mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-02 16:23:58 +03:00
WIP: alpha
This commit is contained in:
wataru
parent
7f5490e202
commit
8082783644
@ -53,22 +53,22 @@ export const useMicrophoneOptions = () => {
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.setServerUrl(serverSetting.mmvcServerUrl)
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}, [serverSetting.mmvcServerUrl])
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}, [clientState.clientInitialized, serverSetting.mmvcServerUrl])
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//// プロトコル変更
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.setProtocol(serverSetting.protocol)
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}, [serverSetting.protocol])
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}, [clientState.clientInitialized, serverSetting.protocol])
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//// フレームワーク変更
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.framework, serverSetting.framework)
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}, [serverSetting.framework])
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}, [clientState.clientInitialized, serverSetting.framework])
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//// OnnxExecutionProvider変更
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.onnxExecutionProvider, serverSetting.onnxExecutionProvider)
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}, [serverSetting.onnxExecutionProvider])
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}, [clientState.clientInitialized, serverSetting.onnxExecutionProvider])
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// 102 DeviceSetting
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//// 入力情報の設定
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@ -82,58 +82,39 @@ export const useMicrophoneOptions = () => {
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.srcId, speakerSetting.srcId)
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}, [speakerSetting.srcId])
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}, [clientState.clientInitialized, speakerSetting.srcId])
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.dstId, speakerSetting.dstId)
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}, [speakerSetting.dstId])
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}, [clientState.clientInitialized, speakerSetting.dstId])
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// 104 ConvertSetting
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.setInputChunkNum(convertSetting.inputChunkNum)
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}, [convertSetting.inputChunkNum])
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}, [clientState.clientInitialized, convertSetting.inputChunkNum])
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.convertChunkNum, convertSetting.convertChunkNum)
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}, [convertSetting.convertChunkNum])
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}, [clientState.clientInitialized, convertSetting.convertChunkNum])
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.gpu, convertSetting.gpu)
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}, [convertSetting.gpu])
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}, [clientState.clientInitialized, convertSetting.gpu])
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.crossFadeOffsetRate, convertSetting.crossFadeOffsetRate)
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}, [convertSetting.crossFadeOffsetRate])
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}, [clientState.clientInitialized, convertSetting.crossFadeOffsetRate])
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.updateSettings(ServerSettingKey.crossFadeEndRate, convertSetting.crossFadeEndRate)
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}, [convertSetting.crossFadeEndRate])
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}, [clientState.clientInitialized, convertSetting.crossFadeEndRate])
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// 105 AdvancedSetting
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useEffect(() => {
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if (!clientState.clientInitialized) return
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clientState.setVoiceChangerMode(advancedSetting.voiceChangerMode)
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}, [advancedSetting.voiceChangerMode])
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// // const [options, setOptions] = useState<MicrophoneOptionsState>(InitMicrophoneOptionsState)
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// const [params, setParams] = useState<VoiceChangerRequestParamas>(DefaultVoiceChangerRequestParamas)
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// const [options, setOptions] = useState<VoiceChangerOptions>(DefaultVoiceChangerOptions)
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// const [isStarted, setIsStarted] = useState<boolean>(false)
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// useEffect(() => {
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// const storeOptions = async () => {
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// if (CHROME_EXTENSION) {
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// // @ts-ignore
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// await chrome.storage.local.set({ microphoneOptions: options })
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// }
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// }
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// storeOptions()
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// }, [options]) // loadより前に持ってくるとstorage内が初期化されるのでだめかも。（要検証）
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}, [clientState.clientInitialized, advancedSetting.voiceChangerMode])
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const voiceChangerSetting = useMemo(() => {
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@ -86,7 +86,6 @@ export const useSpeakerSetting = () => {
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<div className="body-button" onClick={onSetSpeakerMappingClicked}>set</div>
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</div>
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</div>
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)
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}, [speakers, editSpeakerTargetId, editSpeakerTargetName])
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@ -38,7 +38,7 @@ export const useServerControl = (props: UseServerControlProps) => {
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</div>
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)
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}, [isStarted])
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}, [isStarted, props.convertStart, props.convertStop])
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const performanceRow = useMemo(() => {
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return (
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@ -94,7 +94,6 @@ export const useClient = (props: UseClientProps): ClientState => {
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return
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}
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voiceChangerClientRef.current.setProtocol(protocol)
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voiceChangerClientRef.current.stop()
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}
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}, [])
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@ -105,7 +104,6 @@ export const useClient = (props: UseClientProps): ClientState => {
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return
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}
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voiceChangerClientRef.current.setInputChunkNum(num)
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voiceChangerClientRef.current.stop()
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}
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}, [])
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@ -33,6 +33,8 @@ export class VoiceChnagerClient {
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private promiseForInitialize: Promise<void>
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private _isVoiceChanging = false
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private sslCertified: string[] = []
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private callbacks: Callbacks = {
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onVoiceReceived: (voiceChangerMode: VoiceChangerMode, data: ArrayBuffer): void => {
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// console.log(voiceChangerMode, data)
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@ -176,11 +178,12 @@ export class VoiceChnagerClient {
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const pageUrl = `${location.protocol}//${location.host}`
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console.log("SERVER CHECK", url, pageUrl)
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if (url != pageUrl && location.protocol == "https:") {
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if (url != pageUrl && location.protocol == "https:" && this.sslCertified.includes(url) == false) {
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if (openTab) {
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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.)");
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if (value) {
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window.open(url, '_blank')
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this.sslCertified.push(url)
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} else {
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alert("Your voice conversion may fail...")
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}
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@ -1 +1,6 @@
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frontend_path = "../client/demo/dist"
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ERROR_NO_ONNX_SESSION = "ERROR_NO_ONNX_SESSION"
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@ -20,24 +20,14 @@ class MMVC_Namespace(socketio.AsyncNamespace):
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pass
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async def on_request_message(self, sid, msg):
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# print("on_request_message", torch.cuda.memory_allocated())
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gpu = int(msg[0])
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srcId = int(msg[1])
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dstId = int(msg[2])
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timestamp = int(msg[3])
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convertChunkNum = int(msg[4])
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crossFadeLowerValue = float(msg[5])
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crossFadeOffsetRate = float(msg[6])
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crossFadeEndRate = float(msg[7])
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data = msg[8]
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timestamp = int(msg[0])
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data = msg[1]
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unpackedData = np.array(struct.unpack('<%sh' % (len(data) // struct.calcsize('<h')), data))
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audio1 = self.voiceChangerManager.changeVoice(
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gpu, srcId, dstId, timestamp, convertChunkNum, crossFadeLowerValue, crossFadeOffsetRate, crossFadeEndRate, unpackedData)
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audio1 = self.voiceChangerManager.changeVoice(unpackedData)
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# print("sio result:", len(audio1), audio1.shape)
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# bin = struct.pack('<%sh' % len(audio1), *audio1)
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# await self.emit('response', [timestamp, bin])
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bin = struct.pack('<%sh' % len(audio1), *audio1)
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await self.emit('response', [timestamp, bin])
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def on_disconnect(self, sid):
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# print('[{}] disconnect'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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@ -1,3 +1,4 @@
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from const import ERROR_NO_ONNX_SESSION
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import torch
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import math, os, traceback
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from scipy.io.wavfile import write, read
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@ -24,13 +25,13 @@ class VocieChangerSettings():
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crossFadeOffsetRate:float = 0.1
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crossFadeEndRate:float = 0.9
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convertChunkNum:int = 32
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framework:str = "PyTorch"
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framework:str = "PyTorch" # PyTorch or ONNX
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pyTorch_model_file:str = ""
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onnx_model_file:str = ""
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config_file:str = ""
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# ↓mutableな物だけ列挙
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intData = ["srcId", "dstId", "convertChunkNum"]
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floatData = ["gpu", "crossFadeOffsetRate", "crossFadeEndRate",]
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intData = ["gpu","srcId", "dstId", "convertChunkNum"]
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floatData = [ "crossFadeOffsetRate", "crossFadeEndRate",]
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strData = ["framework"]
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class VoiceChanger():
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@ -98,10 +99,20 @@ class VoiceChanger():
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def update_setteings(self, key:str, val:any):
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if key == "onnxExecutionProvider":
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if val == "CUDAExecutionProvider":
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provider_options=[{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=[val], provider_options=provider_options)
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else:
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self.onnx_session.set_providers(providers=[val])
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return self.get_info()
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elif key in self.settings.intData:
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setattr(self.settings, key, int(val))
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if key == "gpu" and val >= 0 and val < self.gpu_num and hasattr(self, "onnx_session"):
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providers = self.onnx_session.get_providers()
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print("Providers::::", providers)
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if "CUDAExecutionProvider" in providers:
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provider_options=[{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options)
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return self.get_info()
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elif key in self.settings.floatData:
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setattr(self.settings, key, float(val))
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@ -114,21 +125,6 @@ class VoiceChanger():
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return self.get_info()
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# def set_gpu(self, gpu:int):
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# self.settings.gpu = gpu
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# return {"gpu":self.settings.gpu}
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# def set_crossfade_setting(self, crossFadeOffsetRate:float, crossFadeEndRate:float):
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# self.settings.crossFadeOffsetRate = crossFadeOffsetRate
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# self.settings.crossFadeEndRate = crossFadeEndRate
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# self.unpackedData_length = 0 # 次のVC時にStrengthを再計算させるため。
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# def set_conversion_setting(self, srcId:int, dstId:int):
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# self.settings.srcId = srcId
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# self.settings.dstId = dstId
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# def set_convert_chunk_num(self, convertChunkNum):
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# self.settings.convertChunkNum = convertChunkNum
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def _generate_strength(self, unpackedData):
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@ -179,6 +175,91 @@ class VoiceChanger():
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return data
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def _onnx_inference(self, data, inputSize):
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if hasattr(self, 'onnx_session'):
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]
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sid_tgt1 = torch.LongTensor([self.settings.dstId])
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# if spec.size()[2] >= 8:
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audio1 = self.onnx_session.run(
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["audio"],
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{
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"specs": spec.numpy(),
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"lengths": spec_lengths.numpy(),
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"sid_src": sid_src.numpy(),
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"sid_tgt": sid_tgt1.numpy()
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})[0][0,0] * self.hps.data.max_wav_value
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if hasattr(self, 'np_prev_audio1') == True:
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prev = self.np_prev_audio1[-1*inputSize:]
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cur = audio1[-2*inputSize:-1*inputSize]
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# print(prev.shape, self.np_prev_strength.shape, cur.shape, self.np_cur_strength.shape)
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powered_prev = prev * self.np_prev_strength
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powered_cur = cur * self.np_cur_strength
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result = powered_prev + powered_cur
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#result = prev * self.np_prev_strength + cur * self.np_cur_strength
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else:
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cur = audio1[-2*inputSize:-1*inputSize]
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result = cur
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self.np_prev_audio1 = audio1
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return result
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else:
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raise ValueError(ERROR_NO_ONNX_SESSION, "No ONNX Session.")
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def _pyTorch_inference(self, data, inputSize):
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if self.settings.gpu < 0 or self.gpu_num == 0:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cpu() for x in data]
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sid_tgt1 = torch.LongTensor([self.settings.dstId]).cpu()
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audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0, 0].data * self.hps.data.max_wav_value)
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if self.prev_strength.device != torch.device('cpu'):
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print(f"prev_strength move from {self.prev_strength.device} to cpu")
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self.prev_strength = self.prev_strength.cpu()
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if self.cur_strength.device != torch.device('cpu'):
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print(f"cur_strength move from {self.cur_strength.device} to cpu")
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self.cur_strength = self.cur_strength.cpu()
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'):
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prev = self.prev_audio1[-1*inputSize:]
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cur = audio1[-2*inputSize:-1*inputSize]
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result = prev * self.prev_strength + cur * self.cur_strength
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else:
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cur = audio1[-2*inputSize:-1*inputSize]
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result = cur
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self.prev_audio1 = audio1
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result = result.cpu().float().numpy()
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else:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda(self.settings.gpu) for x in data]
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sid_tgt1 = torch.LongTensor([self.settings.dstId]).cuda(self.settings.gpu)
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audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0, 0].data * self.hps.data.max_wav_value
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if self.prev_strength.device != torch.device('cuda', self.settings.gpu):
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print(f"prev_strength move from {self.prev_strength.device} to gpu{self.settings.gpu}")
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self.prev_strength = self.prev_strength.cuda(self.settings.gpu)
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if self.cur_strength.device != torch.device('cuda', self.settings.gpu):
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print(f"cur_strength move from {self.cur_strength.device} to gpu{self.settings.gpu}")
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self.cur_strength = self.cur_strength.cuda(self.settings.gpu)
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', self.settings.gpu):
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prev = self.prev_audio1[-1*inputSize:]
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cur = audio1[-2*inputSize:-1*inputSize]
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result = prev * self.prev_strength + cur * self.cur_strength
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# print("merging...", prev.shape, cur.shape)
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else:
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cur = audio1[-2*inputSize:-1*inputSize]
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result = cur
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# print("no merging...", cur.shape)
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self.prev_audio1 = audio1
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#print(result)
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result = result.cpu().float().numpy()
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return result
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def on_request(self, unpackedData:any):
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convertSize = self.settings.convertChunkNum * 128 # 128sample/1chunk
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if unpackedData.shape[0] * 2 > convertSize:
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@ -189,96 +270,21 @@ class VoiceChanger():
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self._generate_strength(unpackedData)
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data = self._generate_input(unpackedData, convertSize)
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# try:
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# # if gpu < 0 or (self.gpu_num == 0 and not self.mps_enabled):
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# if self.gpu == -2 and hasattr(self, 'onnx_session') == True:
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# x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]
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# sid_tgt1 = torch.LongTensor([self.dstId])
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# # if spec.size()[2] >= 8:
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# audio1 = self.onnx_session.run(
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# ["audio"],
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# {
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# "specs": spec.numpy(),
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# "lengths": spec_lengths.numpy(),
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# "sid_src": sid_src.numpy(),
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# "sid_tgt": sid_tgt1.numpy()
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# })[0][0,0] * self.hps.data.max_wav_value
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# if hasattr(self, 'np_prev_audio1') == True:
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# prev = self.np_prev_audio1[-1*unpackedData.shape[0]:]
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# cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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# # print(prev.shape, self.np_prev_strength.shape, cur.shape, self.np_cur_strength.shape)
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# powered_prev = prev * self.np_prev_strength
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# powered_cur = cur * self.np_cur_strength
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# result = powered_prev + powered_cur
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# #result = prev * self.np_prev_strength + cur * self.np_cur_strength
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# else:
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# cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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# result = cur
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# self.np_prev_audio1 = audio1
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# elif self.gpu < 0 or self.gpu_num == 0:
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# with torch.no_grad():
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# x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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# x.cpu() for x in data]
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# sid_tgt1 = torch.LongTensor([self.dstId]).cpu()
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# audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0, 0].data * self.hps.data.max_wav_value)
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# if self.prev_strength.device != torch.device('cpu'):
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# print(f"prev_strength move from {self.prev_strength.device} to cpu")
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# self.prev_strength = self.prev_strength.cpu()
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# if self.cur_strength.device != torch.device('cpu'):
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# print(f"cur_strength move from {self.cur_strength.device} to cpu")
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# self.cur_strength = self.cur_strength.cpu()
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# if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'):
|
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# prev = self.prev_audio1[-1*unpackedData.shape[0]:]
|
||||
# cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
|
||||
# result = prev * self.prev_strength + cur * self.cur_strength
|
||||
# else:
|
||||
# cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
|
||||
# result = cur
|
||||
|
||||
# self.prev_audio1 = audio1
|
||||
# result = result.cpu().float().numpy()
|
||||
|
||||
# else:
|
||||
# with torch.no_grad():
|
||||
# x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda(self.gpu) for x in data]
|
||||
# sid_tgt1 = torch.LongTensor([self.dstId]).cuda(self.gpu)
|
||||
# audio1 = self.net_g.cuda(self.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0, 0].data * self.hps.data.max_wav_value
|
||||
|
||||
# if self.prev_strength.device != torch.device('cuda', self.gpu):
|
||||
# print(f"prev_strength move from {self.prev_strength.device} to gpu{self.gpu}")
|
||||
# self.prev_strength = self.prev_strength.cuda(self.gpu)
|
||||
# if self.cur_strength.device != torch.device('cuda', self.gpu):
|
||||
# print(f"cur_strength move from {self.cur_strength.device} to gpu{self.gpu}")
|
||||
# self.cur_strength = self.cur_strength.cuda(self.gpu)
|
||||
try:
|
||||
if self.settings.framework == "ONNX":
|
||||
result = self._onnx_inference(data, unpackedData.shape[0])
|
||||
else:
|
||||
result = self._pyTorch_inference(data, unpackedData.shape[0])
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print("VC PROCESSING!!!! EXCEPTION!!!", e)
|
||||
print(traceback.format_exc())
|
||||
del self.np_prev_audio1
|
||||
del self.prev_audio1
|
||||
|
||||
# if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', self.gpu):
|
||||
# prev = self.prev_audio1[-1*unpackedData.shape[0]:]
|
||||
# cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
|
||||
# result = prev * self.prev_strength + cur * self.cur_strength
|
||||
# # print("merging...", prev.shape, cur.shape)
|
||||
# else:
|
||||
# cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
|
||||
# result = cur
|
||||
# # print("no merging...", cur.shape)
|
||||
# self.prev_audio1 = audio1
|
||||
|
||||
# #print(result)
|
||||
# result = result.cpu().float().numpy()
|
||||
|
||||
|
||||
# except Exception as e:
|
||||
# print("VC PROCESSING!!!! EXCEPTION!!!", e)
|
||||
# print(traceback.format_exc())
|
||||
# del self.np_prev_audio1
|
||||
# del self.prev_audio1
|
||||
|
||||
# result = result.astype(np.int16)
|
||||
# # print("on_request result size:",result.shape)
|
||||
# return result
|
||||
return
|
||||
result = result.astype(np.int16)
|
||||
# print("on_request result size:",result.shape)
|
||||
return result
|
||||
|
||||
|
||||
@ -27,23 +27,9 @@ class VoiceChangerManager():
|
||||
else:
|
||||
return {"no info":"no info"}
|
||||
|
||||
# def set_onnx_provider(self, provider:str):
|
||||
# if hasattr(self, 'voiceChanger'):
|
||||
# return self.voiceChanger.set_onnx_provider(provider)
|
||||
# else:
|
||||
# return {"error":"no voice changer"}
|
||||
|
||||
|
||||
def changeVoice(self, gpu:int, srcId:int, dstId:int, timestamp:int, convertChunkNum:int, crossFadeLowerValue:float, crossFadeOffsetRate:float, crossFadeEndRate:float, unpackedData:any):
|
||||
def changeVoice(self, unpackedData:any):
|
||||
if hasattr(self, 'voiceChanger') == True:
|
||||
return self.voiceChanger.on_request(unpackedData)
|
||||
else:
|
||||
print("Voice Change is not loaded. Did you load a correct model?")
|
||||
return np.zeros(1).astype(np.int16)
|
||||
|
||||
def changeVoice_old(self, gpu, srcId, dstId, timestamp, prefixChunkSize, unpackedData):
|
||||
if hasattr(self, 'voiceChanger') == True:
|
||||
return self.voiceChanger.on_request(gpu, srcId, dstId, timestamp, prefixChunkSize, unpackedData)
|
||||
else:
|
||||
print("Voice Change is not loaded. Did you load a correct model?")
|
||||
return np.zeros(1).astype(np.int16)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user