WIP: refactoring

server/.vscode/settings.json

@@ -9,7 +9,7 @@
     "editor.formatOnSave": true // ファイル保存時に自動フォーマット
   },
   "flake8.args": [
-    "--ignore=E501"
+    "--ignore=E501,E402,W503"
     // "--max-line-length=150",
     // "--max-complexity=20"
   ]

server/Exceptions.py

@@ -1,12 +1,13 @@
-
 class NoModeLoadedException(Exception):
     def __init__(self, framework):
         self.framework = framework
 
     def __str__(self):
-        return repr(f"No model for {self.framework} loaded. Please confirm the model uploaded.")
+        return repr(
+            f"No model for {self.framework} loaded. Please confirm the model uploaded."
+        )
 
 
 class ONNXInputArgumentException(Exception):
     def __str__(self):
-        return repr(f"ONNX received invalid argument.")
+        return repr("ONNX received invalid argument.")

server/const.py

@@ -4,7 +4,15 @@ import tempfile
 from typing import Literal, TypeAlias
 
 
-ModelType: TypeAlias = Literal['MMVCv15', 'MMVCv13', 'so-vits-svc-40v2', 'so-vits-svc-40', 'so-vits-svc-40_c', 'DDSP-SVC', 'RVC']
+ModelType: TypeAlias = Literal[
+    "MMVCv15",
+    "MMVCv13",
+    "so-vits-svc-40v2",
+    "so-vits-svc-40",
+    "so-vits-svc-40_c",
+    "DDSP-SVC",
+    "RVC",
+]
 
 ERROR_NO_ONNX_SESSION = "ERROR_NO_ONNX_SESSION"
 
@@ -13,19 +21,45 @@ tmpdir = tempfile.TemporaryDirectory()
 # print("generate tmpdir:::",tmpdir)
 SSL_KEY_DIR = os.path.join(tmpdir.name, "keys") if hasattr(sys, "_MEIPASS") else "keys"
 MODEL_DIR = os.path.join(tmpdir.name, "logs") if hasattr(sys, "_MEIPASS") else "logs"
-UPLOAD_DIR = os.path.join(tmpdir.name, "upload_dir") if hasattr(sys, "_MEIPASS") else "upload_dir"
-NATIVE_CLIENT_FILE_WIN = os.path.join(sys._MEIPASS, "voice-changer-native-client.exe") if hasattr(sys, "_MEIPASS") else "voice-changer-native-client"
-NATIVE_CLIENT_FILE_MAC = os.path.join(sys._MEIPASS, "voice-changer-native-client.app", "Contents", "MacOS",
-                                      "voice-changer-native-client") if hasattr(sys, "_MEIPASS") else "voice-changer-native-client"
+UPLOAD_DIR = (
+    os.path.join(tmpdir.name, "upload_dir")
+    if hasattr(sys, "_MEIPASS")
+    else "upload_dir"
+)
+NATIVE_CLIENT_FILE_WIN = (
+    os.path.join(sys._MEIPASS, "voice-changer-native-client.exe")  # type: ignore
+    if hasattr(sys, "_MEIPASS")
+    else "voice-changer-native-client"
+)
+NATIVE_CLIENT_FILE_MAC = (
+    os.path.join(
+        sys._MEIPASS,  # type: ignore
+        "voice-changer-native-client.app",
+        "Contents",
+        "MacOS",
+        "voice-changer-native-client",
+    )
+    if hasattr(sys, "_MEIPASS")
+    else "voice-changer-native-client"
+)
 
-HUBERT_ONNX_MODEL_PATH = os.path.join(sys._MEIPASS, "model_hubert/hubert_simple.onnx") if hasattr(sys,
-                                                                                                  "_MEIPASS") else "model_hubert/hubert_simple.onnx"
+HUBERT_ONNX_MODEL_PATH = (
+    os.path.join(sys._MEIPASS, "model_hubert/hubert_simple.onnx")  # type: ignore
+    if hasattr(sys, "_MEIPASS")
+    else "model_hubert/hubert_simple.onnx"
+)
 
 
-TMP_DIR = os.path.join(tmpdir.name, "tmp_dir") if hasattr(sys, "_MEIPASS") else "tmp_dir"
+TMP_DIR = (
+    os.path.join(tmpdir.name, "tmp_dir") if hasattr(sys, "_MEIPASS") else "tmp_dir"
+)
 os.makedirs(TMP_DIR, exist_ok=True)
 
 
 def getFrontendPath():
-    frontend_path = os.path.join(sys._MEIPASS, "dist") if hasattr(sys, "_MEIPASS") else "../client/demo/dist"
+    frontend_path = (
+        os.path.join(sys._MEIPASS, "dist")
+        if hasattr(sys, "_MEIPASS")
+        else "../client/demo/dist"
+    )
     return frontend_path

server/restapi/MMVC_Rest_Fileuploader.py

@@ -10,6 +10,9 @@ from restapi.mods.FileUploader import upload_file, concat_file_chunks
 from voice_changer.VoiceChangerManager import VoiceChangerManager
 
 from const import MODEL_DIR, UPLOAD_DIR, ModelType
+from voice_changer.utils.LoadModelParams import FilePaths, LoadModelParams
+
+from dataclasses import fields
 
 os.makedirs(UPLOAD_DIR, exist_ok=True)
 os.makedirs(MODEL_DIR, exist_ok=True)
@@ -30,12 +33,6 @@ class MMVC_Rest_Fileuploader:
             "/update_settings", self.post_update_settings, methods=["POST"]
         )
         self.router.add_api_route("/load_model", self.post_load_model, methods=["POST"])
-        self.router.add_api_route(
-            "/load_model_for_train", self.post_load_model_for_train, methods=["POST"]
-        )
-        self.router.add_api_route(
-            "/extract_voices", self.post_extract_voices, methods=["POST"]
-        )
         self.router.add_api_route("/model_type", self.post_model_type, methods=["POST"])
         self.router.add_api_route("/model_type", self.get_model_type, methods=["GET"])
         self.router.add_api_route("/onnx", self.get_onnx, methods=["GET"])
@@ -80,74 +77,42 @@ class MMVC_Rest_Fileuploader:
         isHalf: bool = Form(...),
         params: str = Form(...),
     ):
-        props = {
-            "slot": slot,
-            "isHalf": isHalf,
-            "files": {
-                "configFilename": configFilename,
-                "pyTorchModelFilename": pyTorchModelFilename,
-                "onnxModelFilename": onnxModelFilename,
-                "clusterTorchModelFilename": clusterTorchModelFilename,
-                "featureFilename": featureFilename,
-                "indexFilename": indexFilename,
-            },
-            "params": params,
-        }
+        files = FilePaths(
+            configFilename=configFilename,
+            pyTorchModelFilename=pyTorchModelFilename,
+            onnxModelFilename=onnxModelFilename,
+            clusterTorchModelFilename=clusterTorchModelFilename,
+            featureFilename=featureFilename,
+            indexFilename=indexFilename,
+        )
+        props: LoadModelParams = LoadModelParams(
+            slot=slot, isHalf=isHalf, params=params, files=files
+        )
+
         # Change Filepath
-        for key, val in props["files"].items():
+        for field in fields(props.files):
+            key = field.name
+            val = getattr(props.files, key)
             if val != "-":
                 uploadPath = os.path.join(UPLOAD_DIR, val)
                 storeDir = os.path.join(UPLOAD_DIR, f"{slot}")
                 os.makedirs(storeDir, exist_ok=True)
                 storePath = os.path.join(storeDir, val)
                 shutil.move(uploadPath, storePath)
-                props["files"][key] = storePath
+                setattr(props.files, key, storePath)
             else:
-                props["files"][key] = None
-        # print("---------------------------------------------------2>", props)
+                setattr(props.files, key, None)
 
         info = self.voiceChangerManager.loadModel(props)
         json_compatible_item_data = jsonable_encoder(info)
         return JSONResponse(content=json_compatible_item_data)
-        # return {"load": f"{configFilePath}, {pyTorchModelFilePath}, {onnxModelFilePath}"}
 
-    def post_load_model_for_train(
-        self,
-        modelGFilename: str = Form(...),
-        modelGFilenameChunkNum: int = Form(...),
-        modelDFilename: str = Form(...),
-        modelDFilenameChunkNum: int = Form(...),
-    ):
-        modelGFilePath = concat_file_chunks(
-            UPLOAD_DIR, modelGFilename, modelGFilenameChunkNum, MODEL_DIR
-        )
-        modelDFilePath = concat_file_chunks(
-            UPLOAD_DIR, modelDFilename, modelDFilenameChunkNum, MODEL_DIR
-        )
-        return {"File saved": f"{modelGFilePath}, {modelDFilePath}"}
-
-    def post_extract_voices(
-        self,
-        zipFilename: str = Form(...),
-        zipFileChunkNum: int = Form(...),
-    ):
-        zipFilePath = concat_file_chunks(
-            UPLOAD_DIR, zipFilename, zipFileChunkNum, UPLOAD_DIR
-        )
-        shutil.unpack_archive(zipFilePath, "MMVC_Trainer/dataset/textful/")
-        return {"Zip file unpacked": f"{zipFilePath}"}
-
-    def post_model_type(
-        self,
-        modelType: ModelType = Form(...),
-    ):
+    def post_model_type(self, modelType: ModelType = Form(...)):
         info = self.voiceChangerManager.switchModelType(modelType)
         json_compatible_item_data = jsonable_encoder(info)
         return JSONResponse(content=json_compatible_item_data)
 
-    def get_model_type(
-        self,
-    ):
+    def get_model_type(self):
         info = self.voiceChangerManager.getModelType()
         json_compatible_item_data = jsonable_encoder(info)
         return JSONResponse(content=json_compatible_item_data)

server/sio/MMVC_Namespace.py

@@ -30,7 +30,6 @@ class MMVC_Namespace(socketio.AsyncNamespace):
         else:
             unpackedData = np.array(struct.unpack('<%sh' % (len(data) // struct.calcsize('<h')), data)).astype(np.int16)
 
-            # audio1, perf = self.voiceChangerManager.changeVoice(unpackedData)
             res = self.voiceChangerManager.changeVoice(unpackedData)
             audio1 = res[0]
             perf = res[1] if len(res) == 2 else [0, 0, 0]

server/voice_changer/RVC/RVC.py

@@ -4,8 +4,18 @@ import json
 import resampy
 from voice_changer.RVC.ModelWrapper import ModelWrapper
 from Exceptions import NoModeLoadedException
+from voice_changer.utils.LoadModelParams import LoadModelParams
 from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
 
+from dataclasses import dataclass, asdict, field
+import numpy as np
+import torch
+
+from fairseq import checkpoint_utils
+
+from const import TMP_DIR
+
+
 # avoiding parse arg error in RVC
 sys.argv = ["MMVCServerSIO.py"]
 
@@ -19,25 +29,12 @@ if sys.platform.startswith("darwin"):
 else:
     sys.path.append("RVC")
 
-import io
-from dataclasses import dataclass, asdict, field
-from functools import reduce
-import numpy as np
-import torch
-import onnxruntime
 
-# onnxruntime.set_default_logger_severity(3)
-from const import HUBERT_ONNX_MODEL_PATH, TMP_DIR
-
-import pyworld as pw
-
-from voice_changer.RVC.custom_vc_infer_pipeline import VC
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
 from .models import SynthesizerTrnMsNSFsid as SynthesizerTrnMsNSFsid_webui
 from .models import SynthesizerTrnMsNSFsidNono as SynthesizerTrnMsNSFsidNono_webui
-
 from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
-from fairseq import checkpoint_utils
+from voice_changer.RVC.custom_vc_infer_pipeline import VC
+from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
 
 providers = [
     "OpenVINOExecutionProvider",
@@ -53,7 +50,7 @@ class ModelSlot:
     onnxModelFile: str = ""
     featureFile: str = ""
     indexFile: str = ""
-    defaultTrans: int = ""
+    defaultTrans: int = 0
     modelType: int = RVC_MODEL_TYPE_RVC
     samplingRate: int = -1
     f0: bool = True
@@ -125,19 +122,19 @@ class RVC:
         print("RVC initialization: ", params)
         print("mps: ", self.mps_enabled)
 
-    def loadModel(self, props):
-        self.is_half = props["isHalf"]
-        tmp_slot = props["slot"]
-        params_str = props["params"]
+    def loadModel(self, props: LoadModelParams):
+        self.is_half = props.isHalf
+        tmp_slot = props.slot
+        params_str = props.params
         params = json.loads(params_str)
 
         newSlot = asdict(self.settings.modelSlots[tmp_slot])
         newSlot.update(
             {
-                "pyTorchModelFile": props["files"]["pyTorchModelFilename"],
-                "onnxModelFile": props["files"]["onnxModelFilename"],
-                "featureFile": props["files"]["featureFilename"],
-                "indexFile": props["files"]["indexFilename"],
+                "pyTorchModelFile": props.files.pyTorchModelFilename,
+                "onnxModelFile": props.files.onnxModelFilename,
+                "featureFile": props.files.featureFilename,
+                "indexFile": props.files.indexFilename,
                 "defaultTrans": params["trans"],
             }
         )
@@ -147,14 +144,14 @@ class RVC:
 
         # Load metadata
         if (
-            self.settings.modelSlots[tmp_slot].pyTorchModelFile != None
+            self.settings.modelSlots[tmp_slot].pyTorchModelFile is not None
             and self.settings.modelSlots[tmp_slot].pyTorchModelFile != ""
         ):
             self._setInfoByPytorch(
                 tmp_slot, self.settings.modelSlots[tmp_slot].pyTorchModelFile
             )
         if (
-            self.settings.modelSlots[tmp_slot].onnxModelFile != None
+            self.settings.modelSlots[tmp_slot].onnxModelFile is not None
             and self.settings.modelSlots[tmp_slot].onnxModelFile != ""
         ):
             self._setInfoByONNX(
@@ -241,24 +238,24 @@ class RVC:
 
             if (
                 self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_RVC
-                and self.settings.modelSlots[slot].f0 == True
+                and self.settings.modelSlots[slot].f0 is True
             ):
                 net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=self.is_half)
             elif (
                 self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_RVC
-                and self.settings.modelSlots[slot].f0 == False
+                and self.settings.modelSlots[slot].f0 is False
             ):
                 net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
             elif (
                 self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_WEBUI
-                and self.settings.modelSlots[slot].f0 == True
+                and self.settings.modelSlots[slot].f0 is True
             ):
                 net_g = SynthesizerTrnMsNSFsid_webui(
                     **cpt["params"], is_half=self.is_half
                 )
             elif (
                 self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_WEBUI
-                and self.settings.modelSlots[slot].f0 == False
+                and self.settings.modelSlots[slot].f0 is False
             ):
                 net_g = SynthesizerTrnMsNSFsidNono_webui(
                     **cpt["params"], is_half=self.is_half
@@ -295,7 +292,9 @@ class RVC:
         self.next_index_file = self.settings.modelSlots[slot].indexFile
         self.next_trans = self.settings.modelSlots[slot].defaultTrans
         self.next_samplingRate = self.settings.modelSlots[slot].samplingRate
-        self.next_framework = "ONNX" if self.next_onnx_session != None else "PyTorch"
+        self.next_framework = (
+            "ONNX" if self.next_onnx_session is not None else "PyTorch"
+        )
         print(
             "[Voice Changer] Prepare done.",
         )
@@ -321,7 +320,7 @@ class RVC:
         )
 
     def update_settings(self, key: str, val: any):
-        if key == "onnxExecutionProvider" and self.onnx_session != None:
+        if key == "onnxExecutionProvider" and self.onnx_session is not None:
             if val == "CUDAExecutionProvider":
                 if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num:
                     self.settings.gpu = 0
@@ -345,7 +344,7 @@ class RVC:
                 key == "gpu"
                 and val >= 0
                 and val < self.gpu_num
-                and self.onnx_session != None
+                and self.onnx_session is not None
             ):
                 providers = self.onnx_session.get_providers()
                 print("Providers:", providers)
@@ -374,11 +373,11 @@ class RVC:
         data = asdict(self.settings)
 
         data["onnxExecutionProviders"] = (
-            self.onnx_session.get_providers() if self.onnx_session != None else []
+            self.onnx_session.get_providers() if self.onnx_session is not None else []
         )
         files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
         for f in files:
-            if data[f] != None and os.path.exists(data[f]):
+            if data[f] is not None and os.path.exists(data[f]):
                 data[f] = os.path.basename(data[f])
             else:
                 data[f] = ""
@@ -477,7 +476,7 @@ class RVC:
         return result
 
     def _pyTorch_inference(self, data):
-        if hasattr(self, "net_g") == False or self.net_g == None:
+        if hasattr(self, "net_g") is False or self.net_g is None:
             print(
                 "[Voice Changer] No pyTorch session.",
                 hasattr(self, "net_g"),
@@ -485,7 +484,7 @@ class RVC:
             )
             raise NoModeLoadedException("pytorch")
 
-        if self.settings.gpu < 0 or (self.gpu_num == 0 and self.mps_enabled == False):
+        if self.settings.gpu < 0 or (self.gpu_num == 0 and self.mps_enabled is False):
             dev = torch.device("cpu")
         elif self.mps_enabled:
             dev = torch.device("mps")

server/voice_changer/RVC/const.py

@@ -1,10 +1,2 @@
-# RVC_MODEL_TYPE_NORMAL = 0
-# RVC_MODEL_TYPE_PITCHLESS = 1
-# RVC_MODEL_TYPE_WEBUI_256_NORMAL = 2
-# RVC_MODEL_TYPE_WEBUI_256_PITCHLESS = 3
-# RVC_MODEL_TYPE_WEBUI_768_NORMAL = 4
-# RVC_MODEL_TYPE_WEBUI_768_PITCHLESS = 5
-# RVC_MODEL_TYPE_UNKNOWN = 99
-
 RVC_MODEL_TYPE_RVC = 0
 RVC_MODEL_TYPE_WEBUI = 1

server/voice_changer/VoiceChanger.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, Optional, Protocol, TypeAlias, Union, cast
+from typing import Any, Union, cast
 from const import TMP_DIR, ModelType
 import torch
 import os
@@ -9,6 +9,7 @@ import resampy
 
 
 from voice_changer.IORecorder import IORecorder
+from voice_changer.utils.LoadModelParams import LoadModelParams
 
 from voice_changer.utils.Timer import Timer
 from voice_changer.utils.VoiceChangerModel import VoiceChangerModel, AudioInOut
@@ -24,8 +25,6 @@ providers = [
 
 STREAM_INPUT_FILE = os.path.join(TMP_DIR, "in.wav")
 STREAM_OUTPUT_FILE = os.path.join(TMP_DIR, "out.wav")
-STREAM_ANALYZE_FILE_DIO = os.path.join(TMP_DIR, "analyze-dio.png")
-STREAM_ANALYZE_FILE_HARVEST = os.path.join(TMP_DIR, "analyze-harvest.png")
 
 
 @dataclass
@@ -51,18 +50,20 @@ class VoiceChangerSettings:
 class VoiceChanger:
     settings: VoiceChangerSettings
     voiceChanger: VoiceChangerModel
+    ioRecorder: IORecorder
+    sola_buffer: AudioInOut
 
     def __init__(self, params: VoiceChangerParams):
         # 初期化
         self.settings = VoiceChangerSettings()
         self.onnx_session = None
-        self.currentCrossFadeOffsetRate = 0
-        self.currentCrossFadeEndRate = 0
+        self.currentCrossFadeOffsetRate = 0.0
+        self.currentCrossFadeEndRate = 0.0
         self.currentCrossFadeOverlapSize = 0  # setting
         self.crossfadeSize = 0  # calculated
 
         self.voiceChanger = None
-        self.modelType = None
+        self.modelType: ModelType | None = None
         self.params = params
         self.gpu_num = torch.cuda.device_count()
         self.prev_audio = np.zeros(4096)
@@ -76,7 +77,7 @@ class VoiceChanger:
         )
 
     def switchModelType(self, modelType: ModelType):
-        if hasattr(self, "voiceChanger") and self.voiceChanger != None:
+        if hasattr(self, "voiceChanger") and self.voiceChanger is not None:
             # return {"status": "ERROR", "msg": "vc is already selected. currently re-select is not implemented"}
             del self.voiceChanger
             self.voiceChanger = None
@@ -114,34 +115,18 @@ class VoiceChanger:
         return {"status": "OK", "msg": "vc is switched."}
 
     def getModelType(self):
-        if self.modelType != None:
+        if self.modelType is not None:
             return {"status": "OK", "vc": self.modelType}
         else:
             return {"status": "OK", "vc": "none"}
 
-    def loadModel(
-        self,
-        props,
-    ):
+    def loadModel(self, props: LoadModelParams):
         try:
             return self.voiceChanger.loadModel(props)
         except Exception as e:
             print("[Voice Changer] Model Load Error! Check your model is valid.", e)
             return {"status": "NG"}
 
-        # try:
-        #     if self.modelType == "MMVCv15" or self.modelType == "MMVCv13":
-        #         return self.voiceChanger.loadModel(config, pyTorch_model_file, onnx_model_file)
-        #     elif self.modelType == "so-vits-svc-40" or self.modelType == "so-vits-svc-40_c" or self.modelType == "so-vits-svc-40v2":
-        #         return self.voiceChanger.loadModel(config, pyTorch_model_file, onnx_model_file, clusterTorchModel)
-        #     elif self.modelType == "RVC":
-        #         return self.voiceChanger.loadModel(slot, config, pyTorch_model_file, onnx_model_file, feature_file, index_file, is_half)
-        #     else:
-        #         return self.voiceChanger.loadModel(config, pyTorch_model_file, onnx_model_file, clusterTorchModel)
-        # except Exception as e:
-        #     print("[Voice Changer] Model Load Error! Check your model is valid.", e)
-        #     return {"status": "NG"}
-
     def get_info(self):
         data = asdict(self.settings)
         if hasattr(self, "voiceChanger"):
@@ -167,14 +152,6 @@ class VoiceChanger:
                 if hasattr(self, "ioRecorder"):
                     self.ioRecorder.close()
 
-                # if hasattr(self, "ioAnalyzer") == False:
-                #     self.ioAnalyzer = IOAnalyzer()
-
-                # try:
-                #     self.ioAnalyzer.analyze(STREAM_INPUT_FILE, STREAM_ANALYZE_FILE_DIO, STREAM_ANALYZE_FILE_HARVEST, self.settings.inputSampleRate)
-
-                # except Exception as e:
-                #     print("recordIO exception", e)
         elif key in self.settings.floatData:
             setattr(self.settings, key, float(val))
         elif key in self.settings.strData:
@@ -182,10 +159,10 @@ class VoiceChanger:
         else:
             if hasattr(self, "voiceChanger"):
                 ret = self.voiceChanger.update_settings(key, val)
-                if ret == False:
+                if ret is False:
                     print(f"{key} is not mutable variable or unknown variable!")
             else:
-                print(f"voice changer is not initialized!")
+                print("voice changer is not initialized!")
         return self.get_info()
 
     def _generate_strength(self, crossfadeSize: int):
@@ -228,9 +205,9 @@ class VoiceChanger:
             )
 
             # 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") == True:
+            if hasattr(self, "np_prev_audio1") is True:
                 delattr(self, "np_prev_audio1")
-            if hasattr(self, "sola_buffer"):
+            if hasattr(self, "sola_buffer") is True:
                 del self.sola_buffer
 
     #  receivedData: tuple of short
@@ -275,9 +252,14 @@ class VoiceChanger:
                 # Inference
                 audio = self.voiceChanger.inference(data)
 
-                if hasattr(self, "sola_buffer") == True:
+                if hasattr(self, "sola_buffer") is True:
                     np.set_printoptions(threshold=10000)
-                    audio = audio[-sola_search_frame - crossfade_frame - block_frame :]
+                    audio_offset = -1 * (
+                        sola_search_frame + crossfade_frame + block_frame
+                    )
+                    audio = audio[audio_offset:]
+                    a = 0
+                    audio = audio[a:]
                     # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC, https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI
                     cor_nom = np.convolve(
                         audio[: crossfade_frame + sola_search_frame],
@@ -292,11 +274,9 @@ class VoiceChanger:
                         )
                         + 1e-3
                     )
-                    sola_offset = np.argmax(cor_nom / cor_den)
-
-                    output_wav = audio[sola_offset : sola_offset + block_frame].astype(
-                        np.float64
-                    )
+                    sola_offset = int(np.argmax(cor_nom / cor_den))
+                    sola_end = sola_offset + block_frame
+                    output_wav = audio[sola_offset:sola_end].astype(np.float64)
                     output_wav[:crossfade_frame] *= self.np_cur_strength
                     output_wav[:crossfade_frame] += self.sola_buffer[:]
 
@@ -306,15 +286,12 @@ class VoiceChanger:
                     result = np.zeros(4096).astype(np.int16)
 
                 if (
-                    hasattr(self, "sola_buffer") == True
+                    hasattr(self, "sola_buffer") is True
                     and sola_offset < sola_search_frame
                 ):
-                    sola_buf_org = audio[
-                        -sola_search_frame
-                        - crossfade_frame
-                        + sola_offset : -sola_search_frame
-                        + sola_offset
-                    ]
+                    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
@@ -379,7 +356,7 @@ PRINT_CONVERT_PROCESSING: bool = False
 
 
 def print_convert_processing(mess: str):
-    if PRINT_CONVERT_PROCESSING == True:
+    if PRINT_CONVERT_PROCESSING is True:
         print(mess)
 
 

server/voice_changer/VoiceChangerManager.py

@@ -1,6 +1,8 @@
 import numpy as np
 from voice_changer.VoiceChanger import VoiceChanger
 from const import ModelType
+from voice_changer.utils.LoadModelParams import LoadModelParams
+from voice_changer.utils.VoiceChangerModel import AudioInOut
 from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
 
 
@@ -15,7 +17,7 @@ class VoiceChangerManager(object):
             cls._instance.voiceChanger = VoiceChanger(params)
         return cls._instance
 
-    def loadModel(self, props):
+    def loadModel(self, props: LoadModelParams):
         info = self.voiceChanger.loadModel(props)
         if hasattr(info, "status") and info["status"] == "NG":
             return info
@@ -31,7 +33,7 @@ class VoiceChangerManager(object):
         else:
             return {"status": "ERROR", "msg": "no model loaded"}
 
-    def update_settings(self, key: str, val: any):
+    def update_settings(self, key: str, val: str | int | float):
         if hasattr(self, "voiceChanger"):
             info = self.voiceChanger.update_settings(key, val)
             info["status"] = "OK"
@@ -39,7 +41,7 @@ class VoiceChangerManager(object):
         else:
             return {"status": "ERROR", "msg": "no model loaded"}
 
-    def changeVoice(self, receivedData: any):
+    def changeVoice(self, receivedData: AudioInOut):
         if hasattr(self, "voiceChanger") is True:
             return self.voiceChanger.on_request(receivedData)
         else:

server/voice_changer/utils/LoadModelParams.py

@@ -0,0 +1,19 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class FilePaths:
+    configFilename: str
+    pyTorchModelFilename: str
+    onnxModelFilename: str
+    clusterTorchModelFilename: str
+    featureFilename: str
+    indexFilename: str
+
+
+@dataclass
+class LoadModelParams:
+    slot: int
+    isHalf: bool
+    files: FilePaths
+    params: str

server/voice_changer/utils/VoiceChangerModel.py

@@ -1,14 +1,30 @@
-from typing import Any, Callable, Protocol, TypeAlias
+from typing import Any, Protocol, TypeAlias
 import numpy as np
 
+from voice_changer.utils.LoadModelParams import LoadModelParams
+
 
 AudioInOut: TypeAlias = np.ndarray[Any, np.dtype[np.int16]]
 
 
 class VoiceChangerModel(Protocol):
-    loadModel: Callable[..., dict[str, Any]]
-    def get_processing_sampling_rate(self) -> int: ...
-    def get_info(self) -> dict[str, Any]: ...
-    def inference(self, data: tuple[Any, ...]) -> Any: ...
-    def generate_input(self, newData: AudioInOut, inputSize: int, crossfadeSize: int) -> tuple[Any, ...]: ...
-    def update_settings(self, key: str, val: Any) -> bool: ...
+    # loadModel: Callable[..., dict[str, Any]]
+    def loadModel(self, params: LoadModelParams):
+        ...
+
+    def get_processing_sampling_rate(self) -> int:
+        ...
+
+    def get_info(self) -> dict[str, Any]:
+        ...
+
+    def inference(self, data: tuple[Any, ...]) -> Any:
+        ...
+
+    def generate_input(
+        self, newData: AudioInOut, inputSize: int, crossfadeSize: int
+    ) -> tuple[Any, ...]:
+        ...
+
+    def update_settings(self, key: str, val: Any) -> bool:
+        ...