wip

.gitignore

@@ -5,3 +5,7 @@ __pycache__
 server/upload_dir/
 server/MMVC_Trainer/
 server/key
+server/info
+
+server/memo.txt
+

server/MMVCServerSIO.py

@@ -19,47 +19,17 @@ from fastapi.responses import JSONResponse
 
 import uvicorn
 import socketio
-from pydantic import BaseModel
 
 from typing import Callable
-
-from mods.Trainer_Speakers import mod_get_speakers
-from mods.Trainer_Training import mod_post_pre_training, mod_post_start_training, mod_post_stop_training, mod_get_related_files, mod_get_tail_training_log
-from mods.Trainer_Model import mod_get_model, mod_delete_model
-
-from mods.Trainer_Models import mod_get_models
-from mods.Trainer_MultiSpeakerSetting import mod_get_multi_speaker_setting, mod_post_multi_speaker_setting
-from mods.Trainer_Speaker_Voice import mod_get_speaker_voice
-from mods.Trainer_Speaker_Voices import mod_get_speaker_voices
-
-from mods.Trainer_Speaker import mod_delete_speaker
-from mods.FileUploader import upload_file, concat_file_chunks
-
 from mods.VoiceChanger import VoiceChanger
-
 from mods.ssl import create_self_signed_cert
-
 from voice_changer.VoiceChangerManager import VoiceChangerManager
 from sio.MMVC_SocketIOApp import MMVC_SocketIOApp
 
-
 from restapi.MMVC_Rest import MMVC_Rest
-from pydantic import BaseModel
-
-class VoiceModel(BaseModel):
-    gpu: int
-    srcId: int
-    dstId: int
-    timestamp: int
-    prefixChunkSize: int
-    buffer: str
-
-@dataclass
-class ExApplicationInfo():
-    external_tensorboard_port: int
 
 
-exApplitionInfo = ExApplicationInfo(external_tensorboard_port=0)
+
 
 
 
@@ -105,10 +75,6 @@ args = parser.parse_args()
 printMessage(f"Phase name:{__name__}", level=2)
 thisFilename = os.path.basename(__file__)[:-3]
 
-
-
-
-
 if __name__ == thisFilename or args.colab == True:
     printMessage(f"PHASE3:{__name__}", level=2)
     TYPE = args.t
@@ -116,9 +82,9 @@ if __name__ == thisFilename or args.colab == True:
     CONFIG = args.c
     MODEL = args.m
 
-    if os.getenv("EX_TB_PORT"):
-        EX_TB_PORT = os.environ["EX_TB_PORT"]
-        exApplitionInfo.external_tensorboard_port = int(EX_TB_PORT)
+    # if os.getenv("EX_TB_PORT"):
+    #     EX_TB_PORT = os.environ["EX_TB_PORT"]
+    #     exApplitionInfo.external_tensorboard_port = int(EX_TB_PORT)
 
     voiceChangerManager = VoiceChangerManager.get_instance()    
     if CONFIG and MODEL:
@@ -126,97 +92,6 @@ if __name__ == thisFilename or args.colab == True:
     app_fastapi = MMVC_Rest.get_instance(voiceChangerManager)
     app_socketio = MMVC_SocketIOApp.get_instance(app_fastapi, voiceChangerManager)
 
-    ############
-    # File Uploder
-    # ##########
-    # @app_fastapi.post("/load_model_for_train")
-    # async def post_load_model_for_train(
-    #     modelGFilename: str = Form(...),
-    #     modelGFilenameChunkNum: int = Form(...),
-    #     modelDFilename: str = Form(...),
-    #     modelDFilenameChunkNum: int = Form(...),
-    # ):
-
-
-
-    # @app_fastapi.post("/extract_voices")
-    # async def post_load_model(
-    #     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}"}
-
-    ############
-    # Voice Changer
-    # ##########
-
-    # Trainer REST API ※ ColabがTop直下のパスにしかPOSTを投げれないようなので"REST風"
-
-    @app_fastapi.get("/get_speakers")
-    async def get_speakers():
-        return mod_get_speakers()
-
-    @app_fastapi.delete("/delete_speaker")
-    async def delete_speaker(speaker: str = Form(...)):
-        return mod_delete_speaker(speaker)
-
-    @app_fastapi.get("/get_speaker_voices")
-    async def get_speaker_voices(speaker: str):
-        return mod_get_speaker_voices(speaker)
-
-    @app_fastapi.get("/get_speaker_voice")
-    async def get_speaker_voices(speaker: str, voice: str):
-        return mod_get_speaker_voice(speaker, voice)
-
-    @app_fastapi.get("/get_multi_speaker_setting")
-    async def get_multi_speaker_setting():
-        return mod_get_multi_speaker_setting()
-
-    @app_fastapi.post("/post_multi_speaker_setting")
-    async def post_multi_speaker_setting(setting: str = Form(...)):
-        return mod_post_multi_speaker_setting(setting)
-
-    @app_fastapi.get("/get_models")
-    async def get_models():
-        return mod_get_models()
-
-    @app_fastapi.get("/get_model")
-    async def get_model(model: str):
-        return mod_get_model(model)
-
-    @app_fastapi.delete("/delete_model")
-    async def delete_model(model: str = Form(...)):
-        return mod_delete_model(model)
-
-    @app_fastapi.post("/post_pre_training")
-    async def post_pre_training(batch: int = Form(...)):
-        return mod_post_pre_training(batch)
-
-    @app_fastapi.post("/post_start_training")
-    async def post_start_training(enable_finetuning: bool = Form(...),GModel: str = Form(...),DModel: str = Form(...)):
-        print("POST START TRAINING..")
-        return mod_post_start_training(enable_finetuning, GModel, DModel)
-
-    @app_fastapi.post("/post_stop_training")
-    async def post_stop_training():
-        print("POST STOP TRAINING..")
-        return mod_post_stop_training()
-
-    @app_fastapi.get("/get_related_files")
-    async def get_related_files():
-        return mod_get_related_files()
-
-    @app_fastapi.get("/get_tail_training_log")
-    async def get_tail_training_log(num: int):
-        return mod_get_tail_training_log(num)
-
-    @app_fastapi.get("/get_ex_application_info")
-    async def get_ex_application_info():
-        json_compatible_item_data = jsonable_encoder(exApplitionInfo)
-        return JSONResponse(content=json_compatible_item_data)
 
 if __name__ == '__mp_main__':
     printMessage(f"PHASE2:{__name__}", level=2)

server/mods/Trainer_Model.py

@@ -0,0 +1,13 @@
+
+from fastapi.responses import FileResponse
+import os
+
+def mod_get_model(modelFile:str):
+        modelPath = os.path.join("MMVC_Trainer/logs", modelFile)
+        return FileResponse(path=modelPath)
+
+def mod_delete_model(modelFile:str):
+        modelPath = os.path.join("MMVC_Trainer/logs", modelFile)
+        os.unlink(modelPath)
+        return {"Model deleted": f"{modelFile}"}
+

server/mods/Trainer_Models.py

@@ -0,0 +1,23 @@
+
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+from trainer_mods.files import get_file_list
+import os
+
+def mod_get_models():
+    gModels = get_file_list(f'MMVC_Trainer/logs/G*.pth')
+    dModels = get_file_list(f'MMVC_Trainer/logs/D*.pth')
+    configs = get_file_list(f'MMVC_Trainer/logs/config.json')
+    models = []
+    models.extend(gModels)
+    models.extend(dModels)
+    models.extend(configs)
+    models = [ os.path.basename(x) for x in models]
+
+    models = sorted(models)
+    data = {
+            "models":models
+            }
+    json_compatible_item_data = jsonable_encoder(data)
+    return JSONResponse(content=json_compatible_item_data)
+

server/mods/Trainer_MultiSpeakerSetting.py

@@ -0,0 +1,26 @@
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+import os
+
+MULTI_SPEAKER_SETTING_PATH = "MMVC_Trainer/dataset/multi_speaker_correspondence.txt"
+def mod_get_multi_speaker_setting():
+    data = {}
+    if os.path.isfile(MULTI_SPEAKER_SETTING_PATH) == False:
+        with open(MULTI_SPEAKER_SETTING_PATH, "w") as f:
+            f.write("")
+            f.flush()
+        f.close()
+
+    with open(MULTI_SPEAKER_SETTING_PATH, "r") as f:
+        setting = f.read()
+        data["multi_speaker_setting"] = setting
+    json_compatible_item_data = jsonable_encoder(data)
+    return JSONResponse(content=json_compatible_item_data)
+
+
+def mod_post_multi_speaker_setting(setting:str):
+    with open(MULTI_SPEAKER_SETTING_PATH, "w") as f:
+        f.write(setting)
+        f.flush()
+    f.close()
+    return {"Write Multispeaker setting": f"{setting}"}

server/mods/Trainer_Speaker.py

@@ -0,0 +1,15 @@
+import shutil
+from mods.Trainer_MultiSpeakerSetting import MULTI_SPEAKER_SETTING_PATH
+
+def mod_delete_speaker(speaker:str):
+        shutil.rmtree(f"MMVC_Trainer/dataset/textful/{speaker}")
+
+        with open(MULTI_SPEAKER_SETTING_PATH, "r") as f:
+            setting = f.readlines()
+
+        filtered = filter(lambda x: x.startswith(f"{speaker}|")==False, setting)
+        with open(MULTI_SPEAKER_SETTING_PATH, "w") as f:
+            f.writelines(list(filtered))
+            f.flush()
+        f.close()
+        return {"Speaker deleted": f"{speaker}"}

server/mods/Trainer_Speaker_Voice.py

@@ -0,0 +1,28 @@
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+import os,  base64
+
+def mod_get_speaker_voice(speaker:str, voice:str):
+        wav_file = f'MMVC_Trainer/dataset/textful/{speaker}/wav/{voice}.wav'
+        text_file = f'MMVC_Trainer/dataset/textful/{speaker}/text/{voice}.txt'
+        readable_text_file = f'MMVC_Trainer/dataset/textful/{speaker}/readable_text/{voice}.txt'
+
+        data = {}
+        if os.path.exists(wav_file):
+            with open(wav_file, "rb") as f:
+                wav_data = f.read()
+            wav_data_base64 = base64.b64encode(wav_data).decode('utf-8')
+            data["wav"] = wav_data_base64
+
+
+        if os.path.exists(text_file):
+            with open(text_file, "r") as f:
+                text_data = f.read()
+            data["text"] = text_data
+
+        if os.path.exists(readable_text_file):
+            with open(readable_text_file, "r") as f:
+                text_data = f.read()
+            data["readable_text"] = text_data
+        json_compatible_item_data = jsonable_encoder(data)
+        return JSONResponse(content=json_compatible_item_data)

server/mods/Trainer_Speaker_Voices.py

@@ -0,0 +1,22 @@
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+from trainer_mods.files import get_file_list
+import os
+
+def mod_get_speaker_voices(speaker:str):
+        voices = get_file_list(f'MMVC_Trainer/dataset/textful/{speaker}/wav/*.wav')
+
+        texts = get_file_list(f'MMVC_Trainer/dataset/textful/{speaker}/text/*.txt')
+
+        readable_texts = get_file_list(f'MMVC_Trainer/dataset/textful/{speaker}/readable_text/*.txt')
+
+        items = voices
+        items.extend(texts)
+        items.extend(readable_texts)
+        items = [ os.path.splitext(os.path.basename(x))[0] for x in items]
+        items = sorted(set(items))
+        data = {
+                "voices":items
+                }
+        json_compatible_item_data = jsonable_encoder(data)
+        return JSONResponse(content=json_compatible_item_data)

server/mods/Trainer_Speakers.py

@@ -0,0 +1,15 @@
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+from trainer_mods.files import get_dir_list
+import os
+# CreateはFileUploaderで実装。
+
+def mod_get_speakers():
+    os.makedirs("MMVC_Trainer/dataset/textful", exist_ok=True)
+    speakers = get_dir_list("MMVC_Trainer/dataset/textful/")
+    
+    data = {
+        "speakers":sorted(speakers)
+    }
+    json_compatible_item_data = jsonable_encoder(data)
+    return JSONResponse(content=json_compatible_item_data)

server/mods/Trainer_Training.py

@@ -0,0 +1,176 @@
+import subprocess,os
+from trainer_mods.files import get_file_list
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+
+LOG_DIR = "info"
+train_proc = None
+
+SUCCESS = 0
+ERROR = -1
+### Submodule for Pre train
+def sync_exec(cmd:str, log_path:str, cwd=None):
+    shortCmdStr = cmd[:20]
+    try:
+        with open(log_path, 'w') as log_file:
+            if cwd == None:
+                proc = subprocess.run(cmd, shell=True, text=True, stdout=log_file, stderr=log_file)
+            else:
+                proc = subprocess.run(cmd, shell=True, text=True, stdout=log_file, stderr=log_file, cwd=cwd)
+            print(f"{shortCmdStr} returncode:{proc.returncode}")
+            if proc.returncode != 0:
+                print(f"{shortCmdStr} exception:")
+                return (ERROR, f"returncode:{proc.returncode}")
+    except Exception as e:
+        print(f"{shortCmdStr} exception:", str(e))
+        return (ERROR, str(e))
+    return (SUCCESS, "success")
+
+def sync_exec_with_stdout(cmd:str, log_path:str):
+    shortCmdStr = cmd[:20]
+    try:
+        with open(log_path, 'w') as log_file:
+            proc = subprocess.run(cmd, shell=True, text=True, stdout=subprocess.PIPE,
+            stderr=log_file, cwd="MMVC_Trainer")
+            print(f"STDOUT{shortCmdStr}",proc.stdout)
+    except Exception as e:
+        print(f"{shortCmdStr} exception:", str(e))
+        return (ERROR, str(e))
+    return (SUCCESS, proc.stdout)
+
+
+def create_dataset():
+    cmd = "python3 create_dataset_jtalk.py -f train_config -s 24000 -m dataset/multi_speaker_correspondence.txt"
+    log_file = os.path.join(LOG_DIR, "log_create_dataset_jtalk.txt")
+    res = sync_exec(cmd, log_file, "MMVC_Trainer")
+    return res
+
+def set_batch_size(batch:int):
+    cmd = "sed -i 's/\"batch_size\": [0-9]*/\"batch_size\": " + str(batch) + "/' MMVC_Trainer/configs/baseconfig.json"
+    log_file = os.path.join(LOG_DIR, "log_set_batch_size.txt")
+    res = sync_exec(cmd, log_file)
+    return res
+
+def set_dummy_device_count():
+    cmd = 'sed -ie "s/torch.cuda.device_count()/1/" MMVC_Trainer/train_ms.py'
+    log_file = os.path.join(LOG_DIR, "log_set_dummy_device_count.txt")
+    res = sync_exec(cmd, log_file)
+    return res
+
+### Submodule for Train 
+def exec_training(enable_finetuning:bool, GModel:str, DModel:str):
+    global train_proc
+    log_file = os.path.join(LOG_DIR, "training.txt")
+
+    # トレーニング開始確認(二重起動回避)
+    if train_proc != None:
+        status = train_proc.poll()
+        if status != None:
+            print("Training have ended.", status)
+            train_proc = None
+        else:
+            print("Training have stated.")
+            return (ERROR, "Training have started")
+
+    try:
+        with open(log_file, 'w') as log_file:
+            if enable_finetuning == True:
+                GModelPath = os.path.join("logs", GModel) # 実行時にcwdを指定しているのでフォルダはlogsでよい。
+                DModelPath = os.path.join("logs", DModel)
+                cmd = f'python3 train_ms.py -c configs/train_config.json -m ./ -fg {GModelPath} -fd {DModelPath}'
+            else:
+                cmd = 'python3 train_ms.py -c configs/train_config.json -m ./'
+            print("exec:",cmd)
+            train_proc = subprocess.Popen("exec "+cmd, shell=True, text=True, stdout=log_file, stderr=log_file, cwd="MMVC_Trainer")
+            print("Training stated")
+            print(f"returncode:{train_proc.returncode}")
+    except Exception as e:
+        print("start training exception:", str(e))
+        return (ERROR,  str(e))
+
+    return (SUCCESS, "success")
+
+def stop_training():
+    global train_proc
+    if train_proc == None:
+        print("Training have not stated.")
+        return (ERROR, "Training have not stated.")
+
+    status = train_proc.poll()
+    if status != None:
+        print("Training have already ended.", status)
+        train_proc = None
+        return (ERROR, "Training have already ended. " + status)
+    else:
+        train_proc.kill()
+        print("Training have stoped.")
+        return (SUCCESS, "success")
+
+### Main
+def mod_post_pre_training(batch:int):
+    res = set_batch_size(batch)
+    if res[0] == ERROR:
+        return {"result":"failed", "detail": f"Preprocess(set_batch_size) failed. {res[1]}"}
+
+    res = set_dummy_device_count()
+    if res[0] == ERROR:
+        return {"result":"failed", "detail": f"Preprocess(set_dummy_device_count) failed. {res[1]}"}
+
+    res = create_dataset()
+    if res[0] == ERROR:
+        return {"result":"failed", "detail": f"Preprocess failed(create_dataset). {res[1]}"}
+
+    return {"result":"success", "detail": f"Preprocess succeeded. {res[1]}"}
+
+
+def mod_post_start_training(enable_finetuning:str, GModel:str, DModel:str):
+    print("START_TRAINING:::::::", enable_finetuning, GModel, DModel)
+    res = exec_training(enable_finetuning, GModel, DModel)
+    if res[0] == ERROR:
+        return {"result":"failed", "detail": f"Start training failed. {res[1]}"}
+
+    return {"result":"success", "detail": f"Start training succeeded. {res[1]}"}
+
+def mod_post_stop_training():
+    res = stop_training()
+    if res[0] == ERROR:
+        return {"result":"failed", "detail": f"Stop training failed. {res[1]}"}
+
+    return {"result":"success", "detail": f"Stop training succeeded. {res[1]}"}
+
+### DEBUG
+def mod_get_related_files():
+    files = get_file_list(os.path.join(LOG_DIR,"*"))
+    files.extend([
+        "MMVC_Trainer/dataset/multi_speaker_correspondence.txt",
+        "MMVC_Trainer/train_ms.py",
+    ])
+    files.extend(
+        get_file_list("MMVC_Trainer/configs/*")
+    )
+
+    res = []
+    for f in files:
+        size = os.path.getsize(f)
+        data = ""
+        if size < 1024*1024:
+            with open(f, "r") as input:
+                data = input.read()
+    
+        res.append({
+            "name":f,
+            "size":size,
+            "data":data
+        })
+
+    json_compatible_item_data = jsonable_encoder(res)
+    return JSONResponse(content=json_compatible_item_data)
+
+def mod_get_tail_training_log(num:int):
+    training_log_file = os.path.join(LOG_DIR, "training.txt")
+    res = sync_exec(f"cat {training_log_file} | sed -e 's/.*\r//' > /tmp/out","/dev/null")
+    cmd = f'tail -n {num} /tmp/out'
+    res = sync_exec_with_stdout(cmd, "/dev/null")
+    if res[0] == ERROR:
+        return {"result":"failed", "detail": f"Tail training log failed. {res[1]}"}
+    return {"result":"success", "detail":res[1]}

server/mods/VoiceChanger.py

@@ -0,0 +1,106 @@
+import torch
+
+from scipy.io.wavfile import write, read
+import numpy as np
+import traceback
+
+import utils
+import commons
+from models import SynthesizerTrn
+
+from text.symbols import symbols
+from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
+
+from mel_processing import spectrogram_torch
+from text import text_to_sequence, cleaned_text_to_sequence
+
+
+
+class VoiceChanger():
+    def __init__(self, config, model):
+        self.hps = utils.get_hparams_from_file(config)
+        self.net_g = SynthesizerTrn(
+            len(symbols),
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            n_speakers=self.hps.data.n_speakers,
+            **self.hps.model)
+        self.net_g.eval()
+        self.gpu_num = torch.cuda.device_count()
+        utils.load_checkpoint(model, self.net_g, None)
+
+        text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
+        text_norm = commons.intersperse(text_norm, 0)
+        self.text_norm = torch.LongTensor(text_norm)
+        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 destroy(self):
+        del self.net_g
+
+    def on_request(self, gpu, srcId, dstId, timestamp, prefixChunkSize, wav):
+        unpackedData = wav
+        convertSize = unpackedData.shape[0] + (prefixChunkSize * 512)
+
+        try:
+
+            audio = torch.FloatTensor(unpackedData.astype(np.float32))
+            audio_norm = audio / self.hps.data.max_wav_value
+            audio_norm = audio_norm.unsqueeze(0)
+            self.audio_buffer = torch.cat(
+                [self.audio_buffer, audio_norm], axis=1)
+            audio_norm = self.audio_buffer[:, -convertSize:]
+            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 < 0 or self.gpu_num == 0:
+                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([dstId]).cpu()
+                    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).cpu().float().numpy()
+            # elif self.mps_enabled == True: # MPS doesnt support aten::weight_norm_interface, and PYTORCH_ENABLE_MPS_FALLBACK=1 cause a big dely.
+            #         x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
+            #             x.to("mps") for x in data]
+            #         sid_tgt1 = torch.LongTensor([dstId]).to("mps")
+            #         audio1 = (self.net_g.to("mps").voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
+            #                   0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
+
+            else:
+                with torch.no_grad():
+                    x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
+                        x.cuda(gpu) for x in data]
+                    sid_tgt1 = torch.LongTensor([dstId]).cuda(gpu)
+                    audio1 = (self.net_g.cuda(gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
+                              0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
+
+            # if len(self.prev_audio) > unpackedData.shape[0]:
+            #     prevLastFragment = self.prev_audio[-unpackedData.shape[0]:]
+            #     curSecondLastFragment = audio1[-unpackedData.shape[0]*2:-unpackedData.shape[0]]
+            #     print("prev, cur", prevLastFragment.shape, curSecondLastFragment.shape)
+            # self.prev_audio = audio1
+            # print("self.prev_audio", self.prev_audio.shape)
+
+            audio1 = audio1[-unpackedData.shape[0]*2:]
+
+        except Exception as e:
+            print("VC PROCESSING!!!! EXCEPTION!!!", e)
+            print(traceback.format_exc())
+
+        audio1 = audio1.astype(np.int16)
+        return audio1

server/mods/Whisper.py

@@ -0,0 +1,36 @@
+import whisper
+import numpy as np
+import torchaudio
+from scipy.io.wavfile import write
+
+_MODELS = {
+    "tiny": "/whisper/tiny.pt",
+    "base": "/whisper/base.pt",
+    "small": "/whisper/small.pt",
+    "medium": "/whisper/medium.pt",
+}
+
+
+class Whisper():
+    def __init__(self):
+        self.storedSizeFromTry = 0
+
+    def loadModel(self, model):
+        # self.model = whisper.load_model(_MODELS[model], device="cpu")
+        self.model = whisper.load_model(_MODELS[model])
+        self.data = np.zeros(1).astype(np.float)
+    
+    def addData(self, unpackedData):
+        self.data = np.concatenate([self.data, unpackedData], 0)
+
+    def transcribe(self, audio):
+        received_data_file = "received_data.wav"
+        write(received_data_file, 24000, self.data.astype(np.int16))
+        source, sr = torchaudio.load(received_data_file) 
+        target = torchaudio.functional.resample(source, 24000, 16000)
+        result = self.model.transcribe(received_data_file)
+        print("WHISPER1:::", result["text"])
+        print("WHISPER2:::", result["segments"])
+        self.data = np.zeros(1).astype(np.float)
+        return result["text"]
+

server/mods/ssl.py

@@ -0,0 +1,24 @@
+import os
+from OpenSSL import crypto
+
+def create_self_signed_cert(certfile, keyfile, certargs, cert_dir="."):
+    C_F = os.path.join(cert_dir, certfile)
+    K_F = os.path.join(cert_dir, keyfile)
+    if not os.path.exists(C_F) or not os.path.exists(K_F):
+        k = crypto.PKey()
+        k.generate_key(crypto.TYPE_RSA, 2048)
+        cert = crypto.X509()
+        cert.get_subject().C = certargs["Country"]
+        cert.get_subject().ST = certargs["State"]
+        cert.get_subject().L = certargs["City"]
+        cert.get_subject().O = certargs["Organization"]
+        cert.get_subject().OU = certargs["Org. Unit"]
+        cert.get_subject().CN = 'Example'
+        cert.set_serial_number(1000)
+        cert.gmtime_adj_notBefore(0)
+        cert.gmtime_adj_notAfter(315360000)
+        cert.set_issuer(cert.get_subject())
+        cert.set_pubkey(k)
+        cert.sign(k, 'sha1')
+        open(C_F, "wb").write(crypto.dump_certificate(crypto.FILETYPE_PEM, cert))
+        open(K_F, "wb").write(crypto.dump_privatekey(crypto.FILETYPE_PEM, k))

server/restapi/MMVC_Rest.py

@@ -8,6 +8,10 @@ from voice_changer.VoiceChangerManager import VoiceChangerManager
 from restapi.MMVC_Rest_Hello import MMVC_Rest_Hello
 from restapi.MMVC_Rest_VoiceChanger import MMVC_Rest_VoiceChanger
 from restapi.MMVC_Rest_Fileuploader import MMVC_Rest_Fileuploader
+from restapi.MMVC_Rest_Trainer import MMVC_Rest_Trainer
+
+
+
 class ValidationErrorLoggingRoute(APIRoute):
     def get_route_handler(self) -> Callable:
         original_route_handler = super().get_route_handler()
@@ -53,6 +57,8 @@ class MMVC_Rest:
             app_fastapi.include_router(restVoiceChanger.router)
             fileUploader = MMVC_Rest_Fileuploader(voiceChangerManager)
             app_fastapi.include_router(fileUploader.router)
+            trainer = MMVC_Rest_Trainer()
+            app_fastapi.include_router(trainer.router)
 
             cls._instance = app_fastapi
             return cls._instance

server/restapi/MMVC_Rest_Fileuploader.py

@@ -5,7 +5,7 @@ from fastapi.encoders import jsonable_encoder
 from fastapi.responses import JSONResponse
 from fastapi import HTTPException, FastAPI, UploadFile, File, Form
 
-from mods.FileUploader import upload_file, concat_file_chunks
+from restapi.mods.FileUploader import upload_file, concat_file_chunks
 from voice_changer.VoiceChangerManager import VoiceChangerManager
 
 UPLOAD_DIR = "upload_dir"

server/restapi/MMVC_Rest_Trainer.py

@@ -0,0 +1,94 @@
+import os
+
+from fastapi import APIRouter,Form
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import JSONResponse
+
+
+from mods.Trainer_Speakers import mod_get_speakers
+from mods.Trainer_Training import mod_post_pre_training, mod_post_start_training, mod_post_stop_training, mod_get_related_files, mod_get_tail_training_log
+from mods.Trainer_Model import mod_get_model, mod_delete_model
+
+from mods.Trainer_Models import mod_get_models
+from mods.Trainer_MultiSpeakerSetting import mod_get_multi_speaker_setting, mod_post_multi_speaker_setting
+from mods.Trainer_Speaker_Voice import mod_get_speaker_voice
+from mods.Trainer_Speaker_Voices import mod_get_speaker_voices
+
+from mods.Trainer_Speaker import mod_delete_speaker
+from dataclasses import dataclass
+
+INFO_DIR = "info"
+os.makedirs(INFO_DIR, exist_ok=True)
+
+@dataclass
+class ExApplicationInfo():
+    external_tensorboard_port: int
+
+exApplitionInfo = ExApplicationInfo(external_tensorboard_port=0)
+
+class MMVC_Rest_Trainer:
+    def __init__(self):
+        self.router = APIRouter()
+        self.router.add_api_route("/get_speakers", self.get_speakers, methods=["GET"])
+        self.router.add_api_route("/delete_speaker", self.delete_speaker, methods=["DELETE"])
+        self.router.add_api_route("/get_speaker_voices", self.get_speaker_voices, methods=["GET"])
+        self.router.add_api_route("/get_speaker_voice", self.get_speaker_voice, methods=["GET"])
+        self.router.add_api_route("/get_multi_speaker_setting", self.get_multi_speaker_setting, methods=["GET"])
+        self.router.add_api_route("/post_multi_speaker_setting", self.post_multi_speaker_setting, methods=["POST"])
+        self.router.add_api_route("/get_models", self.get_models, methods=["GET"])
+        self.router.add_api_route("/get_model", self.get_model, methods=["GET"])
+        self.router.add_api_route("/delete_model", self.delete_model, methods=["DELETE"])
+        self.router.add_api_route("/post_pre_training", self.post_pre_training, methods=["POST"])
+        self.router.add_api_route("/post_start_training", self.post_start_training, methods=["POST"])
+        self.router.add_api_route("/post_stop_training", self.post_stop_training, methods=["POST"])
+        self.router.add_api_route("/get_related_files", self.get_related_files, methods=["GET"])
+        self.router.add_api_route("/get_tail_training_log", self.get_tail_training_log, methods=["GET"])
+        self.router.add_api_route("/get_ex_application_info", self.get_ex_application_info, methods=["GET"])
+
+    def get_speakers(self):
+        return mod_get_speakers()
+
+    def delete_speaker(self, speaker: str = Form(...)):
+        return mod_delete_speaker(speaker)
+
+    def get_speaker_voices(self, speaker: str):
+        return mod_get_speaker_voices(speaker)
+
+    def get_speaker_voice(self, speaker: str, voice: str):
+        return mod_get_speaker_voice(speaker, voice)
+
+    def get_multi_speaker_setting(self):
+        return mod_get_multi_speaker_setting()
+
+    def post_multi_speaker_setting(self, setting: str = Form(...)):
+        return mod_post_multi_speaker_setting(setting)
+
+    def get_models(self):
+        return mod_get_models()
+
+    def get_model(self, model: str):
+        return mod_get_model(model)
+
+    def delete_model(self, model: str = Form(...)):
+        return mod_delete_model(model)
+
+    def post_pre_training(self, batch: int = Form(...)):
+        return mod_post_pre_training(batch)
+
+    def post_start_training(self, enable_finetuning: bool = Form(...),GModel: str = Form(...),DModel: str = Form(...)):
+        print("POST START TRAINING..")
+        return mod_post_start_training(enable_finetuning, GModel, DModel)
+
+    def post_stop_training(self):
+        print("POST STOP TRAINING..")
+        return mod_post_stop_training()
+
+    def get_related_files(self):
+        return mod_get_related_files()
+
+    def get_tail_training_log(self, num: int):
+        return mod_get_tail_training_log(num)
+
+    def get_ex_application_info(self):
+        json_compatible_item_data = jsonable_encoder(exApplitionInfo)
+        return JSONResponse(content=json_compatible_item_data)

server/restapi/mods/FileUploader.py

@@ -0,0 +1,27 @@
+import os, shutil
+from fastapi import UploadFile
+
+# UPLOAD_DIR = "model_upload_dir"
+
+def upload_file(upload_dirname:str, file:UploadFile, filename: str):
+    if file and filename:
+        fileobj = file.file
+        upload_dir = open(os.path.join(upload_dirname, filename),'wb+')
+        shutil.copyfileobj(fileobj, upload_dir)
+        upload_dir.close()
+        return {"uploaded files": f"{filename} "}
+    return {"Error": "uploaded file is not found."}
+
+def concat_file_chunks(upload_dirname:str, filename:str, chunkNum:int, dest_dirname:str):
+    target_file_name = os.path.join(dest_dirname, filename)
+    with open(target_file_name, "ab") as target_file:
+        for i in range(chunkNum):
+            chunkName = f"{filename}_{i}"
+            chunk_file_path = os.path.join(upload_dirname, chunkName)
+            stored_chunk_file = open(chunk_file_path, 'rb')
+            target_file.write(stored_chunk_file.read())
+            stored_chunk_file.close()
+            os.unlink(chunk_file_path)
+        target_file.close()
+    return target_file_name
+

server/trainer_mods/files.py

@@ -0,0 +1,24 @@
+import os,glob
+
+
+def get_file_list(top_dir):
+    for root, dirs, files in os.walk(top_dir):
+        for dir in dirs:
+            dirPath = os.path.join(root, dir)
+            print(f'dirPath = {dirPath}')
+
+        for file in files:
+            filePath = os.path.join(root, file)
+            print(f'filePath = {filePath}')
+
+
+def get_dir_list(top_dir):
+    dirlist = []
+    files = os.listdir(top_dir)
+    for filename in files:
+        if os.path.isdir(os.path.join(top_dir, filename)):
+            dirlist.append(filename)
+    return dirlist
+
+def get_file_list(top_dir):
+    return glob.glob(top_dir)