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wataru
parent
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commit
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280
demo/MMVCServerSIO.py
Executable file
280
demo/MMVCServerSIO.py
Executable file
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import sys, os, struct, argparse, logging, shutil, base64, traceback
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sys.path.append("/MMVC_Trainer")
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sys.path.append("/MMVC_Trainer/text")
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import uvicorn
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from fastapi import FastAPI, UploadFile, File, Form
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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from fastapi.encoders import jsonable_encoder
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from pydantic import BaseModel
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from scipy.io.wavfile import write, read
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import socketio
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from distutils.util import strtobool
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from datetime import datetime
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import torch
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import numpy as np
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from mods.ssl import create_self_signed_cert
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from mods.VoiceChanger import VoiceChanger
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class UvicornSuppressFilter(logging.Filter):
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def filter(self, record):
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return False
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logger = logging.getLogger("uvicorn.error")
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logger.addFilter(UvicornSuppressFilter())
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# logger.propagate = False
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logger = logging.getLogger("multipart.multipart")
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logger.propagate = False
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class VoiceModel(BaseModel):
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gpu: int
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srcId: int
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dstId: int
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timestamp: int
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buffer: str
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class MyCustomNamespace(socketio.AsyncNamespace):
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def __init__(self, namespace):
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super().__init__(namespace)
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def loadModel(self, config, model):
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if hasattr(self, 'voiceChanger') == True:
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self.voiceChanger.destroy()
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self.voiceChanger = VoiceChanger(config, model)
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def changeVoice(self, gpu, srcId, dstId, timestamp, unpackedData):
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return self.voiceChanger.on_request(gpu, srcId, dstId, timestamp, unpackedData)
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def on_connect(self, sid, environ):
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# print('[{}] connet sid : {}'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S') , sid))
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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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data = msg[4]
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# print(srcId, dstId, timestamp)
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unpackedData = np.array(struct.unpack('<%sh'%(len(data) // struct.calcsize('<h') ), data))
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audio1 = self.changeVoice(gpu, srcId, dstId, timestamp, unpackedData)
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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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pass;
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def setupArgParser():
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parser = argparse.ArgumentParser()
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parser.add_argument("-p", type=int, default=8080, help="port")
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parser.add_argument("-c", type=str, help="path for the config.json")
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parser.add_argument("-m", type=str, help="path for the model file")
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parser.add_argument("--https", type=strtobool, default=False, help="use https")
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parser.add_argument("--httpsKey", type=str, default="ssl.key", help="path for the key of https")
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parser.add_argument("--httpsCert", type=str, default="ssl.cert", help="path for the cert of https")
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parser.add_argument("--httpsSelfSigned", type=strtobool, default=True, help="generate self-signed certificate")
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return parser
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def printMessage(message, level=0):
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if level == 0:
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print(f"\033[17m{message}\033[0m")
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elif level == 1:
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print(f"\033[34m {message}\033[0m")
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elif level == 2:
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print(f"\033[32m {message}\033[0m")
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else:
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print(f"\033[47m {message}\033[0m")
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global app_socketio
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printMessage(f"Phase name:{__name__}", level=2)
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thisFilename = os.path.basename(__file__)[:-3]
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if __name__ == thisFilename:
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printMessage(f"PHASE3:{__name__}", level=2)
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parser = setupArgParser()
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args = parser.parse_args()
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PORT = args.p
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CONFIG = args.c
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MODEL = args.m
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app_fastapi = FastAPI()
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sio = socketio.AsyncServer(
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async_mode='asgi',
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cors_allowed_origins='*'
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)
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namespace = MyCustomNamespace('/test')
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sio.register_namespace(namespace)
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if CONFIG and MODEL:
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namespace.loadModel(CONFIG, MODEL)
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app_socketio = socketio.ASGIApp(
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sio,
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other_asgi_app=app_fastapi,
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static_files={
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'': '../frontend/dist',
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'/': '../frontend/dist/index.html',
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}
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)
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@app_fastapi.get("/api/hello")
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async def index():
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return {"result": "Index"}
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@app_fastapi.post("/api/uploadfile/model")
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async def upload_file(configFile:UploadFile = File(...), modelFile: UploadFile = File(...)):
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if configFile and modelFile:
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for file in [modelFile, configFile]:
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filename = file.filename
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fileobj = file.file
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upload_dir = open(os.path.join(".", filename),'wb+')
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shutil.copyfileobj(fileobj, upload_dir)
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upload_dir.close()
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namespace.loadModel(configFile.filename, modelFile.filename)
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return {"uploaded files": f"{configFile.filename}, {modelFile.filename} "}
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return {"Error": "uploaded file is not found."}
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@app_fastapi.post("/test")
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async def post_test(voice:VoiceModel):
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try:
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# print("POST REQUEST PROCESSING....")
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gpu = voice.gpu
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srcId = voice.srcId
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dstId = voice.dstId
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timestamp = voice.timestamp
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buffer = voice.buffer
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wav = base64.b64decode(buffer)
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if wav==0:
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samplerate, data=read("dummy.wav")
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unpackedData = data
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else:
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unpackedData = np.array(struct.unpack('<%sh'%(len(wav) // struct.calcsize('<h') ), wav))
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write("logs/received_data.wav", 24000, unpackedData.astype(np.int16))
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changedVoice = namespace.changeVoice(gpu, srcId, dstId, timestamp, unpackedData)
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changedVoiceBase64 = base64.b64encode(changedVoice).decode('utf-8')
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data = {
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"gpu":gpu,
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"srcId":srcId,
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"dstId":dstId,
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"timestamp":timestamp,
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"changedVoiceBase64":changedVoiceBase64
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}
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json_compatible_item_data = jsonable_encoder(data)
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return JSONResponse(content=json_compatible_item_data)
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except Exception as e:
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print("REQUEST PROCESSING!!!! EXCEPTION!!!", e)
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print(traceback.format_exc())
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return str(e)
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if __name__ == '__mp_main__':
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printMessage(f"PHASE2:{__name__}", level=2)
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if __name__ == '__main__':
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printMessage(f"PHASE1:{__name__}", level=2)
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parser = setupArgParser()
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args = parser.parse_args()
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PORT = args.p
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CONFIG = args.c
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MODEL = args.m
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printMessage(f"Start MMVC SocketIO Server", level=0)
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printMessage(f"CONFIG:{CONFIG}, MODEL:{MODEL}", level=1)
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if os.environ["EX_PORT"]:
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EX_PORT = os.environ["EX_PORT"]
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printMessage(f"External_Port:{EX_PORT} Internal_Port:{PORT}", level=1)
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else:
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printMessage(f"Internal_Port:{PORT}", level=1)
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if os.environ["EX_IP"]:
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EX_IP = os.environ["EX_IP"]
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printMessage(f"External_IP:{EX_IP}", level=1)
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# HTTPS key/cert作成
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if args.https and args.httpsSelfSigned == 1:
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# HTTPS(おれおれ証明書生成)
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os.makedirs("./key", exist_ok=True)
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key_base_name = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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keyname = f"{key_base_name}.key"
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certname = f"{key_base_name}.cert"
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create_self_signed_cert(certname, keyname, certargs=
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{"Country": "JP",
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"State": "Tokyo",
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"City": "Chuo-ku",
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"Organization": "F",
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"Org. Unit": "F"}, cert_dir="./key")
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key_path = os.path.join("./key", keyname)
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cert_path = os.path.join("./key", certname)
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printMessage(f"protocol: HTTPS(self-signed), key:{key_path}, cert:{cert_path}", level=1)
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elif args.https and args.httpsSelfSigned == 0:
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# HTTPS
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key_path = args.httpsKey
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cert_path = args.httpsCert
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printMessage(f"protocol: HTTPS, key:{key_path}, cert:{cert_path}", level=1)
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else:
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# HTTP
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printMessage(f"protocol: HTTP", level=1)
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# アドレス表示
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if args.https == 1:
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printMessage(f"open https://<IP>:<PORT>/ with your browser.", level=0)
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else:
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printMessage(f"open http://<IP>:<PORT>/ with your browser.", level=0)
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if EX_PORT and EX_IP and args.https == 1:
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printMessage(f"In many cases it is one of the following", level=1)
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printMessage(f"https://localhost:{EX_PORT}/", level=1)
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for ip in EX_IP.strip().split(" "):
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printMessage(f"https://{ip}:{EX_PORT}/", level=1)
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elif EX_PORT and EX_IP and args.https == 0:
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printMessage(f"In many cases it is one of the following", level=1)
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printMessage(f"http://localhost:{EX_PORT}/", level=1)
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# サーバ起動
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if args.https:
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# HTTPS サーバ起動
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uvicorn.run(
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f"{os.path.basename(__file__)[:-3]}:app_socketio",
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host="0.0.0.0",
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port=int(PORT),
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reload=True,
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ssl_keyfile = key_path,
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ssl_certfile = cert_path,
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log_level="critical"
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)
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else:
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# HTTP サーバ起動
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uvicorn.run(
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f"{os.path.basename(__file__)[:-3]}:app_socketio",
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host="0.0.0.0",
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port=int(PORT),
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reload=True,
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log_level="critical"
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)
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76
demo/mods/VoiceChanger.py
Executable file
76
demo/mods/VoiceChanger.py
Executable file
@ -0,0 +1,76 @@
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import torch
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from scipy.io.wavfile import write, read
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import numpy as np
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import struct, traceback
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import utils
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import commons
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from models import SynthesizerTrn
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from text.symbols import symbols
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from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
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from mel_processing import spectrogram_torch
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from text import text_to_sequence, cleaned_text_to_sequence
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class VoiceChanger():
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def __init__(self, config, model):
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self.hps = utils.get_hparams_from_file(config)
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self.net_g = SynthesizerTrn(
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len(symbols),
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model)
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self.net_g.eval()
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self.gpu_num = torch.cuda.device_count()
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utils.load_checkpoint( model, self.net_g, None)
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print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num})")
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def destroy(self):
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del self.net_g
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def on_request(self, gpu, srcId, dstId, timestamp, wav):
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# if wav==0:
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# samplerate, data=read("dummy.wav")
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# unpackedData = data
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# else:
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# unpackedData = np.array(struct.unpack('<%sh'%(len(wav) // struct.calcsize('<h') ), wav))
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# write("logs/received_data.wav", 24000, unpackedData.astype(np.int16))
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unpackedData = wav
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try:
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text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = torch.LongTensor(text_norm)
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audio = torch.FloatTensor(unpackedData.astype(np.float32))
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audio_norm = audio /self.hps.data.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
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self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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sid = torch.LongTensor([int(srcId)])
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data = (text_norm, spec, audio_norm, sid)
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data = TextAudioSpeakerCollate()([data])
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if 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([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).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(gpu) for x in data]
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sid_tgt1 = torch.LongTensor([dstId]).cuda(gpu)
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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()
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except Exception as e:
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print("VC PROCESSING!!!! EXCEPTION!!!", e)
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print(traceback.format_exc())
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audio1 = audio1.astype(np.int16)
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return audio1
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24
demo/mods/ssl.py
Executable file
24
demo/mods/ssl.py
Executable file
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import os
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from OpenSSL import crypto
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def create_self_signed_cert(certfile, keyfile, certargs, cert_dir="."):
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C_F = os.path.join(cert_dir, certfile)
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K_F = os.path.join(cert_dir, keyfile)
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if not os.path.exists(C_F) or not os.path.exists(K_F):
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k = crypto.PKey()
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k.generate_key(crypto.TYPE_RSA, 2048)
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cert = crypto.X509()
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cert.get_subject().C = certargs["Country"]
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cert.get_subject().ST = certargs["State"]
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cert.get_subject().L = certargs["City"]
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cert.get_subject().O = certargs["Organization"]
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cert.get_subject().OU = certargs["Org. Unit"]
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cert.get_subject().CN = 'Example'
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cert.set_serial_number(1000)
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cert.gmtime_adj_notBefore(0)
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cert.gmtime_adj_notAfter(315360000)
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cert.set_issuer(cert.get_subject())
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cert.set_pubkey(k)
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cert.sign(k, 'sha1')
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open(C_F, "wb").write(crypto.dump_certificate(crypto.FILETYPE_PEM, cert))
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open(K_F, "wb").write(crypto.dump_privatekey(crypto.FILETYPE_PEM, k))
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@ -22,7 +22,12 @@ from mel_processing import spectrogram_torch
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from text import text_to_sequence, cleaned_text_to_sequence
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class MyCustomNamespace(socketio.Namespace):
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def __init__(self, namespace, config, model):
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def __init__(self, namespace):
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super().__init__(namespace)
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self.gpu_num = torch.cuda.device_count()
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print("GPU_NUM:",self.gpu_num)
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def __init__old(self, namespace, config, model):
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super().__init__(namespace)
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self.hps =utils.get_hparams_from_file(config)
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self.net_g = SynthesizerTrn(
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@ -36,12 +41,37 @@ class MyCustomNamespace(socketio.Namespace):
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print("GPU_NUM:",self.gpu_num)
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utils.load_checkpoint( model, self.net_g, None)
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def loadModel(self, config, model):
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self.hps =utils.get_hparams_from_file(config)
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print("before DELETE:", torch.cuda.memory_allocated())
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if hasattr(self, 'net_g') == True:
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print("DELETE MODEL:", torch.cuda.memory_allocated())
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del self.net_g
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print("before load", torch.cuda.memory_allocated())
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self.net_g = SynthesizerTrn(
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len(symbols),
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model)
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self.net_g.eval()
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utils.load_checkpoint( model, self.net_g, None)
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print(torch.cuda.memory_allocated())
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print("after load", torch.cuda.memory_allocated())
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def on_connect(self, sid, environ):
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print('[{}] connet sid : {}'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S') , sid))
|
||||
# print('[{}] connet env : {}'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S') , environ))
|
||||
|
||||
def on_load_model(self, sid, msg):
|
||||
print("on_load_model")
|
||||
print(msg)
|
||||
pass
|
||||
|
||||
def on_request_message(self, sid, msg):
|
||||
# print("MESSGaa", msg)
|
||||
print("on_request_message", torch.cuda.memory_allocated())
|
||||
gpu = int(msg[0])
|
||||
srcId = int(msg[1])
|
||||
dstId = int(msg[2])
|
||||
@ -223,7 +253,17 @@ if __name__ == '__main__':
|
||||
|
||||
# SocketIOセットアップ
|
||||
sio = socketio.Server(cors_allowed_origins='*')
|
||||
sio.register_namespace(MyCustomNamespace('/test', CONFIG, MODEL))
|
||||
namespace = MyCustomNamespace('/test')
|
||||
sio.register_namespace(namespace)
|
||||
print("loadmodel1:")
|
||||
namespace.loadModel(CONFIG, MODEL)
|
||||
print("loadmodel2:")
|
||||
namespace.loadModel(CONFIG, MODEL)
|
||||
print("loadmodel3:")
|
||||
namespace.loadModel(CONFIG, MODEL)
|
||||
print("loadmodel4:")
|
||||
namespace.loadModel(CONFIG, MODEL)
|
||||
print("loadmodel5:")
|
||||
app = socketio.WSGIApp(sio,static_files={
|
||||
'': '../frontend/dist',
|
||||
'/': '../frontend/dist/index.html',
|
||||
|
||||
@ -11,33 +11,18 @@ echo $PARAMS
|
||||
if [[ -e ./setting.json ]]; then
|
||||
echo "カスタムセッティングを使用"
|
||||
cp ./setting.json ../frontend/dist/assets/setting.json
|
||||
else
|
||||
if [ "${TYPE}" = "SOFT_VC" ] ; then
|
||||
cp ../frontend/dist/assets/setting_softvc.json ../frontend/dist/assets/setting.json
|
||||
elif [ "${TYPE}" = "SOFT_VC_FAST_API" ] ; then
|
||||
cp ../frontend/dist/assets/setting_softvc_colab.json ../frontend/dist/assets/setting.json
|
||||
else
|
||||
cp ../frontend/dist/assets/setting_mmvc.json ../frontend/dist/assets/setting.json
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
# 起動
|
||||
if [ "${TYPE}" = "SOFT_VC" ] ; then
|
||||
echo "SOFT_VCを起動します"
|
||||
python3 SoftVcServerSIO.py $PARAMS 2>stderr.txt
|
||||
elif [ "${TYPE}" = "SOFT_VC_VERBOSE" ] ; then
|
||||
echo "SOFT_VCを起動します(verbose)"
|
||||
python3 SoftVcServerSIO.py $PARAMS
|
||||
elif [ "${TYPE}" = "SOFT_VC_FAST_API" ] ; then
|
||||
echo "SOFT_VC_FAST_APIを起動します"
|
||||
python3 SoftVcServerFastAPI.py 8080 docker
|
||||
elif [ "${TYPE}" = "MMVC" ] ; then
|
||||
if [ "${TYPE}" = "MMVC" ] ; then
|
||||
echo "MMVCを起動します"
|
||||
python3 serverSIO.py $PARAMS 2>stderr.txt
|
||||
python3 MMVCServerSIO.py $PARAMS 2>stderr.txt
|
||||
elif [ "${TYPE}" = "MMVC_VERBOSE" ] ; then
|
||||
echo "MMVCを起動します(verbose)"
|
||||
python3 serverSIO.py $PARAMS
|
||||
python3 MMVCServerSIO.py $PARAMS
|
||||
fi
|
||||
|
||||
|
||||
|
||||
2
frontend/dist/index.js
vendored
2
frontend/dist/index.js
vendored
File diff suppressed because one or more lines are too long
@ -1,7 +1,7 @@
|
||||
#!/bin/bash
|
||||
set -eu
|
||||
|
||||
DOCKER_IMAGE=dannadori/voice-changer:20221028_220714
|
||||
DOCKER_IMAGE=dannadori/voice-changer:20221029_233016
|
||||
#DOCKER_IMAGE=voice-changer
|
||||
|
||||
|
||||
|
||||
314
start_v0.1.sh
Normal file
314
start_v0.1.sh
Normal file
@ -0,0 +1,314 @@
|
||||
#!/bin/bash
|
||||
set -eu
|
||||
|
||||
DOCKER_IMAGE=dannadori/voice-changer:20221028_220714
|
||||
#DOCKER_IMAGE=voice-changer
|
||||
|
||||
|
||||
MODE=$1
|
||||
PARAMS=${@:2:($#-1)}
|
||||
|
||||
### DEFAULT VAR ###
|
||||
DEFAULT_EX_PORT=18888
|
||||
DEFAULT_USE_GPU=on # on|off
|
||||
DEFAULT_VERBOSE=off # on|off
|
||||
|
||||
### ENV VAR ###
|
||||
EX_PORT=${EX_PORT:-${DEFAULT_EX_PORT}}
|
||||
USE_GPU=${USE_GPU:-${DEFAULT_USE_GPU}}
|
||||
VERBOSE=${VERBOSE:-${DEFAULT_VERBOSE}}
|
||||
|
||||
#echo $EX_PORT $USE_GPU $VERBOSE
|
||||
|
||||
### INTERNAL SETTING ###
|
||||
TENSORBOARD_PORT=6006
|
||||
SIO_PORT=8080
|
||||
|
||||
|
||||
###
|
||||
if [ "${MODE}" = "MMVC_TRAIN" ]; then
|
||||
echo "トレーニングを開始します"
|
||||
|
||||
docker run -it --gpus all --shm-size=128M \
|
||||
-v `pwd`/exp/${name}/dataset:/MMVC_Trainer/dataset \
|
||||
-v `pwd`/exp/${name}/logs:/MMVC_Trainer/logs \
|
||||
-v `pwd`/exp/${name}/filelists:/MMVC_Trainer/filelists \
|
||||
-v `pwd`/vc_resources:/resources \
|
||||
-e LOCAL_UID=$(id -u $USER) \
|
||||
-e LOCAL_GID=$(id -g $USER) \
|
||||
-e EX_IP="`hostname -I`" \
|
||||
-e EX_PORT=${EX_PORT} \
|
||||
-e VERBOSE=${VERBOSE} \
|
||||
-p ${EX_PORT}:6006 $DOCKER_IMAGE "$@"
|
||||
|
||||
elif [ "${MODE}" = "MMVC" ]; then
|
||||
if [ "${USE_GPU}" = "on" ]; then
|
||||
echo "MMVCを起動します(with gpu)"
|
||||
|
||||
docker run -it --gpus all --shm-size=128M \
|
||||
-v `pwd`/vc_resources:/resources \
|
||||
-e LOCAL_UID=$(id -u $USER) \
|
||||
-e LOCAL_GID=$(id -g $USER) \
|
||||
-e EX_IP="`hostname -I`" \
|
||||
-e EX_PORT=${EX_PORT} \
|
||||
-e VERBOSE=${VERBOSE} \
|
||||
-p ${EX_PORT}:8080 $DOCKER_IMAGE "$@"
|
||||
else
|
||||
echo "MMVCを起動します(only cpu)"
|
||||
docker run -it --shm-size=128M \
|
||||
-v `pwd`/vc_resources:/resources \
|
||||
-e LOCAL_UID=$(id -u $USER) \
|
||||
-e LOCAL_GID=$(id -g $USER) \
|
||||
-e EX_IP="`hostname -I`" \
|
||||
-e EX_PORT=${EX_PORT} \
|
||||
-e VERBOSE=${VERBOSE} \
|
||||
-p ${EX_PORT}:8080 $DOCKER_IMAGE "$@"
|
||||
|
||||
# docker run -it --shm-size=128M \
|
||||
# -v `pwd`/vc_resources:/resources \
|
||||
# -e LOCAL_UID=$(id -u $USER) \
|
||||
# -e LOCAL_GID=$(id -g $USER) \
|
||||
# -e EX_IP="`hostname -I`" \
|
||||
# -e EX_PORT=${EX_PORT} \
|
||||
# -e VERBOSE=${VERBOSE} \
|
||||
# --entrypoint="" \
|
||||
# -p ${EX_PORT}:8080 $DOCKER_IMAGE /bin/bash
|
||||
|
||||
fi
|
||||
|
||||
elif [ "${MODE}" = "SOFT_VC" ]; then
|
||||
if [ "${USE_GPU}" = "on" ]; then
|
||||
echo "Start Soft-vc"
|
||||
|
||||
docker run -it --gpus all --shm-size=128M \
|
||||
-v `pwd`/vc_resources:/resources \
|
||||
-e LOCAL_UID=$(id -u $USER) \
|
||||
-e LOCAL_GID=$(id -g $USER) \
|
||||
-e EX_IP="`hostname -I`" \
|
||||
-e EX_PORT=${EX_PORT} \
|
||||
-e VERBOSE=${VERBOSE} \
|
||||
-p ${EX_PORT}:8080 $DOCKER_IMAGE "$@"
|
||||
else
|
||||
echo "Start Soft-vc withou GPU is not supported"
|
||||
fi
|
||||
|
||||
else
|
||||
echo "
|
||||
usage:
|
||||
$0 <MODE> <params...>
|
||||
MODE: select one of ['MMVC_TRAIN', 'MMVC', 'SOFT_VC']
|
||||
" >&2
|
||||
fi
|
||||
|
||||
|
||||
|
||||
|
||||
# echo $EX_PORT
|
||||
|
||||
|
||||
# echo "------"
|
||||
# echo "$@"
|
||||
# echo "------"
|
||||
|
||||
# # usage() {
|
||||
# # echo "
|
||||
# # usage:
|
||||
# # For training
|
||||
# # $0 [-t] -n <exp_name> [-b batch_size] [-r]
|
||||
# # -t: トレーニングモードで実行する場合に指定してください。(train)
|
||||
# # -n: トレーニングの名前です。(name)
|
||||
# # -b: バッチサイズです。(batchsize)
|
||||
# # -r: トレーニング再開の場合に指定してください。(resume)
|
||||
# # For changing voice
|
||||
# # $0 [-v] [-c config] [-m model] [-g on/off]
|
||||
# # -v: ボイスチェンジャーモードで実行する場合に指定してください。(voice changer)
|
||||
# # -c: トレーニングで使用したConfigのファイル名です。(config)
|
||||
# # -m: トレーニング済みのモデルのファイル名です。(model)
|
||||
# # -g: GPU使用/不使用。デフォルトはonなのでGPUを使う場合は指定不要。(gpu)
|
||||
# # -p: port番号
|
||||
# # For help
|
||||
# # $0 [-h]
|
||||
# # -h: show this help
|
||||
# # " >&2
|
||||
# # }
|
||||
# # warn () {
|
||||
# # echo "! ! ! $1 ! ! !"
|
||||
# # exit 1
|
||||
# # }
|
||||
|
||||
|
||||
# # training_flag=false
|
||||
# # name=999_exp
|
||||
# # batch_size=10
|
||||
# # resume_flag=false
|
||||
|
||||
# # voice_change_flag=false
|
||||
# # config=
|
||||
# # model=
|
||||
# # gpu=on
|
||||
# # port=8080
|
||||
# # escape_flag=false
|
||||
|
||||
# # # オプション解析
|
||||
# # while getopts tn:b:rvc:m:g:p:hx OPT; do
|
||||
# # case $OPT in
|
||||
# # t)
|
||||
# # training_flag=true
|
||||
# # ;;
|
||||
# # n)
|
||||
# # name="$OPTARG"
|
||||
# # ;;
|
||||
# # b)
|
||||
# # batch_size="$OPTARG"
|
||||
# # ;;
|
||||
# # r)
|
||||
# # resume_flag=true
|
||||
# # ;;
|
||||
# # v)
|
||||
# # voice_change_flag=true
|
||||
# # ;;
|
||||
# # c)
|
||||
# # config="$OPTARG"
|
||||
# # ;;
|
||||
# # m)
|
||||
# # model="$OPTARG"
|
||||
# # ;;
|
||||
# # g)
|
||||
# # gpu="$OPTARG"
|
||||
# # ;;
|
||||
# # p)
|
||||
# # port="$OPTARG"
|
||||
# # ;;
|
||||
# # h | \?)
|
||||
# # usage && exit 1
|
||||
# # ;;
|
||||
# # x)
|
||||
# # escape_flag=true
|
||||
# # esac
|
||||
# # done
|
||||
|
||||
|
||||
# # # モード解析
|
||||
# # if $training_flag && $voice_change_flag; then
|
||||
# # warn "-t（トレーニングモード） と -v（ボイチェンモード）は同時に指定できません。"
|
||||
# # elif $training_flag; then
|
||||
# # echo "■■■ ト レ ー ニ ン グ モ ー ド ■■■"
|
||||
# # elif $voice_change_flag; then
|
||||
# # echo "■■■ ボ イ チ ェ ン モ ー ド ■■■"
|
||||
# # elif $escape_flag; then
|
||||
# # /bin/bash
|
||||
# # else
|
||||
# # warn "-t（トレーニングモード） と -v（ボイチェンモード）のいずれかを指定してください。"
|
||||
# # fi
|
||||
|
||||
# if [ "${MODE}" = "MMVC_TRAIN_INITIAL" ]; then
|
||||
# echo "トレーニングを開始します"
|
||||
# elif [ "${MODE}" = "MMVC" ]; then
|
||||
# echo "MMVCを起動します"
|
||||
|
||||
# docker run -it --gpus all --shm-size=128M \
|
||||
# -v `pwd`/vc_resources:/resources \
|
||||
# -e LOCAL_UID=$(id -u $USER) \
|
||||
# -e LOCAL_GID=$(id -g $USER) \
|
||||
# -e EX_IP="`hostname -I`" \
|
||||
# -e EX_PORT=${port} \
|
||||
# -p ${port}:8080 $DOCKER_IMAGE -v -c ${config} -m ${model}
|
||||
|
||||
# elif [ "${MODE}" = "MMVC_VERBOSE" ]; then
|
||||
# echo "MMVCを起動します(verbose)"
|
||||
# elif [ "${MODE}" = "MMVC_CPU" ]; then
|
||||
# echo "MMVCを起動します(CPU)"
|
||||
# elif [ "${MODE}" = "MMVC_CPU_VERBOSE" ]; then
|
||||
# echo "MMVCを起動します(CPU)(verbose)"
|
||||
# elif [ "${MODE}" = "SOFT_VC" ]; then
|
||||
# echo "Start Soft-vc"
|
||||
# elif [ "${MODE}" = "SOFT_VC_VERBOSE" ]; then
|
||||
# echo "Start Soft-vc(verbose)"
|
||||
# else
|
||||
# echo "
|
||||
# usage:
|
||||
# $0 <MODE> <params...>
|
||||
# EX_PORT:
|
||||
# MODE: one of ['MMVC_TRAIN', 'MMVC', 'SOFT_VC']
|
||||
|
||||
# For 'MMVC_TRAIN':
|
||||
# $0 MMVC_TRAIN_INITIAL -n <exp_name> [-b batch_size] [-r]
|
||||
# -n: トレーニングの名前です。(name)
|
||||
# -b: バッチサイズです。(batchsize)
|
||||
# -r: トレーニング再開の場合に指定してください。(resume)
|
||||
# For 'MMVC'
|
||||
# $0 MMVC [-c config] [-m model] [-g on/off] [-p port] [-v]
|
||||
# -c: トレーニングで使用したConfigのファイル名です。(config)
|
||||
# -m: トレーニング済みのモデルのファイル名です。(model)
|
||||
# -g: GPU使用/不使用。デフォルトはonなのでGPUを使う場合は指定不要。(gpu)
|
||||
# -p: Docker からExposeするport番号
|
||||
# -v: verbose
|
||||
# For 'SOFT_VC'
|
||||
# $0 SOFT_VC [-c config] [-m model] [-g on/off]
|
||||
# -p: port exposed from docker container.
|
||||
# -v: verbose
|
||||
# " >&2
|
||||
# fi
|
||||
|
||||
|
||||
|
||||
# # if $training_flag; then
|
||||
# # if $resume_flag; then
|
||||
# # echo "トレーニングを再開します"
|
||||
# # docker run -it --gpus all --shm-size=128M \
|
||||
# # -v `pwd`/exp/${name}/dataset:/MMVC_Trainer/dataset \
|
||||
# # -v `pwd`/exp/${name}/logs:/MMVC_Trainer/logs \
|
||||
# # -v `pwd`/exp/${name}/filelists:/MMVC_Trainer/filelists \
|
||||
# # -v `pwd`/vc_resources:/resources \
|
||||
# # -e LOCAL_UID=$(id -u $USER) \
|
||||
# # -e LOCAL_GID=$(id -g $USER) \
|
||||
# # -p ${TENSORBOARD_PORT}:6006 $DOCKER_IMAGE -t -b ${batch_size} -r
|
||||
# # else
|
||||
# # echo "トレーニングを開始します"
|
||||
# # docker run -it --gpus all --shm-size=128M \
|
||||
# # -v `pwd`/exp/${name}/dataset:/MMVC_Trainer/dataset \
|
||||
# # -v `pwd`/exp/${name}/logs:/MMVC_Trainer/logs \
|
||||
# # -v `pwd`/exp/${name}/filelists:/MMVC_Trainer/filelists \
|
||||
# # -v `pwd`/vc_resources:/resources \
|
||||
# # -e LOCAL_UID=$(id -u $USER) \
|
||||
# # -e LOCAL_GID=$(id -g $USER) \
|
||||
# # -p ${TENSORBOARD_PORT}:6006 $DOCKER_IMAGE -t -b ${batch_size}
|
||||
# # fi
|
||||
# # fi
|
||||
|
||||
# # if $voice_change_flag; then
|
||||
# # if [[ -z "$config" ]]; then
|
||||
# # warn "コンフィグファイル(-c)を指定してください"
|
||||
# # fi
|
||||
# # if [[ -z "$model" ]]; then
|
||||
# # warn "モデルファイル(-m)を指定してください"
|
||||
# # fi
|
||||
# # if [ "${gpu}" = "on" ]; then
|
||||
# # echo "GPUをマウントして起動します。"
|
||||
|
||||
# # docker run -it --gpus all --shm-size=128M \
|
||||
# # -v `pwd`/vc_resources:/resources \
|
||||
# # -e LOCAL_UID=$(id -u $USER) \
|
||||
# # -e LOCAL_GID=$(id -g $USER) \
|
||||
# # -e EX_IP="`hostname -I`" \
|
||||
# # -e EX_PORT=${port} \
|
||||
# # -p ${port}:8080 $DOCKER_IMAGE -v -c ${config} -m ${model}
|
||||
# # elif [ "${gpu}" = "off" ]; then
|
||||
# # echo "CPUのみで稼働します。GPUは使用できません。"
|
||||
# # docker run -it --shm-size=128M \
|
||||
# # -v `pwd`/vc_resources:/resources \
|
||||
# # -e LOCAL_UID=$(id -u $USER) \
|
||||
# # -e LOCAL_GID=$(id -g $USER) \
|
||||
# # -e EX_IP="`hostname -I`" \
|
||||
# # -e EX_PORT=${port} \
|
||||
# # -p ${port}:8080 $DOCKER_IMAGE -v -c ${config} -m ${model}
|
||||
# # else
|
||||
# # echo ${gpu}
|
||||
# # warn "-g は onかoffで指定して下さい。"
|
||||
|
||||
# # fi
|
||||
|
||||
|
||||
# # fi
|
||||
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
FROM dannadori/voice-changer-internal:20221028_220538 as front
|
||||
FROM dannadori/voice-changer-internal:20221029_231527 as front
|
||||
FROM debian:bullseye-slim as base
|
||||
|
||||
ARG DEBIAN_FRONTEND=noninteractive
|
||||
@ -8,7 +8,7 @@ RUN apt-get install -y python3-pip git
|
||||
RUN apt-get install -y espeak
|
||||
RUN apt-get install -y cmake
|
||||
|
||||
RUN git clone --depth 1 https://github.com/isletennos/MMVC_Trainer.git -b v1.3.1.3
|
||||
#RUN git clone --depth 1 https://github.com/isletennos/MMVC_Trainer.git -b v1.3.1.3
|
||||
|
||||
RUN pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
|
||||
|
||||
@ -24,17 +24,20 @@ RUN pip install tqdm==4.64.0
|
||||
RUN pip install retry==0.9.2
|
||||
RUN pip install psutil==5.9.1
|
||||
RUN pip install python-socketio==5.7.1
|
||||
RUN pip install eventlet==0.33.1
|
||||
RUN pip install matplotlib==3.5.3
|
||||
|
||||
RUN pip install fastapi==0.85.0
|
||||
RUN pip install python-multipart==0.0.5
|
||||
RUN pip install uvicorn==0.18.3
|
||||
RUN pip install websockets==10.4
|
||||
RUN pip install pyOpenSSL==22.0.0
|
||||
|
||||
RUN pip install pyopenjtalk==0.2.0
|
||||
RUN pip install tensorboard==2.10.0
|
||||
RUN pip install matplotlib==3.5.3
|
||||
|
||||
RUN pip install pyOpenSSL==22.0.0
|
||||
|
||||
WORKDIR /MMVC_Trainer/monotonic_align
|
||||
RUN cythonize -3 -i core.pyx \
|
||||
&& mv core.cpython-39-x86_64-linux-gnu.so monotonic_align/
|
||||
# WORKDIR /MMVC_Trainer/monotonic_align
|
||||
# RUN cythonize -3 -i core.pyx \
|
||||
# && mv core.cpython-39-x86_64-linux-gnu.so monotonic_align/
|
||||
|
||||
|
||||
FROM debian:bullseye-slim
|
||||
@ -64,12 +67,11 @@ COPY --from=front --chmod=777 /voice-changer-internal/frontend/dist /voice-chang
|
||||
COPY --from=front --chmod=777 /voice-changer-internal/voice-change-service /voice-changer-internal/voice-change-service
|
||||
RUN chmod 0777 /voice-changer-internal/voice-change-service
|
||||
|
||||
##### Soft VC
|
||||
COPY --from=front /hubert /hubert
|
||||
COPY --from=front /acoustic-model /acoustic-model
|
||||
COPY --from=front /hifigan /hifigan
|
||||
|
||||
COPY --from=front /models /models
|
||||
# ##### Soft VC
|
||||
# COPY --from=front /hubert /hubert
|
||||
# COPY --from=front /acoustic-model /acoustic-model
|
||||
# COPY --from=front /hifigan /hifigan
|
||||
# COPY --from=front /models /models
|
||||
|
||||
|
||||
ENTRYPOINT ["/bin/bash", "setup.sh"]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user