diff --git a/client/demo/build-voice-changer-js.sh b/client/demo/build-voice-changer-js.sh
index 2af183a2c..413a0dd12 100644
--- a/client/demo/build-voice-changer-js.sh
+++ b/client/demo/build-voice-changer-js.sh
@@ -4,7 +4,6 @@
 # cp -r ~/git-work/voice-changer-js/lib/package.json node_modules/@dannadori/voice-changer-js/
 # cp -r ~/git-work/voice-changer-js/lib/dist node_modules/@dannadori/voice-changer-js/
 
-
 cd ~/git-work/voice-changer-js/lib/ ; npm run build:prod; cd -
 rm -rf node_modules/@dannadori/voice-changer-js
 mkdir -p node_modules/@dannadori/voice-changer-js/dist
diff --git a/client/demo/src/001_provider/001_AppStateProvider.tsx b/client/demo/src/001_provider/001_AppStateProvider.tsx
index 16e15917e..ce2585968 100644
--- a/client/demo/src/001_provider/001_AppStateProvider.tsx
+++ b/client/demo/src/001_provider/001_AppStateProvider.tsx
@@ -1,12 +1,11 @@
 import { ClientState } from "@dannadori/voice-changer-client-js";
+import { VoiceChangerJSClient } from "@dannadori/voice-changer-js";
 import React, { useContext, useEffect, useRef } from "react";
 import { ReactNode } from "react";
 import { useVCClient } from "../001_globalHooks/001_useVCClient";
 import { useAppRoot } from "./001_AppRootProvider";
 import { useMessageBuilder } from "../hooks/useMessageBuilder";
 
-import { VoiceChangerJSClient } from "./VoiceChangerJSClient";
-
 type Props = {
     children: ReactNode;
 };
@@ -58,22 +57,34 @@ export const AppStateProvider = ({ children }: Props) => {
 
     // useEffect(() => {
     //     if (clientState.clientState.initialized) {
+    //         const baseUrl = "https://192.168.0.247:18888";
+    //         // const modelUrl = `${baseUrl}/models/rvc2v_40k_f0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvc2v_40k_nof0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvc2v_16k_f0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvcv2_amitaro_v2_40k_f0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvcv2_amitaro_v2_40k_nof0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvcv2_amitaro_v2_32k_f0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvcv2_amitaro_v2_32k_nof0_24000.bin`;
+
+    //         // const modelUrl = `${baseUrl}/models/rvcv1_amitaro_v1_32k_f0_24000.bin`;
+    //         const modelUrl = `${baseUrl}/models/rvcv1_amitaro_v1_32k_nof0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvcv1_amitaro_v1_40k_f0_24000.bin`;
+    //         // const modelUrl = `${baseUrl}/models/rvcv1_amitaro_v1_40k_nof0_24000.bin`;
+
     //         voiceChangerJSClient.current = new VoiceChangerJSClient();
-    //         voiceChangerJSClient.current.initialize();
+    //         voiceChangerJSClient.current.initialize(
+    //             {
+    //                 baseUrl: baseUrl,
+    //                 inputSamplingRate: 48000,
+    //                 outputSamplingRate: 48000,
+    //             },
+    //             modelUrl,
+    //         );
     //         clientState.clientState.setInternalAudioProcessCallback({
     //             processAudio: async (data: Uint8Array) => {
-    //                 console.log("[CLIENTJS] start --------------------------------------");
     //                 const audioF32 = new Float32Array(data.buffer);
     //                 const converted = await voiceChangerJSClient.current!.convert(audioF32);
-
-    //                 let audio_int16_out = new Int16Array(converted.length);
-    //                 for (let i = 0; i < converted.length; i++) {
-    //                     audio_int16_out[i] = converted[i] * 32768.0;
-    //                 }
-    //                 const res = new Uint8Array(audio_int16_out.buffer);
-    //                 console.log("AUDIO::::audio_int16_out", audio_int16_out);
-
-    //                 console.log("[CLIENTJS] end --------------------------------------");
+    //                 const res = new Uint8Array(converted.buffer);
     //                 return res;
     //             },
     //         });
diff --git a/client/demo/src/001_provider/VoiceChangerJSClient.ts b/client/demo/src/001_provider/VoiceChangerJSClient.ts
deleted file mode 100644
index a19b9fb32..000000000
--- a/client/demo/src/001_provider/VoiceChangerJSClient.ts
+++ /dev/null
@@ -1,149 +0,0 @@
-import { create, ConverterType } from "@alexanderolsen/libsamplerate-js";
-import { BlockingQueue } from "./_BlockingQueue";
-import { WorkerManager, generateConfig, VoiceChangerProcessorInitializeParams, VoiceChangerProcessorConvertParams, FunctionType, VoiceChangerProcessorResult } from "@dannadori/voice-changer-js";
-
-export class VoiceChangerJSClient {
-    private wm = new WorkerManager();
-    private audioBuffer: Float32Array = new Float32Array(0);
-    private audioInputLength = 24000;
-
-    private inputSamplingRate = 48000;
-    private outputSamplingRate = 48000;
-    private modelInputSamplingRate = 16000;
-    private modelOutputSamplingRate = 40000;
-    private sem = new BlockingQueue<number>();
-    private crossfadeChunks = 1;
-    private solaChunks = 0.5;
-    constructor() {
-        this.sem.enqueue(0);
-    }
-    private lock = async () => {
-        const num = await this.sem.dequeue();
-        return num;
-    };
-    private unlock = (num: number) => {
-        this.sem.enqueue(num + 1);
-    };
-
-    initialize = async () => {
-        console.log("Voice Changer Initializing,,,");
-        const baseUrl = "http://127.0.0.1:18888";
-
-        this.wm = new WorkerManager();
-        const config = generateConfig();
-        config.processorURL = `${baseUrl}/process.js`;
-        config.onnxWasmPaths = `${baseUrl}/`;
-        await this.wm.init(config);
-
-        const initializeParams: VoiceChangerProcessorInitializeParams = {
-            type: FunctionType.initialize,
-            inputLength: 24000,
-            f0_min: 50,
-            f0_max: 1100,
-            embPitchUrl: "http://127.0.0.1:18888/models/emb_pit_24000.bin",
-            rvcv2InputLength: 148,
-            // rvcv2Url: "http://127.0.0.1:18888/models/rvc2v_24000.bin",
-            rvcv2Url: "http://127.0.0.1:18888/models/rvc2vnof0_24000.bin",
-            transfer: [],
-        };
-
-        const res = (await this.wm.execute(initializeParams)) as VoiceChangerProcessorResult;
-        console.log("Voice Changer Initialized..", res);
-    };
-
-    convert = async (audio: Float32Array): Promise<Float32Array> => {
-        console.log("convert start....", audio);
-        const lockNum = await this.lock();
-        //resample
-        const audio_16k = await this.resample(audio, this.inputSamplingRate, this.modelInputSamplingRate);
-        //store data and get target data
-        //// store
-        const newAudioBuffer = new Float32Array(this.audioBuffer.length + audio_16k.length);
-        newAudioBuffer.set(this.audioBuffer);
-        newAudioBuffer.set(audio_16k, this.audioBuffer.length);
-        this.audioBuffer = newAudioBuffer;
-
-        //// Buffering.....
-        if (this.audioBuffer.length < this.audioInputLength * 1) {
-            console.log(`skip covert length:${this.audioBuffer.length}, audio_16k:${audio_16k.length}`);
-            await this.unlock(lockNum);
-            return new Float32Array(1);
-        } else {
-            console.log(`--------------- convert start... length:${this.audioBuffer.length}, audio_16k:${audio_16k.length}`);
-        }
-
-        //// get chunks
-        let chunkIndex = 0;
-        const audioChunks: Float32Array[] = [];
-        while (true) {
-            const chunkOffset = chunkIndex * this.audioInputLength - (this.crossfadeChunks + this.solaChunks) * 320 * chunkIndex;
-            const chunkEnd = chunkOffset + this.audioInputLength;
-            if (chunkEnd > this.audioBuffer.length) {
-                this.audioBuffer = this.audioBuffer.slice(chunkOffset);
-                break;
-            } else {
-                const chunk = this.audioBuffer.slice(chunkOffset, chunkEnd);
-                audioChunks.push(chunk);
-            }
-            chunkIndex++;
-        }
-
-        if (audioChunks.length == 0) {
-            await this.unlock(lockNum);
-            console.log(`skip covert length:${this.audioBuffer.length}, audio_16k:${audio_16k.length}`);
-            return new Float32Array(1);
-        }
-
-        //convert (each)
-        const convetedAudioChunks: Float32Array[] = [];
-        for (let i = 0; i < audioChunks.length; i++) {
-            const convertParams: VoiceChangerProcessorConvertParams = {
-                type: FunctionType.convert,
-                transfer: [audioChunks[i].buffer],
-            };
-            const res = (await this.wm.execute(convertParams)) as VoiceChangerProcessorResult;
-            const converted = new Float32Array(res.transfer[0] as ArrayBuffer);
-            console.log(`converted.length:::${i}:${converted.length}`);
-
-            convetedAudioChunks.push(converted);
-        }
-
-        //concat
-        let totalLength = convetedAudioChunks.reduce((prev, cur) => prev + cur.length, 0);
-        let convetedAudio = new Float32Array(totalLength);
-        let offset = 0;
-        for (let chunk of convetedAudioChunks) {
-            convetedAudio.set(chunk, offset);
-            offset += chunk.length;
-        }
-        console.log(`converted.length:::convetedAudio:${convetedAudio.length}`);
-
-        //resample
-        // const response = await this.resample(convetedAudio, this.params.modelOutputSamplingRate, this.params.outputSamplingRate);
-
-        const outputDuration = (this.audioInputLength * audioChunks.length - this.crossfadeChunks * 320) / 16000;
-        const outputSamples = outputDuration * this.outputSamplingRate;
-        const convertedOutputRatio = outputSamples / convetedAudio.length;
-        const realOutputSamplingRate = this.modelOutputSamplingRate * convertedOutputRatio;
-        console.log(`realOutputSamplingRate:${realOutputSamplingRate}, `, this.modelOutputSamplingRate, convertedOutputRatio);
-
-        // const response2 = await this.resample(convetedAudio, this.params.modelOutputSamplingRate, realOutputSamplingRate);
-        const response2 = await this.resample(convetedAudio, this.modelOutputSamplingRate, this.outputSamplingRate);
-
-        console.log(`converted from :${audioChunks.length * this.audioInputLength} to:${convetedAudio.length} to:${response2.length}`);
-        console.log(`outputDuration :${outputDuration} outputSamples:${outputSamples}, convertedOutputRatio:${convertedOutputRatio}, realOutputSamplingRate:${realOutputSamplingRate}`);
-        await this.unlock(lockNum);
-        return response2;
-    };
-
-    // Utility
-    resample = async (data: Float32Array, srcSampleRate: number, dstSampleRate: number) => {
-        const converterType = ConverterType.SRC_SINC_BEST_QUALITY;
-        const nChannels = 1;
-        const converter = await create(nChannels, srcSampleRate, dstSampleRate, {
-            converterType: converterType, // default SRC_SINC_FASTEST. see API for more
-        });
-        const res = converter.simple(data);
-        return res;
-    };
-}
diff --git a/client/demo/webpack.common.js b/client/demo/webpack.common.js
index 6d92bcc0d..8a0acabdc 100644
--- a/client/demo/webpack.common.js
+++ b/client/demo/webpack.common.js
@@ -55,20 +55,44 @@ module.exports = {
             patterns: [{ from: "public/favicon.ico", to: "favicon.ico" }],
         }),
 
+        new CopyPlugin({
+            patterns: [{ from: "./node_modules/@dannadori/voice-changer-js/dist/ort-wasm-simd.wasm", to: "ort-wasm-simd.wasm" }],
+        }),
         // new CopyPlugin({
-        //     patterns: [{ from: "./node_modules/@dannadori/voice-changer-js/dist/ort-wasm-simd.wasm", to: "ort-wasm-simd.wasm" }],
+        //     patterns: [{ from: "./node_modules/@dannadori/voice-changer-js/dist/tfjs-backend-wasm-simd.wasm", to: "tfjs-backend-wasm-simd.wasm" }],
         // }),
         // new CopyPlugin({
         //     patterns: [{ from: "./node_modules/@dannadori/voice-changer-js/dist/process.js", to: "process.js" }],
         // }),
         // new CopyPlugin({
-        //     patterns: [{ from: "public/models/emb_pit_24000.bin", to: "models/emb_pit_24000.bin" }],
+        //     patterns: [{ from: "public/models/rvcv2_emb_pit_24000.bin", to: "models/rvcv2_emb_pit_24000.bin" }],
+        // }),
+        // new CopyPlugin({
+        //     patterns: [{ from: "public/models/rvcv2_amitaro_v2_32k_f0_24000.bin", to: "models/rvcv2_amitaro_v2_32k_f0_24000.bin" }],
+        // }),
+        // new CopyPlugin({
+        //     patterns: [{ from: "public/models/rvcv2_amitaro_v2_32k_nof0_24000.bin", to: "models/rvcv2_amitaro_v2_32k_nof0_24000.bin" }],
+        // }),
+        // new CopyPlugin({
+        //     patterns: [{ from: "public/models/rvcv2_amitaro_v2_40k_f0_24000.bin", to: "models/rvcv2_amitaro_v2_40k_f0_24000.bin" }],
+        // }),
+        // new CopyPlugin({
+        //     patterns: [{ from: "public/models/rvcv2_amitaro_v2_40k_nof0_24000.bin", to: "models/rvcv2_amitaro_v2_40k_nof0_24000.bin" }],
+        // }),
+        // new CopyPlugin({
+        //     patterns: [{ from: "public/models/rvcv1_emb_pit_24000.bin", to: "models/rvcv1_emb_pit_24000.bin" }],
+        // }),
+        // new CopyPlugin({
+        //     patterns: [{ from: "public/models/rvcv1_amitaro_v1_32k_f0_24000.bin", to: "models/rvcv1_amitaro_v1_32k_f0_24000.bin" }],
+        // }),
+        // new CopyPlugin({
+        //     patterns: [{ from: "public/models/rvcv1_amitaro_v1_32k_nof0_24000.bin", to: "models/rvcv1_amitaro_v1_32k_nof0_24000.bin" }],
         // }),
         // new CopyPlugin({
-        //     patterns: [{ from: "public/models/rvc2v_24000.bin", to: "models/rvc2v_24000.bin" }],
+        //     patterns: [{ from: "public/models/rvcv1_amitaro_v1_40k_f0_24000.bin", to: "models/rvcv1_amitaro_v1_40k_f0_24000.bin" }],
         // }),
         // new CopyPlugin({
-        //     patterns: [{ from: "public/models/rvc2vnof0_24000.bin", to: "models/rvc2vnof0_24000.bin" }],
+        //     patterns: [{ from: "public/models/rvcv1_amitaro_v1_40k_nof0_24000.bin", to: "models/rvcv1_amitaro_v1_40k_nof0_24000.bin" }],
         // }),
     ],
 };
diff --git a/server/voice_changer/DDSP_SVC/DDSP_SVC.py b/server/voice_changer/DDSP_SVC/DDSP_SVC.py
index 8cc7b8aa2..20fa6a52f 100644
--- a/server/voice_changer/DDSP_SVC/DDSP_SVC.py
+++ b/server/voice_changer/DDSP_SVC/DDSP_SVC.py
@@ -20,7 +20,7 @@
 
 from .models.diffusion.infer_gt_mel import DiffGtMel
 
-from voice_changer.utils.VoiceChangerModel import AudioInOut
+from voice_changer.utils.VoiceChangerModel import AudioInOut, VoiceChangerModel
 from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
 from voice_changer.DDSP_SVC.DDSP_SVCSetting import DDSP_SVCSettings
 from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
@@ -44,15 +44,20 @@ def phase_vocoder(a, b, fade_out, fade_in):
     deltaphase = deltaphase - 2 * np.pi * torch.floor(deltaphase / 2 / np.pi + 0.5)
     w = 2 * np.pi * torch.arange(n // 2 + 1).to(a) + deltaphase
     t = torch.arange(n).unsqueeze(-1).to(a) / n
-    result = a * (fade_out**2) + b * (fade_in**2) + torch.sum(absab * torch.cos(w * t + phia), -1) * fade_out * fade_in / n
+    result = (
+        a * (fade_out**2)
+        + b * (fade_in**2)
+        + torch.sum(absab * torch.cos(w * t + phia), -1) * fade_out * fade_in / n
+    )
     return result
 
 
-class DDSP_SVC:
+class DDSP_SVC(VoiceChangerModel):
     initialLoad: bool = True
 
     def __init__(self, params: VoiceChangerParams, slotInfo: DDSPSVCModelSlot):
         print("[Voice Changer] [DDSP-SVC] Creating instance ")
+        self.voiceChangerType = "DDSP-SVC"
         self.deviceManager = DeviceManager.get_instance()
         self.gpu_num = torch.cuda.device_count()
         self.params = params
@@ -71,8 +76,18 @@ def __init__(self, params: VoiceChangerParams, slotInfo: DDSPSVCModelSlot):
     def initialize(self):
         self.device = self.deviceManager.getDevice(self.settings.gpu)
         vcparams = VoiceChangerParamsManager.get_instance().params
-        modelPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), "model",  self.slotInfo.modelFile)
-        diffPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), "diff", self.slotInfo.diffModelFile)
+        modelPath = os.path.join(
+            vcparams.model_dir,
+            str(self.slotInfo.slotIndex),
+            "model",
+            self.slotInfo.modelFile,
+        )
+        diffPath = os.path.join(
+            vcparams.model_dir,
+            str(self.slotInfo.slotIndex),
+            "diff",
+            self.slotInfo.diffModelFile,
+        )
 
         self.svc_model = SvcDDSP()
         self.svc_model.setVCParams(self.params)
@@ -112,11 +127,15 @@ def generate_input(
         # newData = newData.astype(np.float32)
 
         if self.audio_buffer is not None:
-            self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)  # 過去のデータに連結
+            self.audio_buffer = np.concatenate(
+                [self.audio_buffer, newData], 0
+            )  # 過去のデータに連結
         else:
             self.audio_buffer = newData
 
-        convertSize = inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
+        convertSize = (
+            inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
+        )
 
         # if convertSize % self.hop_size != 0:  # モデルの出力のホップサイズで切り捨てが発生するので補う。
         #     convertSize = convertSize + (self.hop_size - (convertSize % self.hop_size))
@@ -147,7 +166,8 @@ def _pyTorch_inference(self, data):
             f0_min=50,
             f0_max=1100,
             # safe_prefix_pad_length=0,  # TBD なにこれ？
-            safe_prefix_pad_length=self.settings.extraConvertSize / self.svc_model.args.data.sampling_rate,
+            safe_prefix_pad_length=self.settings.extraConvertSize
+            / self.svc_model.args.data.sampling_rate,
             diff_model=self.diff_model,
             diff_acc=self.settings.diffAcc,  # TBD なにこれ？
             diff_spk_id=self.settings.diffSpkId,
@@ -155,7 +175,9 @@ def _pyTorch_inference(self, data):
             # diff_use_dpm=True if self.settings.useDiffDpm == 1 else False,  # TBD なにこれ？
             method=self.settings.diffMethod,
             k_step=self.settings.kStep,  # TBD なにこれ？
-            diff_silence=True if self.settings.useDiffSilence == 1 else False,  # TBD なにこれ？
+            diff_silence=True
+            if self.settings.useDiffSilence == 1
+            else False,  # TBD なにこれ？
         )
 
         return _audio.cpu().numpy() * 32768.0
@@ -182,5 +204,4 @@ def __del__(self):
                 pass
 
     def get_model_current(self):
-        return [
-        ]
+        return []
diff --git a/server/voice_changer/DiffusionSVC/DiffusionSVC.py b/server/voice_changer/DiffusionSVC/DiffusionSVC.py
index 9bd6d7045..531c874c2 100644
--- a/server/voice_changer/DiffusionSVC/DiffusionSVC.py
+++ b/server/voice_changer/DiffusionSVC/DiffusionSVC.py
@@ -6,16 +6,28 @@
 from voice_changer.DiffusionSVC.inferencer.InferencerManager import InferencerManager
 from voice_changer.DiffusionSVC.pipeline.Pipeline import Pipeline
 from voice_changer.DiffusionSVC.pipeline.PipelineGenerator import createPipeline
-from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
+from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractorManager import (
+    PitchExtractorManager,
+)
 from voice_changer.ModelSlotManager import ModelSlotManager
 
-from voice_changer.utils.VoiceChangerModel import AudioInOut, PitchfInOut, FeatureInOut, VoiceChangerModel
+from voice_changer.utils.VoiceChangerModel import (
+    AudioInOut,
+    PitchfInOut,
+    FeatureInOut,
+    VoiceChangerModel,
+)
 from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
 from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
+
 # from voice_changer.RVC.onnxExporter.export2onnx import export2onnx
 from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
 
-from Exceptions import DeviceCannotSupportHalfPrecisionException, PipelineCreateException, PipelineNotInitializedException
+from Exceptions import (
+    DeviceCannotSupportHalfPrecisionException,
+    PipelineCreateException,
+    PipelineNotInitializedException,
+)
 
 logger = VoiceChangaerLogger.get_instance().getLogger()
 
@@ -23,6 +35,7 @@
 class DiffusionSVC(VoiceChangerModel):
     def __init__(self, params: VoiceChangerParams, slotInfo: DiffusionSVCModelSlot):
         logger.info("[Voice Changer] [DiffusionSVC] Creating instance ")
+        self.voiceChangerType = "Diffusion-SVC"
         self.deviceManager = DeviceManager.get_instance()
         EmbedderManager.initialize(params)
         PitchExtractorManager.initialize(params)
@@ -46,9 +59,17 @@ def initialize(self):
 
         # pipelineの生成
         try:
-            self.pipeline = createPipeline(self.slotInfo, self.settings.gpu, self.settings.f0Detector, self.inputSampleRate, self.outputSampleRate)
+            self.pipeline = createPipeline(
+                self.slotInfo,
+                self.settings.gpu,
+                self.settings.f0Detector,
+                self.inputSampleRate,
+                self.outputSampleRate,
+            )
         except PipelineCreateException as e:  # NOQA
-            logger.error("[Voice Changer] pipeline create failed. check your model is valid.")
+            logger.error(
+                "[Voice Changer] pipeline create failed. check your model is valid."
+            )
             return
 
         # その他の設定
@@ -76,7 +97,9 @@ def update_settings(self, key: str, val: int | float | str):
         elif key in self.settings.strData:
             setattr(self.settings, key, str(val))
             if key == "f0Detector" and self.pipeline is not None:
-                pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector, self.settings.gpu)
+                pitchExtractor = PitchExtractorManager.getPitchExtractor(
+                    self.settings.f0Detector, self.settings.gpu
+                )
                 self.pipeline.setPitchExtractor(pitchExtractor)
         else:
             return False
@@ -100,30 +123,65 @@ def generate_input(
         crossfadeSize: int,
         solaSearchFrame: int = 0,
     ):
-        newData = newData.astype(np.float32) / 32768.0  # DiffusionSVCのモデルのサンプリングレートで入ってきている。（extraDataLength, Crossfade等も同じSRで処理）(★１)
-        new_feature_length = int(((newData.shape[0] / self.inputSampleRate) * self.slotInfo.samplingRate) / 512)  # 100 は hubertのhosizeから (16000 / 160).
+        newData = (
+            newData.astype(np.float32) / 32768.0
+        )  # DiffusionSVCのモデルのサンプリングレートで入ってきている。（extraDataLength, Crossfade等も同じSRで処理）(★１)
+        new_feature_length = int(
+            ((newData.shape[0] / self.inputSampleRate) * self.slotInfo.samplingRate)
+            / 512
+        )  # 100 は hubertのhosizeから (16000 / 160).
         # ↑newData.shape[0]//sampleRate でデータ秒数。これに16000かけてhubertの世界でのデータ長。これにhop数(160)でわるとfeatsのデータサイズになる。
         if self.audio_buffer is not None:
             # 過去のデータに連結
             self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)
-            self.pitchf_buffer = np.concatenate([self.pitchf_buffer, np.zeros(new_feature_length)], 0)
-            self.feature_buffer = np.concatenate([self.feature_buffer, np.zeros([new_feature_length, self.slotInfo.embChannels])], 0)
+            self.pitchf_buffer = np.concatenate(
+                [self.pitchf_buffer, np.zeros(new_feature_length)], 0
+            )
+            self.feature_buffer = np.concatenate(
+                [
+                    self.feature_buffer,
+                    np.zeros([new_feature_length, self.slotInfo.embChannels]),
+                ],
+                0,
+            )
         else:
             self.audio_buffer = newData
             self.pitchf_buffer = np.zeros(new_feature_length)
-            self.feature_buffer = np.zeros([new_feature_length, self.slotInfo.embChannels])
+            self.feature_buffer = np.zeros(
+                [new_feature_length, self.slotInfo.embChannels]
+            )
 
-        convertSize = newData.shape[0] + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
+        convertSize = (
+            newData.shape[0]
+            + crossfadeSize
+            + solaSearchFrame
+            + self.settings.extraConvertSize
+        )
 
         if convertSize % 128 != 0:  # モデルの出力のホップサイズで切り捨てが発生するので補う。
             convertSize = convertSize + (128 - (convertSize % 128))
 
         # バッファがたまっていない場合はzeroで補う
-        generateFeatureLength = int(((convertSize / self.inputSampleRate) * self.slotInfo.samplingRate) / 512) + 1
+        generateFeatureLength = (
+            int(
+                ((convertSize / self.inputSampleRate) * self.slotInfo.samplingRate)
+                / 512
+            )
+            + 1
+        )
         if self.audio_buffer.shape[0] < convertSize:
-            self.audio_buffer = np.concatenate([np.zeros([convertSize]), self.audio_buffer])
-            self.pitchf_buffer = np.concatenate([np.zeros(generateFeatureLength), self.pitchf_buffer])
-            self.feature_buffer = np.concatenate([np.zeros([generateFeatureLength, self.slotInfo.embChannels]), self.feature_buffer])
+            self.audio_buffer = np.concatenate(
+                [np.zeros([convertSize]), self.audio_buffer]
+            )
+            self.pitchf_buffer = np.concatenate(
+                [np.zeros(generateFeatureLength), self.pitchf_buffer]
+            )
+            self.feature_buffer = np.concatenate(
+                [
+                    np.zeros([generateFeatureLength, self.slotInfo.embChannels]),
+                    self.feature_buffer,
+                ]
+            )
 
         convertOffset = -1 * convertSize
         featureOffset = -1 * generateFeatureLength
@@ -139,9 +197,17 @@ def generate_input(
         vol = float(max(vol, self.prevVol * 0.0))
         self.prevVol = vol
 
-        return (self.audio_buffer, self.pitchf_buffer, self.feature_buffer, convertSize, vol)
+        return (
+            self.audio_buffer,
+            self.pitchf_buffer,
+            self.feature_buffer,
+            convertSize,
+            vol,
+        )
 
-    def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int):
+    def inference(
+        self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int
+    ):
         if self.pipeline is None:
             logger.info("[Voice Changer] Pipeline is not initialized.")
             raise PipelineNotInitializedException()
@@ -169,7 +235,11 @@ def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_
         speedUp = self.settings.speedUp
         embOutputLayer = 12
         useFinalProj = False
-        silenceFrontSec = self.settings.extraConvertSize / self.inputSampleRate if self.settings.silenceFront else 0.  # extaraConvertSize(既にモデルのサンプリングレートにリサンプリング済み)の秒数。モデルのサンプリングレートで処理(★１)。
+        silenceFrontSec = (
+            self.settings.extraConvertSize / self.inputSampleRate
+            if self.settings.silenceFront
+            else 0.0
+        )  # extaraConvertSize(既にモデルのサンプリングレートにリサンプリング済み)の秒数。モデルのサンプリングレートで処理(★１)。
 
         try:
             audio_out, self.pitchf_buffer, self.feature_buffer = self.pipeline.exec(
@@ -190,7 +260,9 @@ def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_
             result = audio_out.detach().cpu().numpy()
             return result
         except DeviceCannotSupportHalfPrecisionException as e:  # NOQA
-            logger.warn("[Device Manager] Device cannot support half precision. Fallback to float....")
+            logger.warn(
+                "[Device Manager] Device cannot support half precision. Fallback to float...."
+            )
             self.deviceManager.setForceTensor(True)
             self.initialize()
             # raise e
diff --git a/server/voice_changer/MMVCv13/MMVCv13.py b/server/voice_changer/MMVCv13/MMVCv13.py
index 8ea77365a..8fe2f36b6 100644
--- a/server/voice_changer/MMVCv13/MMVCv13.py
+++ b/server/voice_changer/MMVCv13/MMVCv13.py
@@ -3,7 +3,7 @@
 from data.ModelSlot import MMVCv13ModelSlot
 from voice_changer.VoiceChangerParamsManager import VoiceChangerParamsManager
 
-from voice_changer.utils.VoiceChangerModel import AudioInOut
+from voice_changer.utils.VoiceChangerModel import AudioInOut, VoiceChangerModel
 
 if sys.platform.startswith("darwin"):
     baseDir = [x for x in sys.path if x.endswith("Contents/MacOS")]
@@ -48,9 +48,10 @@ class MMVCv13Settings:
     strData: list[str] = field(default_factory=lambda: [])
 
 
-class MMVCv13:
+class MMVCv13(VoiceChangerModel):
     def __init__(self, slotInfo: MMVCv13ModelSlot):
         print("[Voice Changer] [MMVCv13] Creating instance ")
+        self.voiceChangerType = "MMVCv13"
         self.settings = MMVCv13Settings()
         self.net_g = None
         self.onnx_session = None
@@ -65,8 +66,12 @@ def __init__(self, slotInfo: MMVCv13ModelSlot):
     def initialize(self):
         print("[Voice Changer] [MMVCv13] Initializing... ")
         vcparams = VoiceChangerParamsManager.get_instance().params
-        configPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.configFile)
-        modelPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile)
+        configPath = os.path.join(
+            vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.configFile
+        )
+        modelPath = os.path.join(
+            vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile
+        )
 
         self.hps = get_hparams_from_file(configPath)
         if self.slotInfo.isONNX:
@@ -77,7 +82,13 @@ def initialize(self):
                 provider_options=options,
             )
         else:
-            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)
+            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
+            )
             self.net_g.eval()
             load_checkpoint(modelPath, self.net_g, None)
 
@@ -89,7 +100,11 @@ def initialize(self):
     def getOnnxExecutionProvider(self):
         availableProviders = onnxruntime.get_available_providers()
         devNum = torch.cuda.device_count()
-        if self.settings.gpu >= 0 and "CUDAExecutionProvider" in availableProviders and devNum > 0:
+        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"], [{}]
@@ -110,7 +125,11 @@ def update_settings(self, key: str, val: int | float | str):
             if key == "gpu" and self.slotInfo.isONNX:
                 providers, options = self.getOnnxExecutionProvider()
                 vcparams = VoiceChangerParamsManager.get_instance().params
-                modelPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile)
+                modelPath = os.path.join(
+                    vcparams.model_dir,
+                    str(self.slotInfo.slotIndex),
+                    self.slotInfo.modelFile,
+                )
                 self.onnx_session = onnxruntime.InferenceSession(
                     modelPath,
                     providers=providers,
@@ -136,7 +155,9 @@ def update_settings(self, key: str, val: int | float | str):
     def get_info(self):
         data = asdict(self.settings)
 
-        data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session is not None else []
+        data["onnxExecutionProviders"] = (
+            self.onnx_session.get_providers() if self.onnx_session is not None else []
+        )
         return data
 
     def get_processing_sampling_rate(self):
@@ -166,7 +187,9 @@ def generate_input(
         newData = newData.astype(np.float32) / self.hps.data.max_wav_value
 
         if self.audio_buffer is not None:
-            self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)  # 過去のデータに連結
+            self.audio_buffer = np.concatenate(
+                [self.audio_buffer, newData], 0
+            )  # 過去のデータに連結
         else:
             self.audio_buffer = newData
 
@@ -175,7 +198,9 @@ def generate_input(
         # if convertSize < 8192:
         #     convertSize = 8192
         if convertSize % self.hps.data.hop_length != 0:  # モデルの出力のホップサイズで切り捨てが発生するので補う。
-            convertSize = convertSize + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length))
+            convertSize = convertSize + (
+                self.hps.data.hop_length - (convertSize % self.hps.data.hop_length)
+            )
 
         convertOffset = -1 * convertSize
         self.audio_buffer = self.audio_buffer[convertOffset:]  # 変換対象の部分だけ抽出
@@ -207,9 +232,7 @@ def _onnx_inference(self, data):
                     "sid_src": sid_src.numpy(),
                     "sid_tgt": sid_tgt1.numpy(),
                 },
-            )[
-                0
-            ][0, 0]
+            )[0][0, 0]
             * self.hps.data.max_wav_value
         )
         return audio1
@@ -225,10 +248,19 @@ def _pyTorch_inference(self, data):
             dev = torch.device("cuda", index=self.settings.gpu)
 
         with torch.no_grad():
-            x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.to(dev) for x in data]
+            x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
+                x.to(dev) for x in data
+            ]
             sid_target = torch.LongTensor([self.settings.dstId]).to(dev)
 
-            audio1 = self.net_g.to(dev).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_target)[0, 0].data * self.hps.data.max_wav_value
+            audio1 = (
+                self.net_g.to(dev)
+                .voice_conversion(
+                    spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_target
+                )[0, 0]
+                .data
+                * self.hps.data.max_wav_value
+            )
             result = audio1.float().cpu().numpy()
 
         return result
@@ -265,5 +297,5 @@ def get_model_current(self):
             {
                 "key": "dstId",
                 "val": self.settings.dstId,
-            }
+            },
         ]
diff --git a/server/voice_changer/MMVCv15/MMVCv15.py b/server/voice_changer/MMVCv15/MMVCv15.py
index 51849c1d4..6b1663351 100644
--- a/server/voice_changer/MMVCv15/MMVCv15.py
+++ b/server/voice_changer/MMVCv15/MMVCv15.py
@@ -2,7 +2,7 @@
 import os
 from data.ModelSlot import MMVCv15ModelSlot
 from voice_changer.VoiceChangerParamsManager import VoiceChangerParamsManager
-from voice_changer.utils.VoiceChangerModel import AudioInOut
+from voice_changer.utils.VoiceChangerModel import AudioInOut, VoiceChangerModel
 
 if sys.platform.startswith("darwin"):
     baseDir = [x for x in sys.path if x.endswith("Contents/MacOS")]
@@ -56,9 +56,10 @@ class MMVCv15Settings:
     strData = ["f0Detector"]
 
 
-class MMVCv15:
+class MMVCv15(VoiceChangerModel):
     def __init__(self, slotInfo: MMVCv15ModelSlot):
         print("[Voice Changer] [MMVCv15] Creating instance ")
+        self.voiceChangerType = "MMVCv15"
         self.settings = MMVCv15Settings()
         self.net_g = None
         self.onnx_session: onnxruntime.InferenceSession | None = None
@@ -72,8 +73,12 @@ def __init__(self, slotInfo: MMVCv15ModelSlot):
     def initialize(self):
         print("[Voice Changer] [MMVCv15] Initializing... ")
         vcparams = VoiceChangerParamsManager.get_instance().params
-        configPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.configFile)
-        modelPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile)
+        configPath = os.path.join(
+            vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.configFile
+        )
+        modelPath = os.path.join(
+            vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile
+        )
 
         self.hps = get_hparams_from_file(configPath)
 
@@ -110,7 +115,11 @@ def initialize(self):
                 # print("ONNX INPUT SHAPE", i.name, i.shape)
                 if i.name == "sin":
                     self.onxx_input_length = i.shape[2]
-                    self.settings.maxInputLength = self.onxx_input_length - (0.012 * self.hps.data.sampling_rate) - 1024 # onnxの場合は入力長固(crossfadeの1024は仮) # NOQA
+                    self.settings.maxInputLength = (
+                        self.onxx_input_length
+                        - (0.012 * self.hps.data.sampling_rate)
+                        - 1024
+                    )  # onnxの場合は入力長固(crossfadeの1024は仮) # NOQA
         else:
             self.net_g.eval()
             load_checkpoint(modelPath, self.net_g, None)
@@ -125,7 +134,11 @@ def initialize(self):
     def getOnnxExecutionProvider(self):
         availableProviders = onnxruntime.get_available_providers()
         devNum = torch.cuda.device_count()
-        if self.settings.gpu >= 0 and "CUDAExecutionProvider" in availableProviders and devNum > 0:
+        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"], [{}]
@@ -145,7 +158,11 @@ def update_settings(self, key: str, val: int | float | str):
             if key == "gpu" and self.slotInfo.isONNX:
                 providers, options = self.getOnnxExecutionProvider()
                 vcparams = VoiceChangerParamsManager.get_instance().params
-                modelPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile)
+                modelPath = os.path.join(
+                    vcparams.model_dir,
+                    str(self.slotInfo.slotIndex),
+                    self.slotInfo.modelFile,
+                )
                 self.onnx_session = onnxruntime.InferenceSession(
                     modelPath,
                     providers=providers,
@@ -155,7 +172,11 @@ def update_settings(self, key: str, val: int | float | str):
                 for i in inputs_info:
                     if i.name == "sin":
                         self.onxx_input_length = i.shape[2]
-                        self.settings.maxInputLength = self.onxx_input_length - (0.012 * self.hps.data.sampling_rate) - 1024 # onnxの場合は入力長固(crossfadeの1024は仮) # NOQA
+                        self.settings.maxInputLength = (
+                            self.onxx_input_length
+                            - (0.012 * self.hps.data.sampling_rate)
+                            - 1024
+                        )  # onnxの場合は入力長固(crossfadeの1024は仮) # NOQA
         elif key in self.settings.floatData:
             setattr(self.settings, key, float(val))
         elif key in self.settings.strData:
@@ -168,7 +189,9 @@ def update_settings(self, key: str, val: int | float | str):
     def get_info(self):
         data = asdict(self.settings)
 
-        data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session is not None else []
+        data["onnxExecutionProviders"] = (
+            self.onnx_session.get_providers() if self.onnx_session is not None else []
+        )
         return data
 
     def get_processing_sampling_rate(self):
@@ -179,7 +202,9 @@ def get_processing_sampling_rate(self):
     def _get_f0(self, detector: str, newData: AudioInOut):
         audio_norm_np = newData.astype(np.float64)
         if detector == "dio":
-            _f0, _time = pw.dio(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5)
+            _f0, _time = pw.dio(
+                audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5
+            )
             f0 = pw.stonemask(audio_norm_np, _f0, _time, self.hps.data.sampling_rate)
         else:
             f0, t = pw.harvest(
@@ -189,7 +214,9 @@ def _get_f0(self, detector: str, newData: AudioInOut):
                 f0_floor=71.0,
                 f0_ceil=1000.0,
             )
-        f0 = convert_continuos_f0(f0, int(audio_norm_np.shape[0] / self.hps.data.hop_length))
+        f0 = convert_continuos_f0(
+            f0, int(audio_norm_np.shape[0] / self.hps.data.hop_length)
+        )
         f0 = torch.from_numpy(f0.astype(np.float32))
         return f0
 
@@ -216,12 +243,16 @@ def generate_input(
     ):
         # maxInputLength を更新(ここでやると非効率だが、とりあえず。)
         if self.slotInfo.isONNX:
-            self.settings.maxInputLength = self.onxx_input_length - crossfadeSize - solaSearchFrame # onnxの場合は入力長固(crossfadeの1024は仮) # NOQA get_infoで返る値。この関数内の処理では使わない。
+            self.settings.maxInputLength = (
+                self.onxx_input_length - crossfadeSize - solaSearchFrame
+            )  # onnxの場合は入力長固(crossfadeの1024は仮) # NOQA get_infoで返る値。この関数内の処理では使わない。
 
         newData = newData.astype(np.float32) / self.hps.data.max_wav_value
 
         if self.audio_buffer is not None:
-            self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)  # 過去のデータに連結
+            self.audio_buffer = np.concatenate(
+                [self.audio_buffer, newData], 0
+            )  # 過去のデータに連結
         else:
             self.audio_buffer = newData
 
@@ -230,7 +261,9 @@ def generate_input(
         # if convertSize < 8192:
         #     convertSize = 8192
         if convertSize % self.hps.data.hop_length != 0:  # モデルの出力のホップサイズで切り捨てが発生するので補う。
-            convertSize = convertSize + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length))
+            convertSize = convertSize + (
+                self.hps.data.hop_length - (convertSize % self.hps.data.hop_length)
+            )
 
         # ONNX は固定長
         if self.slotInfo.isONNX:
@@ -266,9 +299,7 @@ def _onnx_inference(self, data):
                     "sid_src": sid_src.numpy(),
                     "sid_tgt": sid_tgt1.numpy(),
                 },
-            )[
-                0
-            ][0, 0]
+            )[0][0, 0]
             * self.hps.data.max_wav_value
         )
         return audio1
@@ -287,7 +318,12 @@ def _pyTorch_inference(self, data):
             sid_src = sid_src.to(dev)
             sid_target = torch.LongTensor([self.settings.dstId]).to(dev)
 
-            audio1 = self.net_g.to(dev).voice_conversion(spec, spec_lengths, f0, sid_src, sid_target)[0, 0].data * self.hps.data.max_wav_value
+            audio1 = (
+                self.net_g.to(dev)
+                .voice_conversion(spec, spec_lengths, f0, sid_src, sid_target)[0, 0]
+                .data
+                * self.hps.data.max_wav_value
+            )
             result = audio1.float().cpu().numpy()
         return result
 
@@ -316,7 +352,7 @@ def __del__(self):
                 if file_path.find(remove_path + os.path.sep) >= 0:
                     # print("remove", key, file_path)
                     sys.modules.pop(key)
-            except: # NOQA
+            except:  # NOQA
                 pass
 
     def get_model_current(self):
@@ -332,5 +368,5 @@ def get_model_current(self):
             {
                 "key": "f0Factor",
                 "val": self.settings.f0Factor,
-            }
+            },
         ]
diff --git a/server/voice_changer/RVC/RVCr2.py b/server/voice_changer/RVC/RVCr2.py
index 01ad1cbd3..0422f882b 100644
--- a/server/voice_changer/RVC/RVCr2.py
+++ b/server/voice_changer/RVC/RVCr2.py
@@ -1,6 +1,6 @@
-'''
+"""
 VoiceChangerV2向け
-'''
+"""
 from dataclasses import asdict
 import numpy as np
 import torch
@@ -9,7 +9,12 @@
 
 from voice_changer.RVC.RVCSettings import RVCSettings
 from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
-from voice_changer.utils.VoiceChangerModel import AudioInOut, PitchfInOut, FeatureInOut, VoiceChangerModel
+from voice_changer.utils.VoiceChangerModel import (
+    AudioInOut,
+    PitchfInOut,
+    FeatureInOut,
+    VoiceChangerModel,
+)
 from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
 from voice_changer.RVC.onnxExporter.export2onnx import export2onnx
 from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
@@ -17,7 +22,11 @@
 from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
 from voice_changer.RVC.pipeline.Pipeline import Pipeline
 
-from Exceptions import DeviceCannotSupportHalfPrecisionException, PipelineCreateException, PipelineNotInitializedException
+from Exceptions import (
+    DeviceCannotSupportHalfPrecisionException,
+    PipelineCreateException,
+    PipelineNotInitializedException,
+)
 import resampy
 from typing import cast
 
@@ -27,6 +36,8 @@
 class RVCr2(VoiceChangerModel):
     def __init__(self, params: VoiceChangerParams, slotInfo: RVCModelSlot):
         logger.info("[Voice Changer] [RVCr2] Creating instance ")
+        self.voiceChangerType = "RVC"
+
         self.deviceManager = DeviceManager.get_instance()
         EmbedderManager.initialize(params)
         PitchExtractorManager.initialize(params)
@@ -48,9 +59,13 @@ def initialize(self):
 
         # pipelineの生成
         try:
-            self.pipeline = createPipeline(self.params, self.slotInfo, self.settings.gpu, self.settings.f0Detector)
+            self.pipeline = createPipeline(
+                self.params, self.slotInfo, self.settings.gpu, self.settings.f0Detector
+            )
         except PipelineCreateException as e:  # NOQA
-            logger.error("[Voice Changer] pipeline create failed. check your model is valid.")
+            logger.error(
+                "[Voice Changer] pipeline create failed. check your model is valid."
+            )
             return
 
         # その他の設定
@@ -76,7 +91,9 @@ def update_settings(self, key: str, val: int | float | str):
         elif key in self.settings.strData:
             setattr(self.settings, key, str(val))
             if key == "f0Detector" and self.pipeline is not None:
-                pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector, self.settings.gpu)
+                pitchExtractor = PitchExtractorManager.getPitchExtractor(
+                    self.settings.f0Detector, self.settings.gpu
+                )
                 self.pipeline.setPitchExtractor(pitchExtractor)
         else:
             return False
@@ -99,7 +116,7 @@ def generate_input(
         newData: AudioInOut,
         crossfadeSize: int,
         solaSearchFrame: int,
-        extra_frame: int
+        extra_frame: int,
     ):
         # 16k で入ってくる。
         inputSize = newData.shape[0]
@@ -110,26 +127,47 @@ def generate_input(
             # 過去のデータに連結
             self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)
             if self.slotInfo.f0:
-                self.pitchf_buffer = np.concatenate([self.pitchf_buffer, np.zeros(newFeatureLength)], 0)
-            self.feature_buffer = np.concatenate([self.feature_buffer, np.zeros([newFeatureLength, self.slotInfo.embChannels])], 0)
+                self.pitchf_buffer = np.concatenate(
+                    [self.pitchf_buffer, np.zeros(newFeatureLength)], 0
+                )
+            self.feature_buffer = np.concatenate(
+                [
+                    self.feature_buffer,
+                    np.zeros([newFeatureLength, self.slotInfo.embChannels]),
+                ],
+                0,
+            )
         else:
             self.audio_buffer = newData
             if self.slotInfo.f0:
                 self.pitchf_buffer = np.zeros(newFeatureLength)
-            self.feature_buffer = np.zeros([newFeatureLength, self.slotInfo.embChannels])
+            self.feature_buffer = np.zeros(
+                [newFeatureLength, self.slotInfo.embChannels]
+            )
 
         convertSize = inputSize + crossfadeSize + solaSearchFrame + extra_frame
 
         if convertSize % 160 != 0:  # モデルの出力のホップサイズで切り捨てが発生するので補う。
             convertSize = convertSize + (160 - (convertSize % 160))
-        outSize = int(((convertSize - extra_frame) / 16000) * self.slotInfo.samplingRate) 
+        outSize = int(
+            ((convertSize - extra_frame) / 16000) * self.slotInfo.samplingRate
+        )
 
         # バッファがたまっていない場合はzeroで補う
         if self.audio_buffer.shape[0] < convertSize:
-            self.audio_buffer = np.concatenate([np.zeros([convertSize]), self.audio_buffer])
+            self.audio_buffer = np.concatenate(
+                [np.zeros([convertSize]), self.audio_buffer]
+            )
             if self.slotInfo.f0:
-                self.pitchf_buffer = np.concatenate([np.zeros([convertSize // 160]), self.pitchf_buffer])
-            self.feature_buffer = np.concatenate([np.zeros([convertSize // 160, self.slotInfo.embChannels]), self.feature_buffer])
+                self.pitchf_buffer = np.concatenate(
+                    [np.zeros([convertSize // 160]), self.pitchf_buffer]
+                )
+            self.feature_buffer = np.concatenate(
+                [
+                    np.zeros([convertSize // 160, self.slotInfo.embChannels]),
+                    self.feature_buffer,
+                ]
+            )
 
         # 不要部分をトリミング
         convertOffset = -1 * convertSize
@@ -147,9 +185,18 @@ def generate_input(
         vol = max(vol, self.prevVol * 0.0)
         self.prevVol = vol
 
-        return (self.audio_buffer, self.pitchf_buffer, self.feature_buffer, convertSize, vol, outSize)
+        return (
+            self.audio_buffer,
+            self.pitchf_buffer,
+            self.feature_buffer,
+            convertSize,
+            vol,
+            outSize,
+        )
 
-    def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int):
+    def inference(
+        self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int
+    ):
         if self.pipeline is None:
             logger.info("[Voice Changer] Pipeline is not initialized.")
             raise PipelineNotInitializedException()
@@ -165,10 +212,14 @@ def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_
         )
         crossfade_frame = int((crossfade_frame / self.inputSampleRate) * 16000)
         sola_search_frame = int((sola_search_frame / self.inputSampleRate) * 16000)
-        extra_frame = int((self.settings.extraConvertSize / self.inputSampleRate) * 16000)
+        extra_frame = int(
+            (self.settings.extraConvertSize / self.inputSampleRate) * 16000
+        )
 
         # 入力データ生成
-        data = self.generate_input(receivedData, crossfade_frame, sola_search_frame, extra_frame)
+        data = self.generate_input(
+            receivedData, crossfade_frame, sola_search_frame, extra_frame
+        )
 
         audio = data[0]
         pitchf = data[1]
@@ -181,7 +232,7 @@ def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_
             return np.zeros(convertSize).astype(np.int16) * np.sqrt(vol)
 
         device = self.pipeline.device
-        
+
         audio = torch.from_numpy(audio).to(device=device, dtype=torch.float32)
         repeat = 1 if self.settings.rvcQuality else 0
         sid = self.settings.dstId
@@ -192,7 +243,7 @@ def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_
         if_f0 = 1 if self.slotInfo.f0 else 0
         embOutputLayer = self.slotInfo.embOutputLayer
         useFinalProj = self.slotInfo.useFinalProj
-        
+
         try:
             audio_out, self.pitchf_buffer, self.feature_buffer = self.pipeline.exec(
                 sid,
@@ -203,12 +254,14 @@ def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_
                 index_rate,
                 if_f0,
                 # 0,
-                self.settings.extraConvertSize / self.inputSampleRate if self.settings.silenceFront else 0.,  # extaraDataSizeの秒数。入力のサンプリングレートで算出
+                self.settings.extraConvertSize / self.inputSampleRate
+                if self.settings.silenceFront
+                else 0.0,  # extaraDataSizeの秒数。入力のサンプリングレートで算出
                 embOutputLayer,
                 useFinalProj,
                 repeat,
                 protect,
-                outSize
+                outSize,
             )
             # result = audio_out.detach().cpu().numpy() * np.sqrt(vol)
             result = audio_out[-outSize:].detach().cpu().numpy() * np.sqrt(vol)
@@ -224,7 +277,9 @@ def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_
 
             return result
         except DeviceCannotSupportHalfPrecisionException as e:  # NOQA
-            logger.warn("[Device Manager] Device cannot support half precision. Fallback to float....")
+            logger.warn(
+                "[Device Manager] Device cannot support half precision. Fallback to float...."
+            )
             self.deviceManager.setForceTensor(True)
             self.initialize()
             # raise e
diff --git a/server/voice_changer/SoVitsSvc40/SoVitsSvc40.py b/server/voice_changer/SoVitsSvc40/SoVitsSvc40.py
index 80cfe1b76..19b0fc1ae 100644
--- a/server/voice_changer/SoVitsSvc40/SoVitsSvc40.py
+++ b/server/voice_changer/SoVitsSvc40/SoVitsSvc40.py
@@ -3,7 +3,7 @@
 from data.ModelSlot import SoVitsSvc40ModelSlot
 from voice_changer.VoiceChangerParamsManager import VoiceChangerParamsManager
 
-from voice_changer.utils.VoiceChangerModel import AudioInOut
+from voice_changer.utils.VoiceChangerModel import AudioInOut, VoiceChangerModel
 from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
 
 if sys.platform.startswith("darwin"):
@@ -27,7 +27,13 @@
 
 # from models import SynthesizerTrn  # type:ignore
 from .models.models import SynthesizerTrn
-from .models.utils import interpolate_f0, get_hparams_from_file, load_checkpoint, repeat_expand_2d, get_hubert_content
+from .models.utils import (
+    interpolate_f0,
+    get_hparams_from_file,
+    load_checkpoint,
+    repeat_expand_2d,
+    get_hubert_content,
+)
 from .models.cluster import get_cluster_model, get_cluster_center_result
 from fairseq import checkpoint_utils
 import librosa
@@ -64,9 +70,10 @@ class SoVitsSvc40Settings:
     strData = ["f0Detector"]
 
 
-class SoVitsSvc40:
+class SoVitsSvc40(VoiceChangerModel):
     def __init__(self, params: VoiceChangerParams, slotInfo: SoVitsSvc40ModelSlot):
         print("[Voice Changer] [so-vits-svc40] Creating instance ")
+        self.voiceChangerType = "so-vits-svc-40"
         self.settings = SoVitsSvc40Settings()
         self.net_g = None
         self.onnx_session = None
@@ -94,20 +101,31 @@ def __init__(self, params: VoiceChangerParams, slotInfo: SoVitsSvc40ModelSlot):
     def initialize(self):
         print("[Voice Changer] [so-vits-svc40] Initializing... ")
         vcparams = VoiceChangerParamsManager.get_instance().params
-        configPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.configFile)
-        modelPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile)
+        configPath = os.path.join(
+            vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.configFile
+        )
+        modelPath = os.path.join(
+            vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile
+        )
         self.hps = get_hparams_from_file(configPath)
         self.settings.speakers = self.hps.spk
 
         # cluster
         try:
             if self.slotInfo.clusterFile is not None:
-                clusterPath = os.path.join(vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.clusterFile)
+                clusterPath = os.path.join(
+                    vcparams.model_dir,
+                    str(self.slotInfo.slotIndex),
+                    self.slotInfo.clusterFile,
+                )
                 self.cluster_model = get_cluster_model(clusterPath)
             else:
                 self.cluster_model = None
         except Exception as e:
-            print("[Voice Changer] [so-vits-svc40] EXCEPTION during loading cluster model ", e)
+            print(
+                "[Voice Changer] [so-vits-svc40] EXCEPTION during loading cluster model ",
+                e,
+            )
             print("[Voice Changer] [so-vits-svc40] fallback to without cluster")
             self.cluster_model = None
 
@@ -132,7 +150,11 @@ def initialize(self):
     def getOnnxExecutionProvider(self):
         availableProviders = onnxruntime.get_available_providers()
         devNum = torch.cuda.device_count()
-        if self.settings.gpu >= 0 and "CUDAExecutionProvider" in availableProviders and devNum > 0:
+        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"], [{}]
@@ -170,7 +192,9 @@ def update_settings(self, key: str, val: int | float | str):
     def get_info(self):
         data = asdict(self.settings)
 
-        data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session is not None else []
+        data["onnxExecutionProviders"] = (
+            self.onnx_session.get_providers() if self.onnx_session is not None else []
+        )
 
         return data
 
@@ -196,7 +220,9 @@ def get_unit_f0(self, audio_buffer, tran):
             )
 
         if wav_44k.shape[0] % self.hps.data.hop_length != 0:
-            print(f" !!! !!! !!! wav size not multiple of hopsize: {wav_44k.shape[0] / self.hps.data.hop_length}")
+            print(
+                f" !!! !!! !!! wav size not multiple of hopsize: {wav_44k.shape[0] / self.hps.data.hop_length}"
+            )
 
         f0, uv = interpolate_f0(f0)
         f0 = torch.FloatTensor(f0)
@@ -205,7 +231,9 @@ def get_unit_f0(self, audio_buffer, tran):
         f0 = f0.unsqueeze(0)
         uv = uv.unsqueeze(0)
 
-        wav16k_numpy = librosa.resample(audio_buffer, orig_sr=self.hps.data.sampling_rate, target_sr=16000)
+        wav16k_numpy = librosa.resample(
+            audio_buffer, orig_sr=self.hps.data.sampling_rate, target_sr=16000
+        )
         wav16k_tensor = torch.from_numpy(wav16k_numpy)
 
         if (self.settings.gpu < 0 or self.gpu_num == 0) or self.slotInfo.isONNX:
@@ -226,7 +254,9 @@ def get_unit_f0(self, audio_buffer, tran):
             if self.hps.model.ssl_dim == 768:
                 self.hubert_model = self.hubert_model.to(dev)
                 wav16k_tensor = wav16k_tensor.to(dev)
-                c = get_hubert_content_layer9(self.hubert_model, wav_16k_tensor=wav16k_tensor)
+                c = get_hubert_content_layer9(
+                    self.hubert_model, wav_16k_tensor=wav16k_tensor
+                )
             else:
                 self.hubert_model = self.hubert_model.to(dev)
                 wav16k_tensor = wav16k_tensor.to(dev)
@@ -237,16 +267,29 @@ def get_unit_f0(self, audio_buffer, tran):
 
         c = repeat_expand_2d(c.squeeze(0), f0.shape[1])
 
-        if self.settings.clusterInferRatio != 0 and hasattr(self, "cluster_model") and self.cluster_model is not None:
-            speaker = [key for key, value in self.settings.speakers.items() if value == self.settings.dstId]
+        if (
+            self.settings.clusterInferRatio != 0
+            and hasattr(self, "cluster_model")
+            and self.cluster_model is not None
+        ):
+            speaker = [
+                key
+                for key, value in self.settings.speakers.items()
+                if value == self.settings.dstId
+            ]
             if len(speaker) != 1:
                 pass
                 # print("not only one speaker found.", speaker)
             else:
-                cluster_c = get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker[0]).T
+                cluster_c = get_cluster_center_result(
+                    self.cluster_model, c.cpu().numpy().T, speaker[0]
+                ).T
                 cluster_c = torch.FloatTensor(cluster_c).to(dev)
                 c = c.to(dev)
-                c = self.settings.clusterInferRatio * cluster_c + (1 - self.settings.clusterInferRatio) * c
+                c = (
+                    self.settings.clusterInferRatio * cluster_c
+                    + (1 - self.settings.clusterInferRatio) * c
+                )
 
         c = c.unsqueeze(0)
         return c, f0, uv
@@ -261,14 +304,20 @@ def generate_input(
         newData = newData.astype(np.float32) / self.hps.data.max_wav_value
 
         if self.audio_buffer is not None:
-            self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)  # 過去のデータに連結
+            self.audio_buffer = np.concatenate(
+                [self.audio_buffer, newData], 0
+            )  # 過去のデータに連結
         else:
             self.audio_buffer = newData
 
-        convertSize = inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
+        convertSize = (
+            inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
+        )
 
         if convertSize % self.hps.data.hop_length != 0:  # モデルの出力のホップサイズで切り捨てが発生するので補う。
-            convertSize = convertSize + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length))
+            convertSize = convertSize + (
+                self.hps.data.hop_length - (convertSize % self.hps.data.hop_length)
+            )
 
         convertOffset = -1 * convertSize
         self.audio_buffer = self.audio_buffer[convertOffset:]  # 変換対象の部分だけ抽出
@@ -306,7 +355,9 @@ def _onnx_inference(self, data):
                     "f0": f0.astype(np.float32),
                     "uv": uv.astype(np.float32),
                     "g": sid_target.astype(np.int64),
-                    "noise_scale": np.array([self.settings.noiseScale]).astype(np.float32),
+                    "noise_scale": np.array([self.settings.noiseScale]).astype(
+                        np.float32
+                    ),
                     # "predict_f0": np.array([self.settings.dstId]).astype(np.int64),
                 },
             )[0][0, 0]
@@ -385,8 +436,7 @@ def __del__(self):
                 pass
 
     def get_model_current(self):
-        return [
-        ]
+        return []
 
 
 def resize_f0(x, target_len):
diff --git a/server/voice_changer/VoiceChangerV2.py b/server/voice_changer/VoiceChangerV2.py
index c6466b8b5..81e473b72 100644
--- a/server/voice_changer/VoiceChangerV2.py
+++ b/server/voice_changer/VoiceChangerV2.py
@@ -1,4 +1,4 @@
-'''
+"""
 ■ VoiceChangerV2
 - VoiceChangerとの差分
 ・リサンプル処理の無駄を省くため、VoiceChangerModelにリサンプル処理を移譲
@@ -7,7 +7,7 @@
 - 適用VoiceChangerModel
 ・DiffusionSVC
 ・RVC
-'''
+"""
 
 from typing import Any, Union
 
@@ -18,7 +18,8 @@
 from dataclasses import dataclass, asdict, field
 import onnxruntime
 from mods.log_control import VoiceChangaerLogger
-from voice_changer.Beatrice.Beatrice import Beatrice
+
+# from voice_changer.Beatrice.Beatrice import Beatrice
 
 from voice_changer.IORecorder import IORecorder
 
@@ -89,27 +90,38 @@ def __init__(self, params: VoiceChangerParams):
         self.params = params
         self.gpu_num = torch.cuda.device_count()
         self.prev_audio = np.zeros(4096)
-        self.mps_enabled: bool = getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available()
+        self.mps_enabled: bool = (
+            getattr(torch.backends, "mps", None) is not None
+            and torch.backends.mps.is_available()
+        )
         self.onnx_device = onnxruntime.get_device()
         self.noCrossFade = False
 
-        logger.info(f"VoiceChangerV2 Initialized (GPU_NUM(cuda):{self.gpu_num}, mps_enabled:{self.mps_enabled}, onnx_device:{self.onnx_device})")
+        logger.info(
+            f"VoiceChangerV2 Initialized (GPU_NUM(cuda):{self.gpu_num}, mps_enabled:{self.mps_enabled}, onnx_device:{self.onnx_device})"
+        )
 
     def setModel(self, model: VoiceChangerModel):
         self.voiceChanger = model
-        self.voiceChanger.setSamplingRate(self.settings.inputSampleRate, self.settings.outputSampleRate)
-        if isinstance(model, Beatrice):
+        self.voiceChanger.setSamplingRate(
+            self.settings.inputSampleRate, self.settings.outputSampleRate
+        )
+        if model.voiceChangerType == "Beatrice":
             self.noCrossFade = True
         else:
             self.noCrossFade = False
 
     def setInputSampleRate(self, sr: int):
         self.settings.inputSampleRate = sr
-        self.voiceChanger.setSamplingRate(self.settings.inputSampleRate, self.settings.outputSampleRate)
+        self.voiceChanger.setSamplingRate(
+            self.settings.inputSampleRate, self.settings.outputSampleRate
+        )
 
     def setOutputSampleRate(self, sr: int):
         self.settings.outputSampleRate = sr
-        self.voiceChanger.setSamplingRate(self.settings.inputSampleRate, self.settings.outputSampleRate)
+        self.voiceChanger.setSamplingRate(
+            self.settings.inputSampleRate, self.settings.outputSampleRate
+        )
 
     def get_info(self):
         data = asdict(self.settings)
@@ -128,7 +140,9 @@ def update_settings(self, key: str, val: Any):
         if key == "serverAudioStated" and val == 0:
             self.settings.inputSampleRate = 48000
             self.settings.outputSampleRate = 48000
-            self.voiceChanger.setSamplingRate(self.settings.inputSampleRate, self.settings.outputSampleRate)
+            self.voiceChanger.setSamplingRate(
+                self.settings.inputSampleRate, self.settings.outputSampleRate
+            )
 
         if key in self.settings.intData:
             setattr(self.settings, key, int(val))
@@ -137,7 +151,12 @@ def update_settings(self, key: str, val: Any):
             if key == "recordIO" and val == 1:
                 if hasattr(self, "ioRecorder"):
                     self.ioRecorder.close()
-                self.ioRecorder = IORecorder(STREAM_INPUT_FILE, STREAM_OUTPUT_FILE, self.settings.inputSampleRate, self.settings.outputSampleRate)
+                self.ioRecorder = IORecorder(
+                    STREAM_INPUT_FILE,
+                    STREAM_OUTPUT_FILE,
+                    self.settings.inputSampleRate,
+                    self.settings.outputSampleRate,
+                )
             if key == "recordIO" and val == 0:
                 if hasattr(self, "ioRecorder"):
                     self.ioRecorder.close()
@@ -146,7 +165,9 @@ def update_settings(self, key: str, val: Any):
                 if hasattr(self, "ioRecorder"):
                     self.ioRecorder.close()
             if key == "inputSampleRate" or key == "outputSampleRate":
-                self.voiceChanger.setSamplingRate(self.settings.inputSampleRate, self.settings.outputSampleRate)
+                self.voiceChanger.setSamplingRate(
+                    self.settings.inputSampleRate, self.settings.outputSampleRate
+                )
         elif key in self.settings.floatData:
             setattr(self.settings, key, float(val))
         elif key in self.settings.strData:
@@ -159,7 +180,12 @@ def update_settings(self, key: str, val: Any):
         return self.get_info()
 
     def _generate_strength(self, crossfadeSize: int):
-        if self.crossfadeSize != crossfadeSize or self.currentCrossFadeOffsetRate != self.settings.crossFadeOffsetRate or self.currentCrossFadeEndRate != self.settings.crossFadeEndRate or self.currentCrossFadeOverlapSize != self.settings.crossFadeOverlapSize:
+        if (
+            self.crossfadeSize != crossfadeSize
+            or self.currentCrossFadeOffsetRate != self.settings.crossFadeOffsetRate
+            or self.currentCrossFadeEndRate != self.settings.crossFadeEndRate
+            or self.currentCrossFadeOverlapSize != self.settings.crossFadeOverlapSize
+        ):
             self.crossfadeSize = crossfadeSize
             self.currentCrossFadeOffsetRate = self.settings.crossFadeOffsetRate
             self.currentCrossFadeEndRate = self.settings.crossFadeEndRate
@@ -188,7 +214,9 @@ def _generate_strength(self, crossfadeSize: int):
                 ]
             )
 
-            logger.info(f"Generated Strengths: for prev:{self.np_prev_strength.shape}, for cur:{self.np_cur_strength.shape}")
+            logger.info(
+                f"Generated Strengths: for prev:{self.np_prev_strength.shape}, for cur:{self.np_cur_strength.shape}"
+            )
 
             # ひとつ前の結果とサイズが変わるため、記録は消去する。
             if hasattr(self, "np_prev_audio1") is True:
@@ -203,13 +231,19 @@ def get_processing_sampling_rate(self):
             return self.voiceChanger.get_processing_sampling_rate()
 
     #  receivedData: tuple of short
-    def on_request(self, receivedData: AudioInOut) -> tuple[AudioInOut, list[Union[int, float]]]:
+    def on_request(
+        self, receivedData: AudioInOut
+    ) -> tuple[AudioInOut, list[Union[int, float]]]:
         try:
             if self.voiceChanger is None:
-                raise VoiceChangerIsNotSelectedException("Voice Changer is not selected.")
+                raise VoiceChangerIsNotSelectedException(
+                    "Voice Changer is not selected."
+                )
 
             with Timer("main-process") as t:
-                processing_sampling_rate = self.voiceChanger.get_processing_sampling_rate()
+                processing_sampling_rate = (
+                    self.voiceChanger.get_processing_sampling_rate()
+                )
 
                 if self.noCrossFade:  # Beatrice
                     audio = self.voiceChanger.inference(
@@ -223,18 +257,22 @@ def on_request(self, receivedData: AudioInOut) -> tuple[AudioInOut, list[Union[i
                 else:
                     sola_search_frame = int(0.012 * processing_sampling_rate)
                     block_frame = receivedData.shape[0]
-                    crossfade_frame = min(self.settings.crossFadeOverlapSize, block_frame)
+                    crossfade_frame = min(
+                        self.settings.crossFadeOverlapSize, block_frame
+                    )
                     self._generate_strength(crossfade_frame)
-            
+
                     audio = self.voiceChanger.inference(
                         receivedData,
                         crossfade_frame=crossfade_frame,
-                        sola_search_frame=sola_search_frame
+                        sola_search_frame=sola_search_frame,
                     )
 
                     if hasattr(self, "sola_buffer") is True:
                         np.set_printoptions(threshold=10000)
-                        audio_offset = -1 * (sola_search_frame + crossfade_frame + block_frame)
+                        audio_offset = -1 * (
+                            sola_search_frame + crossfade_frame + block_frame
+                        )
                         audio = audio[audio_offset:]
 
                         # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC, https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI
@@ -259,16 +297,25 @@ def on_request(self, receivedData: AudioInOut) -> tuple[AudioInOut, list[Union[i
 
                         result = output_wav
                     else:
-                        logger.info("[Voice Changer] warming up... generating sola buffer.")
+                        logger.info(
+                            "[Voice Changer] warming up... generating sola buffer."
+                        )
                         result = np.zeros(4096).astype(np.int16)
 
-                    if hasattr(self, "sola_buffer") is True and sola_offset < sola_search_frame:
-                        offset = -1 * (sola_search_frame + crossfade_frame - sola_offset)
+                    if (
+                        hasattr(self, "sola_buffer") is True
+                        and sola_offset < sola_search_frame
+                    ):
+                        offset = -1 * (
+                            sola_search_frame + crossfade_frame - sola_offset
+                        )
                         end = -1 * (sola_search_frame - sola_offset)
                         sola_buf_org = audio[offset:end]
                         self.sola_buffer = sola_buf_org * self.np_prev_strength
                     else:
-                        self.sola_buffer = audio[-crossfade_frame:] * self.np_prev_strength
+                        self.sola_buffer = (
+                            audio[-crossfade_frame:] * self.np_prev_strength
+                        )
                         # self.sola_buffer = audio[- crossfade_frame:]
 
             mainprocess_time = t.secs
@@ -277,7 +324,9 @@ def on_request(self, receivedData: AudioInOut) -> tuple[AudioInOut, list[Union[i
             with Timer("post-process") as t:
                 result = result.astype(np.int16)
 
-                print_convert_processing(f" Output data size of {result.shape[0]}/{processing_sampling_rate}hz {result .shape[0]}/{self.settings.outputSampleRate}hz")
+                print_convert_processing(
+                    f" Output data size of {result.shape[0]}/{processing_sampling_rate}hz {result .shape[0]}/{self.settings.outputSampleRate}hz"
+                )
 
                 if receivedData.shape[0] != result.shape[0]:
                     outputData = pad_array(result, receivedData.shape[0])
@@ -291,7 +340,9 @@ def on_request(self, receivedData: AudioInOut) -> tuple[AudioInOut, list[Union[i
 
             postprocess_time = t.secs
 
-            print_convert_processing(f" [fin] Input/Output size:{receivedData.shape[0]},{outputData.shape[0]}")
+            print_convert_processing(
+                f" [fin] Input/Output size:{receivedData.shape[0]},{outputData.shape[0]}"
+            )
             perf = [0, mainprocess_time, postprocess_time]
 
             return outputData, perf
@@ -300,7 +351,9 @@ def on_request(self, receivedData: AudioInOut) -> tuple[AudioInOut, list[Union[i
             logger.warn(f"[Voice Changer] [Exception], {e}")
             return np.zeros(1).astype(np.int16), [0, 0, 0]
         except ONNXInputArgumentException as e:
-            logger.warn(f"[Voice Changer] [Exception] onnx are waiting valid input., {e}")
+            logger.warn(
+                f"[Voice Changer] [Exception] onnx are waiting valid input., {e}"
+            )
             return np.zeros(1).astype(np.int16), [0, 0, 0]
         except HalfPrecisionChangingException:
             logger.warn("[Voice Changer] Switching model configuration....")
@@ -312,7 +365,9 @@ def on_request(self, receivedData: AudioInOut) -> tuple[AudioInOut, list[Union[i
             logger.warn(f"[Voice Changer] embedder: {e}")
             return np.zeros(1).astype(np.int16), [0, 0, 0]
         except VoiceChangerIsNotSelectedException:
-            logger.warn("[Voice Changer] Voice Changer is not selected. Wait a bit and if there is no improvement, please re-select vc.")
+            logger.warn(
+                "[Voice Changer] Voice Changer is not selected. Wait a bit and if there is no improvement, please re-select vc."
+            )
             return np.zeros(1).astype(np.int16), [0, 0, 0]
         except DeviceCannotSupportHalfPrecisionException:
             # RVC.pyでfallback処理をするので、ここはダミーデータ返すだけ。
diff --git a/server/voice_changer/utils/VoiceChangerModel.py b/server/voice_changer/utils/VoiceChangerModel.py
index fbe40d6c4..1a3ad4b28 100644
--- a/server/voice_changer/utils/VoiceChangerModel.py
+++ b/server/voice_changer/utils/VoiceChangerModel.py
@@ -1,5 +1,6 @@
 from typing import Any, Protocol, TypeAlias
 import numpy as np
+from const import VoiceChangerType
 
 from voice_changer.utils.LoadModelParams import LoadModelParams
 
@@ -10,6 +11,8 @@
 
 
 class VoiceChangerModel(Protocol):
+    voiceChangerType: VoiceChangerType = "RVC"
+
     # loadModel: Callable[..., dict[str, Any]]
     def loadModel(self, params: LoadModelParams):
         ...
@@ -23,7 +26,13 @@ def get_info(self) -> dict[str, Any]:
     def inference(self, data: tuple[Any, ...]) -> Any:
         ...
 
-    def generate_input(self, newData: AudioInOut, inputSize: int, crossfadeSize: int, solaSearchFrame: int) -> tuple[Any, ...]:
+    def generate_input(
+        self,
+        newData: AudioInOut,
+        inputSize: int,
+        crossfadeSize: int,
+        solaSearchFrame: int,
+    ) -> tuple[Any, ...]:
         ...
 
     def update_settings(self, key: str, val: int | float | str) -> bool: