wip:refactor

client/lib/src/AudioStreamer.ts

@@ -58,12 +58,9 @@ export class AudioStreamer extends Duplex {
     }
 
     // Option Change
-    setServerUrl = (serverUrl: string, mode: Protocol, openTab: boolean = false) => {
+    setServerUrl = (serverUrl: string, mode: Protocol) => {
         this.serverUrl = serverUrl
         this.protocol = mode
-        if (openTab) {
-            window.open(serverUrl, '_blank')
-        }
         console.log(`[AudioStreamer] Server Setting:${this.serverUrl} ${this.protocol}`)
 
         this.createSocketIO()// mode check is done in the method.

client/lib/src/VoiceChangerClient.ts

@@ -2,7 +2,7 @@ import { VoiceChangerWorkletNode, VolumeListener } from "./VoiceChangerWorkletNo
 // @ts-ignore
 import workerjs from "raw-loader!../worklet/dist/index.js";
 import { VoiceFocusDeviceTransformer, VoiceFocusTransformDevice } from "amazon-chime-sdk-js";
-import { createDummyMediaStream } from "./util";
+import { createDummyMediaStream, validateUrl } from "./util";
 import { BufferSize, DefaultVoiceChangerOptions, DefaultVoiceChangerRequestParamas, Protocol, VoiceChangerMode, VoiceChangerRequestParamas, VOICE_CHANGER_CLIENT_EXCEPTION } from "./const";
 import MicrophoneStream from "microphone-stream";
 import { AudioStreamer, Callbacks, AudioStreamerListeners } from "./AudioStreamer";
@@ -169,7 +169,21 @@ export class VoiceChnagerClient {
     }
     // Audio Streamer Settingg
     setServerUrl = (serverUrl: string, mode: Protocol, openTab: boolean = false) => {
-        this.audioStreamer.setServerUrl(serverUrl, mode, openTab)
+        const url = validateUrl(serverUrl)
+        const pageUrl = `${location.protocol}//${location.host}`
+        console.log("SERVER CHECK", url, pageUrl)
+
+        if (url != pageUrl && location.protocol == "https:") {
+            if (openTab) {
+                const value = window.confirm("MMVC Server is different from this page's origin. Open tab to open ssl connection. OK? (You can close the opened tab after ssl connection succeed.)");
+                if (value) {
+                    window.open(url, '_blank')
+                } else {
+                    alert("Your voice conversion may fail...")
+                }
+            }
+        }
+        this.audioStreamer.setServerUrl(validateUrl(serverUrl), mode)
     }
 
     setRequestParams = (val: VoiceChangerRequestParamas) => {

client/lib/src/uploader.ts

@@ -1,7 +1,6 @@
 import { OnnxExecutionProvider } from "./const"
+import { validateUrl } from "./util"
 
-const DEBUG = false
-const DEBUG_BASE_URL = "http://localhost:18888"
 
 type FileChunk = {
     hash: number,
@@ -15,12 +14,10 @@ export type ServerInfo = {
     providers: string[]
 }
 
-
 export const getInfo = async (baseUrl: string) => {
-    const getInfoURL = DEBUG ? `${DEBUG_BASE_URL}/info` : `${baseUrl}/info`
+    const url = validateUrl(baseUrl) + "/info"
-
     const info = await new Promise<ServerInfo>((resolve) => {
-        const request = new Request(getInfoURL, {
+        const request = new Request(url, {
             method: 'GET',
         });
         fetch(request).then(async (response) => {
@@ -33,7 +30,7 @@ export const getInfo = async (baseUrl: string) => {
 
 
 export const uploadLargeFile = async (baseUrl: string, file: File, onprogress: (progress: number, end: boolean) => void) => {
-    const uploadURL = DEBUG ? `${DEBUG_BASE_URL}/upload_file` : `${baseUrl}/upload_file`
+    const url = validateUrl(baseUrl) + "/upload_file"
     onprogress(0, false)
     const size = 1024 * 1024;
     const fileChunks: FileChunk[] = [];
@@ -60,7 +57,7 @@ export const uploadLargeFile = async (baseUrl: string, file: File, onprogress: (
                 const formData = new FormData();
                 formData.append("file", chunk.chunk);
                 formData.append("filename", `${file.name}_${chunk.hash}`);
-                const request = new Request(uploadURL, {
+                const request = new Request(url, {
                     method: 'POST',
                     body: formData,
                 });
@@ -82,13 +79,12 @@ export const uploadLargeFile = async (baseUrl: string, file: File, onprogress: (
 }
 
 export const concatUploadedFile = async (baseUrl: string, file: File, chunkNum: number) => {
-    const loadModelURL = DEBUG ? `${DEBUG_BASE_URL}/concat_uploaded_file` : `${baseUrl}/concat_uploaded_file`
+    const url = validateUrl(baseUrl) + "/concat_uploaded_file"
-
     new Promise<void>((resolve) => {
         const formData = new FormData();
         formData.append("filename", file.name);
         formData.append("filenameChunkNum", "" + chunkNum);
-        const request = new Request(loadModelURL, {
+        const request = new Request(url, {
             method: 'POST',
             body: formData,
         });
@@ -100,13 +96,13 @@ export const concatUploadedFile = async (baseUrl: string, file: File, chunkNum:
 }
 
 export const loadModel = async (baseUrl: string, configFile: File, pyTorchModelFile: File | null, onnxModelFile: File | null) => {
-    const loadModelURL = DEBUG ? `${DEBUG_BASE_URL}/load_model` : `${baseUrl}/load_model`
+    const url = validateUrl(baseUrl) + "/load_model"
     const loadP = new Promise<void>((resolve) => {
         const formData = new FormData();
         formData.append("pyTorchModelFilename", pyTorchModelFile?.name || "-");
         formData.append("onnxModelFilename", onnxModelFile?.name || "-");
         formData.append("configFilename", configFile.name);
-        const request = new Request(loadModelURL, {
+        const request = new Request(url, {
             method: 'POST',
             body: formData,
         });
@@ -119,7 +115,7 @@ export const loadModel = async (baseUrl: string, configFile: File, pyTorchModelF
 }
 
 export const setOnnxExecutionProvider = async (baseUrl: string, provider: OnnxExecutionProvider) => {
-    const url = DEBUG ? `${DEBUG_BASE_URL}/set_onnx_provider` : `${baseUrl}/set_onnx_provider`
+    const url = validateUrl(baseUrl) + "/set_onnx_provider"
     const loadP = new Promise<void>((resolve) => {
         const formData = new FormData();
         formData.append("provider", provider);

client/lib/src/util.ts

@@ -57,3 +57,10 @@ export const fileSelectorAsDataURL = async (regex: string) => {
 }
 
 
+export const validateUrl = (url: string) => {
+
+    if (url.endsWith("/")) {
+        return url.substring(0, url.length - 1)
+    }
+    return url
+}

server/voice_changer/VoiceChanger.py

@@ -88,16 +88,11 @@ class VoiceChanger():
     def set_onnx_provider(self, provider:str):
         if hasattr(self, "onnx_session"):
             self.onnx_session.set_providers(providers=[provider])
-            print("ONNX_MDEOL!1", self.onnx_session.get_providers())
+            print("ONNX_MDEOL: ", self.onnx_session.get_providers())
             return {"provider":self.onnx_session.get_providers()}
         
 
-    def on_request(self, gpu, srcId, dstId, timestamp, convertChunkNum, crossFadeLowerValue, crossFadeOffsetRate, crossFadeEndRate, unpackedData):
+    def _generate_strength(self, crossFadeOffsetRate, crossFadeEndRate, unpackedData):
-        convertSize = convertChunkNum * 128 # 128sample/1chunk
-        if unpackedData.shape[0] * 2 > convertSize:
-            convertSize = unpackedData.shape[0] * 2
-
-        print("convert Size", convertChunkNum, convertSize)
 
         if self.crossFadeOffsetRate != crossFadeOffsetRate or self.crossFadeEndRate != crossFadeEndRate or self.unpackedData_length != unpackedData.shape[0]:
             self.crossFadeOffsetRate = crossFadeOffsetRate
@@ -128,25 +123,37 @@ class VoiceChanger():
             if hasattr(self, 'prev_audio1') == True:
                 delattr(self,"prev_audio1")
 
+    def _generate_input(self, unpackedData, convertSize, srcId):
+        # 今回変換するデータをテンソルとして整形する
+        audio = torch.FloatTensor(unpackedData.astype(np.float32)) # float32でtensorfを作成
+        audio_norm = audio / self.hps.data.max_wav_value # normalize
+        audio_norm = audio_norm.unsqueeze(0) # unsqueeze
+        self.audio_buffer = torch.cat([self.audio_buffer, audio_norm], axis=1) # 過去のデータに連結
+        audio_norm = self.audio_buffer[:, -convertSize:] # 変換対象の部分だけ抽出
+        self.audio_buffer = audio_norm
+
+        spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
+                                    self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
+                                    center=False)
+        spec = torch.squeeze(spec, 0)
+        sid = torch.LongTensor([int(srcId)])
+
+        data = (self.text_norm, spec, audio_norm, sid)
+        data = TextAudioSpeakerCollate()([data])
+        return data
+
+
+    def on_request(self, gpu, srcId, dstId, timestamp, convertChunkNum, crossFadeLowerValue, crossFadeOffsetRate, crossFadeEndRate, unpackedData):
+        convertSize = convertChunkNum * 128 # 128sample/1chunk
+        if unpackedData.shape[0] * 2 > convertSize:
+            convertSize = unpackedData.shape[0] * 2
+
+        print("convert Size", convertChunkNum, convertSize)
+
+        self._generate_strength(crossFadeOffsetRate, crossFadeEndRate, unpackedData)
+        data = self. _generate_input(unpackedData, convertSize, srcId)
 
         try:
-            # 今回変換するデータをテンソルとして整形する
-            audio = torch.FloatTensor(unpackedData.astype(np.float32)) # float32でtensorfを作成
-            audio_norm = audio / self.hps.data.max_wav_value # normalize
-            audio_norm = audio_norm.unsqueeze(0) # unsqueeze
-            self.audio_buffer = torch.cat([self.audio_buffer, audio_norm], axis=1) # 過去のデータに連結
-            audio_norm = self.audio_buffer[:, -convertSize:] # 変換対象の部分だけ抽出
-            self.audio_buffer = audio_norm
-
-            spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
-                                     self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
-                                     center=False)
-            spec = torch.squeeze(spec, 0)
-            sid = torch.LongTensor([int(srcId)])
-
-            data = (self.text_norm, spec, audio_norm, sid)
-            data = TextAudioSpeakerCollate()([data])
-
             # if gpu < 0 or (self.gpu_num == 0 and not self.mps_enabled):
             if gpu == -2 and hasattr(self, 'onnx_session') == True:
                 x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]