update

demo/SoftVcServerFastAPI.py

@@ -0,0 +1,158 @@
+import uvicorn
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import JSONResponse
+from fastapi.staticfiles import StaticFiles
+
+import logging
+from logging.config import dictConfig
+import os, sys, math, base64, struct, traceback, time
+
+import torch, torchaudio
+import numpy as np
+from scipy.io.wavfile import write, read
+from datetime import datetime
+
+args = sys.argv
+PORT = args[1]
+MODE = args[2]
+
+
+logger = logging.getLogger('uvicorn')  
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.mount("/front", StaticFiles(directory="voice-changer/frontend/dist", html=True), name="static")
+
+if MODE == "colab":
+    print("ENV: colab")
+    
+    hubert_model = torch.hub.load("bshall/hubert:main", "hubert_soft").cuda()
+    acoustic_model = torch.hub.load("bshall/acoustic-model:main", "hubert_soft").cuda()
+    hifigan_model = torch.hub.load("bshall/hifigan:main", "hifigan_hubert_soft").cuda()
+else:    
+    print("ENV: Docker")
+
+    app.mount("/front", StaticFiles(directory="../frontend/dist", html=True), name="static")
+
+    sys.path.append("/hubert")
+    from hubert import hubert_discrete, hubert_soft, kmeans100
+
+    sys.path.append("/acoustic-model")
+    from acoustic import hubert_discrete, hubert_soft
+
+    sys.path.append("/hifigan")
+    from hifigan import hifigan
+
+    hubert_model = torch.load("/models/bshall_hubert_main.pt").cuda()
+    acoustic_model = torch.load("/models/bshall_acoustic-model_main.pt").cuda()
+    hifigan_model = torch.load("/models/bshall_hifigan_main.pt").cuda()
+
+
+def applyVol(i, chunk, vols):
+  curVol = vols[i] / 2
+  if curVol < 0.0001:
+    line = torch.zeros(chunk.size())
+  else:
+    line = torch.ones(chunk.size())
+
+  volApplied = torch.mul(line, chunk)  
+  volApplied = volApplied.unsqueeze(0)
+  return volApplied
+
+
+@app.get("/test")
+def get_test():
+    try:
+        return request.args.get('query', '')
+    except Exception as e:
+        print("REQUEST PROCESSING!!!! EXCEPTION!!!", e)
+        print(traceback.format_exc())
+        return str(e)
+
+
+
+class VoiceModel(BaseModel):
+    gpu: int
+    srcId: int
+    dstId: int
+    timestamp: int
+    buffer: str
+
+@app.post("/test")
+def post_test(voice:VoiceModel):
+    try:
+        print("POST REQUEST PROCESSING....")
+        gpu = voice.gpu
+        srcId = voice.srcId
+        dstId = voice.dstId
+        timestamp = voice.timestamp
+        buffer = voice.buffer
+        wav = base64.b64decode(buffer)
+        unpackedData = np.array(struct.unpack('<%sh'%(len(wav) // struct.calcsize('<h') ), wav))
+        # received_data_file = f"received_data_{timestamp}.wav"
+        received_data_file = "received_data.wav"
+        write(received_data_file, 24000, unpackedData.astype(np.int16))
+        source, sr = torchaudio.load(received_data_file) # デフォルトでnormalize=Trueがついており、float32に変換して読んでくれるらしいのでこれを使う。https://pytorch.org/audio/stable/backend.html
+
+        source_16k = torchaudio.functional.resample(source, 24000, 16000)
+        source_16k = source_16k.unsqueeze(0).cuda()
+        # SOFT-VC
+        with torch.inference_mode():
+            units = hubert_model.units(source_16k)
+            mel = acoustic_model.generate(units).transpose(1, 2)
+            target = hifigan_model(mel)
+
+        dest = torchaudio.functional.resample(target, 16000,24000)
+        dest = dest.squeeze().cpu()
+
+        # ソースの音量取得
+        source = source.cpu()
+        specgram = torchaudio.transforms.MelSpectrogram(sample_rate=24000)(source)
+        vol_apply_window_size = math.ceil(len(source[0]) / specgram.size()[2])
+        specgram = specgram.transpose(1,2)
+        vols = [ torch.max(i) for i in specgram[0]]
+        chunks = torch.split(dest, vol_apply_window_size,0)
+
+        chunks = [applyVol(i,c,vols) for i, c in enumerate(chunks)]
+        dest = torch.cat(chunks,1)
+        arr = np.array(dest.squeeze())
+
+        int_size = 2**(16 - 1) - 1
+        arr = (arr * int_size).astype(np.int16)
+        write("converted_data.wav", 24000, arr)
+        changedVoiceBase64 = base64.b64encode(arr).decode('utf-8')
+
+        data = {
+            "gpu":gpu,
+            "srcId":srcId,
+            "dstId":dstId,
+            "timestamp":timestamp,
+            "changedVoiceBase64":changedVoiceBase64
+        }
+
+        json_compatible_item_data = jsonable_encoder(data)
+
+        
+        return JSONResponse(content=json_compatible_item_data)
+    except Exception as e:
+        print("REQUEST PROCESSING!!!! EXCEPTION!!!", e)
+        print(traceback.format_exc())
+        return str(e)
+
+
+if __name__ == '__main__':
+    args = sys.argv
+    PORT = args[1]
+    MODE = args[2]
+    logger.info('INITIALIZE MODEL')
+    logger.info('START APP')
+    uvicorn.run(f"{os.path.basename(__file__)[:-3]}:app", host="0.0.0.0", port=int(PORT), reload=True, log_level="info")

demo/serverFastAPI.py

@@ -0,0 +1,144 @@
+from flask import Flask, request, Markup, abort, jsonify, send_from_directory
+from flask_cors import CORS
+import logging
+from logging.config import dictConfig
+import sys
+import base64
+
+import torch
+import numpy as np
+from scipy.io.wavfile import write, read
+from datetime import datetime
+
+import traceback
+import struct
+
+sys.path.append("mod")
+sys.path.append("mod/text")
+
+import utils
+from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
+from models import SynthesizerTrn
+from text.symbols import symbols
+
+dictConfig({
+    'version': 1,
+    'formatters': {'default': {
+        'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',
+    }},
+    'handlers': {'wsgi': {
+        'class': 'logging.StreamHandler',
+        'stream': 'ext://flask.logging.wsgi_errors_stream',
+        'formatter': 'default'
+    }},
+    'root': {
+        'level': 'INFO',
+        'handlers': ['wsgi']
+    }
+})
+
+app = Flask(__name__)
+@app.route("/<path:path>")
+def static_dir(path):
+    return send_from_directory("../frontend/dist", path)
+
+@app.route('/', methods=['GET'])
+def redirect_to_index():
+    return send_from_directory("../frontend/dist", 'index.html')
+
+CORS(app, resources={r"/*": {"origins": "*"}}) 
+
+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()
+        print("GPU_NUM:",self.gpu_num)
+        utils.load_checkpoint( model, self.net_g, None)
+
+
+    def on_request(self, gpu, srcId, dstId, timestamp, wav): 
+        if wav==0:
+            samplerate, data=read("dummy.wav")
+            unpackedData = data
+        else:
+            unpackedData = np.array(struct.unpack('<%sh'%(len(wav) // struct.calcsize('<h') ), wav))
+            write("logs/received_data.wav", 24000, unpackedData.astype(np.int16))
+
+        try:
+            if gpu<0 or self.gpu_num==0 :
+                with torch.no_grad():
+                    dataset = TextAudioSpeakerLoader("dummy.txt", self.hps.data, no_use_textfile=True)
+                    data = dataset.get_audio_text_speaker_pair([ unpackedData, srcId, "a"])
+                    data = TextAudioSpeakerCollate()([data])
+                    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()
+            else:
+                with torch.no_grad():
+                    dataset = TextAudioSpeakerLoader("dummy.txt", self.hps.data, no_use_textfile=True)
+                    data = dataset.get_audio_text_speaker_pair([ unpackedData, srcId, "a"])
+                    data = TextAudioSpeakerCollate()([data])
+                    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()
+        except Exception as e:
+            print("VC PROCESSING!!!! EXCEPTION!!!", e)
+            print(traceback.format_exc())
+        
+        audio1 = audio1.astype(np.int16)
+        return audio1
+
+
+
+@app.route('/test', methods=['GET', 'POST'])
+def test():
+    try:
+        if request.method == 'GET':
+            return request.args.get('query', '')
+        elif request.method == 'POST':
+            print("POST REQUEST PROCESSING....")
+            gpu = int(request.json['gpu'])
+            srcId = int(request.json['srcId'])
+            dstId = int(request.json['dstId'])
+            timestamp = int(request.json['timestamp'])
+            buffer = request.json['buffer']
+            wav = base64.b64decode(buffer)
+            # print(wav)
+            # print(base64.b64encode(wav))
+            changedVoice = voiceChanger.on_request(gpu, srcId, dstId, timestamp, wav)
+            changedVoiceBase64 = base64.b64encode(changedVoice).decode('utf-8')
+            # print("changedVoice",changedVoice)
+            # print("CV64",changedVoiceBase64)
+            data = {
+                "gpu":gpu,
+                "srcId":srcId,
+                "dstId":dstId,
+                "timestamp":timestamp,
+                "changedVoiceBase64":changedVoiceBase64
+            }
+            return jsonify(data)
+        else:
+            return abort(400)
+    except Exception as e:
+        print("REQUEST PROCESSING!!!! EXCEPTION!!!", e)
+        print(traceback.format_exc())
+        return str(e)
+
+
+if __name__ == '__main__':
+    args = sys.argv
+    PORT = args[1]
+    CONFIG = args[2]
+    MODEL  = args[3]
+    app.logger.info('INITIALIZE MODEL')
+    voiceChanger = VoiceChanger(CONFIG, MODEL)
+    voiceChanger.on_request(0,0,0,0,0)
+    app.logger.info('START APP')
+    app.run(debug=True, host='0.0.0.0',port=PORT)

demo/serverFlask.py

@@ -37,9 +37,6 @@ dictConfig({
     }
 })
 
-
-#app = Flask(__name__, static_folder="../frontend/dist", static_url_path='/')
-
 app = Flask(__name__)
 @app.route("/<path:path>")
 def static_dir(path):

demo/serverSIO.py

@@ -15,8 +15,6 @@ from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
 from models import SynthesizerTrn
 from text.symbols import symbols
 
-
-
 class MyCustomNamespace(socketio.Namespace): 
     def __init__(self, namespace, config, model):
         super().__init__(namespace)
@@ -91,6 +89,7 @@ if __name__ == '__main__':
     sio.register_namespace(MyCustomNamespace('/test', CONFIG, MODEL)) 
     app = socketio.WSGIApp(sio,static_files={
         '': '../frontend/dist',
+        '/': '../frontend/dist/index.html',
     }) 
     eventlet.wsgi.server(eventlet.listen(('0.0.0.0',int(PORT))), app) 
     

demo/setup.sh

@@ -1,13 +1,31 @@
 #!/bin/bash
 
-echo config: $1
+CONFIG=$1
-echo model: $2
+MODEL=$2
+TYPE=$3
+
+echo config: $CONFIG
+echo model: $MODEL
+echo type: $TYPE
+
+
+
 cp -r /resources/* .
 
 if [[ -e ./setting.json ]]; then
   cp ./setting.json ../frontend/dist/assets/setting.json
 fi
 
-python3 serverSIO.py 8080 $1 $2
+if [ "${TYPE}" = "SOFT_VC" ] ; then
+  echo "SOFT_VCを起動します"
+  python3 SoftVcServerFlask.py 8080
+elif [ "${TYPE}" = "SOFT_VC_FAST_API" ] ; then
+  echo "SOFT_VC_FAST_APIを起動します"
+  python3 SoftVcServerFastAPI.py 8080
+else
+  echo "MMVCを起動します"
+  python3 serverSIO.py 8080 $CONFIG $MODEL
+fi
+
 
 

frontend/dist/index.html

@@ -1 +1,13 @@
-<!doctype html><html lang="ja" style="width:100%;height:100%;overflow:hidden"><head><meta charset="utf-8"/><title>voice recorder</title><script defer="defer" src="index.js"></script></head><body style="width:100%;height:100%;margin:0"><div id="app" style="width:100%;height:100%"></div><noscript><strong>javascriptを有効にしてください</strong></noscript></body></html>
+<!DOCTYPE html>
+<html lang="ja" style="width: 100%; height: 100%; overflow: hidden">
+    <head>
+        <meta charset="utf-8" />
+        <title>voice recorder</title>
+    <script defer src="index.js"></script></head>
+    <body style="width: 100%; height: 100%; margin: 0px">
+        <div id="app" style="width: 100%; height: 100%"></div>
+        <noscript>
+            <strong>javascriptを有効にしてください</strong>
+        </noscript>
+    </body>
+</html>

start2.sh

@@ -2,9 +2,9 @@
 
 # 参考:https://programwiz.org/2022/03/22/how-to-write-shell-script-for-option-parsing/
 
-DOCKER_IMAGE=dannadori/voice-changer:20220903_150931
+DOCKER_IMAGE=dannadori/voice-changer:20220918_220447
 TENSORBOARD_PORT=6006
-VOICE_CHANGER_PORT=8080
+VOICE_CHANGER_PORT=8081
 
 set -eu
 

template/setting_colab_softvc.json

@@ -0,0 +1,38 @@
+{
+    "app_title": "voice-changer",
+    "majar_mode": "colab",
+    "voice_changer_server_url": "/test",
+    "sample_rate": 48000,
+    "buffer_size": 1024,
+    "prefix_chunk_size": 60,
+    "chunk_size": 60,
+    "speaker_ids": [999, 107],
+    "speaker_names": ["---", "user"],
+    "src_id": 107,
+    "dst_id": 999,
+    "vf_enable": true,
+    "voice_changer_mode": "realtime",
+    "gpu": 0,
+    "available_gpus": [0],
+    "avatar": {
+        "enable_avatar": true,        
+        "motion_capture_face": true,
+        "motion_capture_upperbody": true,
+        "lip_overwrite_with_voice": true,
+        "avatar_url": "./assets/vrm/zundamon/zundamon.vrm",
+        "backgournd_image_url": "./assets/images/bg_natural_sougen.jpg",
+        "background_color": "#0000dd",
+        "chroma_key": "#0000dd",
+        "avatar_canvas_size": [1280, 720],
+        "screen_canvas_size": [1280, 720]
+    },
+    "advance": {
+        "avatar_draw_skip_rate": 3,
+        "screen_draw_skip_rate": 3,
+        "visualizer_draw_skip_rate": 3,
+        "cross_fade_lower_value": 0.1,
+        "cross_fade_offset_rate": 0.3,
+        "cross_fade_end_rate": 0.6,
+        "cross_fade_type": 2
+    }
+}

trainer/Dockerfile

@@ -1,4 +1,4 @@
-FROM dannadori/voice-changer-internal:20220903_150759 as front
+FROM dannadori/voice-changer-internal:20220918_215800 as front
 FROM debian:bullseye-slim as base
 
 ARG DEBIAN_FRONTEND=noninteractive