mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-03-13 19:34:02 +03:00
update information when upload model
This commit is contained in:
wataru
parent
eb846bedcc
commit
9a473a07be
2
client/demo/dist/index.js
vendored
2
client/demo/dist/index.js
vendored
File diff suppressed because one or more lines are too long
7014
client/demo/package-lock.json
generated
7014
client/demo/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@ -38,7 +38,7 @@
|
||||
"postcss-loader": "^7.0.2",
|
||||
"postcss-nested": "^6.0.0",
|
||||
"prettier": "^2.8.3",
|
||||
"rimraf": "^4.1.1",
|
||||
"rimraf": "^4.1.2",
|
||||
"style-loader": "^3.3.1",
|
||||
"ts-loader": "^9.4.2",
|
||||
"tsconfig-paths": "^4.1.2",
|
||||
@ -48,7 +48,7 @@
|
||||
"webpack-dev-server": "^4.11.1"
|
||||
},
|
||||
"dependencies": {
|
||||
"@dannadori/voice-changer-client-js": "^1.0.20",
|
||||
"@dannadori/voice-changer-client-js": "^1.0.28",
|
||||
"react": "^18.2.0",
|
||||
"react-dom": "^18.2.0"
|
||||
}
|
||||
|
||||
2718
client/lib/package-lock.json
generated
2718
client/lib/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "@dannadori/voice-changer-client-js",
|
||||
"version": "1.0.20",
|
||||
"version": "1.0.28",
|
||||
"description": "",
|
||||
"main": "dist/index.js",
|
||||
"directories": {
|
||||
@ -26,17 +26,17 @@
|
||||
"devDependencies": {
|
||||
"@types/audioworklet": "^0.0.36",
|
||||
"@types/node": "^18.11.18",
|
||||
"@types/react": "18.0.26",
|
||||
"@types/react": "18.0.27",
|
||||
"@types/react-dom": "18.0.10",
|
||||
"eslint": "^8.32.0",
|
||||
"eslint-config-prettier": "^8.6.0",
|
||||
"eslint-plugin-prettier": "^4.2.1",
|
||||
"eslint-plugin-react": "^7.32.0",
|
||||
"eslint-plugin-react": "^7.32.1",
|
||||
"eslint-webpack-plugin": "^3.2.0",
|
||||
"npm-run-all": "^4.1.5",
|
||||
"prettier": "^2.8.3",
|
||||
"raw-loader": "^4.0.2",
|
||||
"rimraf": "^4.0.7",
|
||||
"rimraf": "^4.1.2",
|
||||
"ts-loader": "^9.4.2",
|
||||
"typescript": "^4.9.4",
|
||||
"webpack": "^5.75.0",
|
||||
|
||||
@ -135,7 +135,7 @@ export const DefaultVoiceChangerServerSetting: VoiceChangerServerSetting = {
|
||||
crossFadeOffsetRate: 0.1,
|
||||
crossFadeEndRate: 0.9,
|
||||
crossFadeOverlapRate: 0.5,
|
||||
framework: "PyTorch",
|
||||
framework: "ONNX",
|
||||
onnxExecutionProvider: "CPUExecutionProvider"
|
||||
|
||||
}
|
||||
|
||||
@ -179,10 +179,10 @@ export const useServerSetting = (props: UseServerSettingProps): ServerSettingSta
|
||||
console.log(progress, end)
|
||||
})
|
||||
|
||||
const serverInfo = await props.voiceChangerClient.loadModel(fileUploadSetting.configFile, fileUploadSetting.pyTorchModel, fileUploadSetting.onnxModel)
|
||||
console.log(serverInfo)
|
||||
await props.voiceChangerClient.loadModel(fileUploadSetting.configFile, fileUploadSetting.pyTorchModel, fileUploadSetting.onnxModel)
|
||||
setUploadProgress(0)
|
||||
setIsUploading(false)
|
||||
reloadServerInfo()
|
||||
}
|
||||
}, [fileUploadSetting, props.voiceChangerClient])
|
||||
|
||||
@ -203,7 +203,6 @@ export const useServerSetting = (props: UseServerSettingProps): ServerSettingSta
|
||||
framework: res.framework,
|
||||
onnxExecutionProvider: (!!res.onnxExecutionProvider && res.onnxExecutionProvider.length > 0) ? res.onnxExecutionProvider[0] as OnnxExecutionProvider : DefaultVoiceChangerServerSetting.onnxExecutionProvider
|
||||
})
|
||||
|
||||
}
|
||||
}, [props.voiceChangerClient])
|
||||
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
from const import ERROR_NO_ONNX_SESSION
|
||||
import torch
|
||||
import os, traceback
|
||||
import os
|
||||
import traceback
|
||||
import numpy as np
|
||||
from dataclasses import dataclass, asdict
|
||||
|
||||
@ -11,39 +12,41 @@ from models import SynthesizerTrn
|
||||
|
||||
from voice_changer.TrainerFunctions import TextAudioSpeakerCollate, spectrogram_torch, load_checkpoint, get_hparams_from_file
|
||||
|
||||
providers = ['OpenVINOExecutionProvider',"CUDAExecutionProvider","DmlExecutionProvider","CPUExecutionProvider"]
|
||||
providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
|
||||
|
||||
|
||||
@dataclass
|
||||
class VocieChangerSettings():
|
||||
gpu:int = 0
|
||||
srcId:int = 107
|
||||
dstId:int = 100
|
||||
crossFadeOffsetRate:float = 0.1
|
||||
crossFadeEndRate:float = 0.9
|
||||
crossFadeOverlapRate:float = 0.9
|
||||
convertChunkNum:int = 32
|
||||
minConvertSize:int = 0
|
||||
framework:str = "PyTorch" # PyTorch or ONNX
|
||||
pyTorchModelFile:str = ""
|
||||
onnxModelFile:str = ""
|
||||
configFile:str = ""
|
||||
gpu: int = 0
|
||||
srcId: int = 107
|
||||
dstId: int = 100
|
||||
crossFadeOffsetRate: float = 0.1
|
||||
crossFadeEndRate: float = 0.9
|
||||
crossFadeOverlapRate: float = 0.9
|
||||
convertChunkNum: int = 32
|
||||
minConvertSize: int = 0
|
||||
framework: str = "ONNX" # PyTorch or ONNX
|
||||
pyTorchModelFile: str = ""
|
||||
onnxModelFile: str = ""
|
||||
configFile: str = ""
|
||||
# ↓mutableな物だけ列挙
|
||||
intData = ["gpu","srcId", "dstId", "convertChunkNum", "minConvertSize"]
|
||||
floatData = [ "crossFadeOffsetRate", "crossFadeEndRate", "crossFadeOverlapRate"]
|
||||
intData = ["gpu", "srcId", "dstId", "convertChunkNum", "minConvertSize"]
|
||||
floatData = ["crossFadeOffsetRate", "crossFadeEndRate", "crossFadeOverlapRate"]
|
||||
strData = ["framework"]
|
||||
|
||||
|
||||
class VoiceChanger():
|
||||
|
||||
def __init__(self, config:str):
|
||||
def __init__(self, config: str):
|
||||
# 初期化
|
||||
self.settings = VocieChangerSettings(configFile=config)
|
||||
self.unpackedData_length=0
|
||||
self.unpackedData_length = 0
|
||||
self.net_g = None
|
||||
self.onnx_session = None
|
||||
self.currentCrossFadeOffsetRate=0
|
||||
self.currentCrossFadeEndRate=0
|
||||
self.currentCrossFadeOverlapRate=0
|
||||
|
||||
self.currentCrossFadeOffsetRate = 0
|
||||
self.currentCrossFadeEndRate = 0
|
||||
self.currentCrossFadeOverlapRate = 0
|
||||
|
||||
# 共通で使用する情報を収集
|
||||
# self.hps = utils.get_hparams_from_file(config)
|
||||
self.hps = get_hparams_from_file(config)
|
||||
@ -55,20 +58,20 @@ class VoiceChanger():
|
||||
# print("text_norm2: ",text_norm)
|
||||
# self.text_norm = torch.LongTensor(text_norm)
|
||||
|
||||
self.text_norm = torch.LongTensor([0, 6, 0])
|
||||
self.text_norm = torch.LongTensor([0, 6, 0])
|
||||
self.audio_buffer = torch.zeros(1, 0)
|
||||
self.prev_audio = np.zeros(1)
|
||||
self.mps_enabled = getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available()
|
||||
|
||||
print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})")
|
||||
|
||||
def loadModel(self, config:str, pyTorch_model_file:str=None, onnx_model_file:str=None):
|
||||
def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None):
|
||||
self.settings.configFile = config
|
||||
if pyTorch_model_file != None:
|
||||
self.settings.pyTorchModelFile = pyTorch_model_file
|
||||
if onnx_model_file:
|
||||
self.settings.onnxModelFile = onnx_model_file
|
||||
|
||||
|
||||
# PyTorchモデル生成
|
||||
if pyTorch_model_file != None:
|
||||
self.net_g = SynthesizerTrn(
|
||||
@ -93,7 +96,7 @@ class VoiceChanger():
|
||||
|
||||
def destroy(self):
|
||||
del self.net_g
|
||||
del self.onnx_session
|
||||
del self.onnx_session
|
||||
|
||||
def get_info(self):
|
||||
data = asdict(self.settings)
|
||||
@ -101,19 +104,19 @@ class VoiceChanger():
|
||||
data["onnxExecutionProvider"] = self.onnx_session.get_providers() if self.onnx_session != None else []
|
||||
files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
|
||||
for f in files:
|
||||
if data[f]!=None and os.path.exists(data[f]):
|
||||
if data[f] != None and os.path.exists(data[f]):
|
||||
data[f] = os.path.basename(data[f])
|
||||
else:
|
||||
data[f] = ""
|
||||
|
||||
return data
|
||||
|
||||
def update_setteings(self, key:str, val:any):
|
||||
def update_setteings(self, key: str, val: any):
|
||||
if key == "onnxExecutionProvider" and self.onnx_session != None:
|
||||
if val == "CUDAExecutionProvider":
|
||||
if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num:
|
||||
self.settings.gpu = 0
|
||||
provider_options=[{'device_id': self.settings.gpu}]
|
||||
provider_options = [{'device_id': self.settings.gpu}]
|
||||
self.onnx_session.set_providers(providers=[val], provider_options=provider_options)
|
||||
else:
|
||||
self.onnx_session.set_providers(providers=[val])
|
||||
@ -123,7 +126,7 @@ class VoiceChanger():
|
||||
providers = self.onnx_session.get_providers()
|
||||
print("Providers:", providers)
|
||||
if "CUDAExecutionProvider" in providers:
|
||||
provider_options=[{'device_id': self.settings.gpu}]
|
||||
provider_options = [{'device_id': self.settings.gpu}]
|
||||
self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options)
|
||||
if key == "crossFadeOffsetRate" or key == "crossFadeEndRate":
|
||||
self.unpackedData_length = 0
|
||||
@ -136,7 +139,6 @@ class VoiceChanger():
|
||||
|
||||
return self.get_info()
|
||||
|
||||
|
||||
def _generate_strength(self, unpackedData):
|
||||
|
||||
if self.unpackedData_length != unpackedData.shape[0] or self.currentCrossFadeOffsetRate != self.settings.crossFadeOffsetRate or self.currentCrossFadeEndRate != self.settings.crossFadeEndRate or self.currentCrossFadeOverlapRate != self.settings.crossFadeOverlapRate:
|
||||
@ -148,12 +150,12 @@ class VoiceChanger():
|
||||
overlapSize = int(unpackedData.shape[0] * self.settings.crossFadeOverlapRate)
|
||||
|
||||
cf_offset = int(overlapSize * self.settings.crossFadeOffsetRate)
|
||||
cf_end = int(overlapSize * self.settings.crossFadeEndRate)
|
||||
cf_end = int(overlapSize * self.settings.crossFadeEndRate)
|
||||
cf_range = cf_end - cf_offset
|
||||
percent = np.arange(cf_range) / cf_range
|
||||
|
||||
np_prev_strength = np.cos(percent * 0.5 * np.pi) ** 2
|
||||
np_cur_strength = np.cos((1-percent) * 0.5 * np.pi) ** 2
|
||||
np_prev_strength = np.cos(percent * 0.5 * np.pi) ** 2
|
||||
np_cur_strength = np.cos((1 - percent) * 0.5 * np.pi) ** 2
|
||||
|
||||
self.np_prev_strength = np.concatenate([np.ones(cf_offset), np_prev_strength, np.zeros(overlapSize - cf_offset - len(np_prev_strength))])
|
||||
self.np_cur_strength = np.concatenate([np.zeros(cf_offset), np_cur_strength, np.ones(overlapSize - cf_offset - len(np_cur_strength))])
|
||||
@ -167,23 +169,23 @@ class VoiceChanger():
|
||||
# print(f"target_len:{unpackedData.shape[0]}, prev_len:{len(self.prev_strength)} cur_len:{len(self.cur_strength)}")
|
||||
# print("Prev", self.prev_strength)
|
||||
# print("Cur", self.cur_strength)
|
||||
|
||||
|
||||
# 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, 'prev_audio1') == True:
|
||||
delattr(self,"prev_audio1")
|
||||
delattr(self, "prev_audio1")
|
||||
|
||||
def _generate_input(self, unpackedData:any, convertSize:int):
|
||||
def _generate_input(self, unpackedData: any, convertSize: int):
|
||||
# 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
|
||||
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)
|
||||
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(self.settings.srcId)])
|
||||
|
||||
@ -191,7 +193,6 @@ class VoiceChanger():
|
||||
data = TextAudioSpeakerCollate()([data])
|
||||
return data
|
||||
|
||||
|
||||
def _onnx_inference(self, data, inputSize):
|
||||
if hasattr(self, "onnx_session") == False or self.onnx_session == None:
|
||||
print("[Voice Changer] No ONNX session.")
|
||||
@ -207,26 +208,26 @@ class VoiceChanger():
|
||||
"lengths": spec_lengths.numpy(),
|
||||
"sid_src": sid_src.numpy(),
|
||||
"sid_tgt": sid_tgt1.numpy()
|
||||
})[0][0,0] * self.hps.data.max_wav_value
|
||||
})[0][0, 0] * self.hps.data.max_wav_value
|
||||
if hasattr(self, 'np_prev_audio1') == True:
|
||||
overlapSize = int(inputSize * self.settings.crossFadeOverlapRate)
|
||||
prev_overlap = self.np_prev_audio1[-1*overlapSize:]
|
||||
cur_overlap = audio1[-1*(inputSize + overlapSize) :-1*inputSize]
|
||||
prev_overlap = self.np_prev_audio1[-1 * overlapSize:]
|
||||
cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
|
||||
# print(prev_overlap.shape, self.np_prev_strength.shape, cur_overlap.shape, self.np_cur_strength.shape)
|
||||
# print(">>>>>>>>>>>", -1*(inputSize + overlapSize) , -1*inputSize)
|
||||
powered_prev = prev_overlap * self.np_prev_strength
|
||||
powered_cur = cur_overlap * self.np_cur_strength
|
||||
powered_result = powered_prev + powered_cur
|
||||
|
||||
cur = audio1[-1*inputSize:-1*overlapSize]
|
||||
result = np.concatenate([powered_result, cur],axis=0)
|
||||
cur = audio1[-1 * inputSize:-1 * overlapSize]
|
||||
result = np.concatenate([powered_result, cur], axis=0)
|
||||
else:
|
||||
result = np.zeros(1).astype(np.int16)
|
||||
self.np_prev_audio1 = audio1
|
||||
return result
|
||||
|
||||
def _pyTorch_inference(self, data, inputSize):
|
||||
if hasattr(self, "net_g") == False or self.net_g ==None:
|
||||
if hasattr(self, "net_g") == False or self.net_g == None:
|
||||
print("[Voice Changer] No pyTorch session.")
|
||||
return np.zeros(1).astype(np.int16)
|
||||
|
||||
@ -234,7 +235,8 @@ class VoiceChanger():
|
||||
with torch.no_grad():
|
||||
x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cpu() for x in data]
|
||||
sid_tgt1 = torch.LongTensor([self.settings.dstId]).cpu()
|
||||
audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value)
|
||||
audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src,
|
||||
sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value)
|
||||
|
||||
if self.prev_strength.device != torch.device('cpu'):
|
||||
print(f"prev_strength move from {self.prev_strength.device} to cpu")
|
||||
@ -243,19 +245,19 @@ class VoiceChanger():
|
||||
print(f"cur_strength move from {self.cur_strength.device} to cpu")
|
||||
self.cur_strength = self.cur_strength.cpu()
|
||||
|
||||
if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'): # prev_audio1が所望のデバイスに無い場合は一回休み。
|
||||
if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'): # prev_audio1が所望のデバイスに無い場合は一回休み。
|
||||
overlapSize = int(inputSize * self.settings.crossFadeOverlapRate)
|
||||
prev_overlap = self.prev_audio1[-1*overlapSize:]
|
||||
cur_overlap = audio1[-1*(inputSize + overlapSize) :-1*inputSize]
|
||||
prev_overlap = self.prev_audio1[-1 * overlapSize:]
|
||||
cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
|
||||
powered_prev = prev_overlap * self.prev_strength
|
||||
powered_cur = cur_overlap * self.cur_strength
|
||||
powered_result = powered_prev + powered_cur
|
||||
|
||||
cur = audio1[-1*inputSize:-1*overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
|
||||
result = torch.cat([powered_result, cur],axis=0) # Crossfadeと今回のインプットの生部分を結合
|
||||
cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
|
||||
result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合
|
||||
|
||||
else:
|
||||
cur = audio1[-2*inputSize:-1*inputSize]
|
||||
cur = audio1[-2 * inputSize:-1 * inputSize]
|
||||
result = cur
|
||||
|
||||
self.prev_audio1 = audio1
|
||||
@ -265,7 +267,8 @@ class VoiceChanger():
|
||||
with torch.no_grad():
|
||||
x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda(self.settings.gpu) for x in data]
|
||||
sid_tgt1 = torch.LongTensor([self.settings.dstId]).cuda(self.settings.gpu)
|
||||
audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value
|
||||
audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src,
|
||||
sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value
|
||||
|
||||
if self.prev_strength.device != torch.device('cuda', self.settings.gpu):
|
||||
print(f"prev_strength move from {self.prev_strength.device} to gpu{self.settings.gpu}")
|
||||
@ -274,33 +277,30 @@ class VoiceChanger():
|
||||
print(f"cur_strength move from {self.cur_strength.device} to gpu{self.settings.gpu}")
|
||||
self.cur_strength = self.cur_strength.cuda(self.settings.gpu)
|
||||
|
||||
|
||||
|
||||
if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', self.settings.gpu):
|
||||
overlapSize = int(inputSize * self.settings.crossFadeOverlapRate)
|
||||
prev_overlap = self.prev_audio1[-1*overlapSize:]
|
||||
cur_overlap = audio1[-1*(inputSize + overlapSize) :-1*inputSize]
|
||||
prev_overlap = self.prev_audio1[-1 * overlapSize:]
|
||||
cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
|
||||
powered_prev = prev_overlap * self.prev_strength
|
||||
powered_cur = cur_overlap * self.cur_strength
|
||||
powered_result = powered_prev + powered_cur
|
||||
|
||||
cur = audio1[-1*inputSize:-1*overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
|
||||
result = torch.cat([powered_result, cur],axis=0) # Crossfadeと今回のインプットの生部分を結合
|
||||
cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
|
||||
result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合
|
||||
|
||||
else:
|
||||
cur = audio1[-2*inputSize:-1*inputSize]
|
||||
cur = audio1[-2 * inputSize:-1 * inputSize]
|
||||
result = cur
|
||||
self.prev_audio1 = audio1
|
||||
|
||||
result = result.cpu().float().numpy()
|
||||
return result
|
||||
|
||||
|
||||
def on_request(self, unpackedData:any):
|
||||
convertSize = self.settings.convertChunkNum * 128 # 128sample/1chunk
|
||||
def on_request(self, unpackedData: any):
|
||||
convertSize = self.settings.convertChunkNum * 128 # 128sample/1chunk
|
||||
|
||||
if unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate) + 1024 > convertSize:
|
||||
convertSize = int(unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate)) + 1024
|
||||
if unpackedData.shape[0] * (1 + self.settings.crossFadeOverlapRate) + 1024 > convertSize:
|
||||
convertSize = int(unpackedData.shape[0] * (1 + self.settings.crossFadeOverlapRate)) + 1024
|
||||
if convertSize < self.settings.minConvertSize:
|
||||
convertSize = self.settings.minConvertSize
|
||||
# print("convert Size", unpackedData.shape[0], unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate), convertSize, self.settings.minConvertSize)
|
||||
@ -308,16 +308,14 @@ class VoiceChanger():
|
||||
self._generate_strength(unpackedData)
|
||||
data = self._generate_input(unpackedData, convertSize)
|
||||
|
||||
|
||||
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("VC PROCESSING!!!! EXCEPTION!!!", e)
|
||||
print(traceback.format_exc())
|
||||
if hasattr(self, "np_prev_audio1"):
|
||||
del self.np_prev_audio1
|
||||
@ -328,4 +326,3 @@ class VoiceChanger():
|
||||
result = result.astype(np.int16)
|
||||
# print("on_request result size:",result.shape)
|
||||
return result
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user