settings.gpu, self.settings.f0Detector
)
# その他の設定
self.next_trans = modelSlot.defaultTrans
self.next_samplingRate = modelSlot.samplingRate
self.next_framework = "ONNX" if modelSlot.isONNX else "PyTorch"
self.needSwitch = True
print("[Voice Changer] Prepare done.")
return self.get_info()
def switchModel(self):
print("[Voice Changer] Switching model..")
self.pipeline = self.next_pipeline
self.settings.tran = self.next_trans
self.settings.modelSamplingRate = self.next_samplingRate
self.settings.framework = self.next_framework
print(
"[Voice Changer] Switching model..done",
)
def get_info(self):
data = asdict(self.settings)
return data

2
server/voice_changer/RVC/RVCSettings.py
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@ -26,6 +26,7 @@ class RVCSettings:
speakers: dict[str, int] = field(default_factory=lambda: {})
isHalf: int = 1 # 0:off, 1:on
enableDirectML: int = 0 # 0:off, 1:on
# ↓mutableな物だけ列挙
intData = [
"gpu",
@ -37,6 +38,7 @@ class RVCSettings:
"silenceFront",
"modelSlotIndex",
"isHalf",
"enableDirectML",
]
floatData = ["silentThreshold", "indexRatio"]
strData = ["framework", "f0Detector"]

18
server/voice_changer/RVC/inferencer/OnnxRVCInferencer.py
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@ -38,9 +38,9 @@ class OnnxRVCInferencer(Inferencer):
if pitch is None or pitchf is None:
raise RuntimeError("[Voice Changer] Pitch or Pitchf is not found.")
# print("INFER1", self.model.get_providers())
# print("INFER2", self.model.get_provider_options())
# print("INFER3", self.model.get_session_options())
print("INFER1", self.model.get_providers())
print("INFER2", self.model.get_provider_options())
print("INFER3", self.model.get_session_options())
if self.isHalf:
audio1 = self.model.run(
["audio"],
@ -86,3 +86,15 @@ class OnnxRVCInferencer(Inferencer):
self.model.set_providers(providers=["CPUExecutionProvider"])
return self
def setDirectMLEnable(self, enable: bool):
if "DmlExecutionProvider" not in onnxruntime.get_available_providers():
print("[Voice Changer] DML is not available.")
return
if enable:
self.model.set_providers(
providers=["DmlExecutionProvider", "CPUExecutionProvider"]
)
else:
self.model.set_providers(providers=["CPUExecutionProvider"])

8
server/voice_changer/RVC/Pipeline.py → server/voice_changer/RVC/pipeline/Pipeline.py
View File

@ -14,6 +14,10 @@ from voice_changer.RVC.inferencer.Inferencer import Inferencer
from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
# isHalfが変わる場合はPipeline作り直し
# device(GPU, isHalf変更が伴わない場合), pitchExtractorの変更は、入れ替えで対応
class Pipeline(object):
embedder: Embedder
inferencer: Inferencer
@ -59,6 +63,10 @@ class Pipeline(object):
self.embedder.setDevice(device)
self.inferencer.setDevice(device)
def setDirectMLEnable(self, enable: bool):
if hasattr(self.inferencer, "setDirectMLEnable"):
self.inferencer.setDirectMLEnable(enable)
def setPitchExtractor(self, pitchExtractor: PitchExtractor):
self.pitchExtractor = pitchExtractor

88
server/voice_changer/RVC/pipeline/PipelineGenerator.py Normal file
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@ -0,0 +1,88 @@
import os
import traceback
import numpy as np
import faiss
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
from voice_changer.RVC.pipeline.Pipeline import Pipeline
from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
def createPipeline(modelSlot: ModelSlot, gpu: int, f0Detector: str):
dev = DeviceManager.get_instance().getDevice(gpu)
half = DeviceManager.get_instance().halfPrecisionAvailable(gpu)
# ファイル名特定(Inferencer)
inferencerFilename = (
modelSlot.onnxModelFile if modelSlot.isONNX else modelSlot.pyTorchModelFile
)
# Inferencer 生成
try:
inferencer = InferencerManager.getInferencer(
modelSlot.modelType,
inferencerFilename,
half,
dev,
)
except Exception as e:
print("[Voice Changer] exception! loading inferencer", e)
traceback.print_exc()
# Embedder 生成
try:
embedder = EmbedderManager.getEmbedder(
modelSlot.embedder,
# emmbedderFilename,
half,
dev,
)
except Exception as e:
print("[Voice Changer] exception! loading embedder", e)
traceback.print_exc()
# pitchExtractor
pitchExtractor = PitchExtractorManager.getPitchExtractor(f0Detector)
# index, feature
index, feature = _loadIndex(modelSlot)
pipeline = Pipeline(
embedder,
inferencer,
pitchExtractor,
index,
feature,
modelSlot.samplingRate,
dev,
half,
)
return pipeline
def _loadIndex(modelSlot: ModelSlot):
# Indexのロード
print("[Voice Changer] Loading index...")
# ファイル指定がない場合はNone
if modelSlot.featureFile is None or modelSlot.indexFile is None:
return None, None
# ファイル指定があってもファイルがない場合はNone
if (
os.path.exists(modelSlot.featureFile) is not True
or os.path.exists(modelSlot.indexFile) is not True
):
return None, None
try:
index = faiss.read_index(modelSlot.indexFile)
feature = np.load(modelSlot.featureFile)
except:
print("[Voice Changer] load index failed. Use no index.")
traceback.print_exc()
return None, None
return index, feature