File Manager

Editing File: /home/ubuntu/codegamaai-test/voice_clone/notebooks/.ipynb_checkpoints/RVC_Clone-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d714ec09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de0bc82a07684c66aa442c58852e4c97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='success', description='✔ Success', style=ButtonStyle())"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install gradio\n",
    "clear_output()\n",
    "Button(description=\"\\u2714 Success\", button_style=\"success\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3527ddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/haive/voice_clone_rvc\n"
     ]
    }
   ],
   "source": [
    "cd /home/haive/voice_clone_rvc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97f77d88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34mdataset\u001b[0m/  RVC_Clone.ipynb  \u001b[01;34mrvc_implementation\u001b[0m/  \u001b[01;34mVoiceConversionWebUI\u001b[0m/\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33b91808",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "from ipywidgets import Button\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dab76631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "--2023-11-16 08:07:36--  https://cdn-lfs.huggingface.co/repos/75/c8/75c8d4b069a1863fc7f85ffabaff4b3755f0b320f4254aad27abf080b56d6f7c/dcfb833d0514f8d3558abb5fca9975f9093c75ff7b316fd395e2b80e358a3769?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27project-main.zip%3B+filename%3D%22project-main.zip%22%3B&response-content-type=application%2Fzip&Expires=1700381256&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMDM4MTI1Nn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy83NS9jOC83NWM4ZDRiMDY5YTE4NjNmYzdmODVmZmFiYWZmNGIzNzU1ZjBiMzIwZjQyNTRhYWQyN2FiZjA4MGI1NmQ2ZjdjL2RjZmI4MzNkMDUxNGY4ZDM1NThhYmI1ZmNhOTk3NWY5MDkzYzc1ZmY3YjMxNmZkMzk1ZTJiODBlMzU4YTM3Njk%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=Cm3Upo7Zibh5fREHF0nlLLA%7ERCalOW3PNa-xHM1%7EaPFAFb939GyZVIbFhTGT2ZDHa2pdxxfW4BCIX-umB78-oZWECCthqa7n4IEnKjNj2dPwdNGiv6PrtsbazKvW10DeLT18tR9xYKoj5HJpC37eLqrRIlc7ZM6WcDEUD03XcpecoyX0IWCYSnnIoG9RhbYZ2D0ovU%7EYHJd3HE1EFBZZECekHdu-C46RHbuz-cnrD9LYf5-VeVlp1rKmVd92dgz72-65Q39Yfa1c%7EP1vQST8jC1x0AA3ogcaVLsFEXacQ71hlWi70-mDiCNn2DF1XXRoTDuCBVJJFYWwKdZ3AEGUYw__&Key-Pair-Id=KVTP0A1DKRTAX\n",
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      "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.164.116.15|:443... connected.\n",
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      "Saving to: ‘/home/haive/voice_clone_rvc/project-main.zip’\n",
      "\n",
      "/home/haive/voice_c 100%[===================>]   1.44M  --.-KB/s    in 0.04s   \n",
      "\n",
      "2023-11-16 08:07:36 (34.8 MB/s) - ‘/home/haive/voice_clone_rvc/project-main.zip’ saved [1510847/1510847]\n",
      "\n",
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      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/.github/\n",
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      "  inflating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/GUI.py  \n",
      "  inflating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/LICENSE  \n",
      "  inflating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/MIT协议暨相关引用库协议  \n",
      "  inflating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/README.md  \n",
      "  inflating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/Retrieval_based_Voice_Conversion_WebUI.ipynb  \n",
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      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/\n",
      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/hubert/\n",
      " extracting: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/hubert/.gitignore  \n",
      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/pretrained/\n",
      " extracting: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/pretrained/.gitignore  \n",
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      " extracting: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/pretrained_v2/.gitignore  \n",
      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/rmvpe/\n",
      " extracting: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/rmvpe/.gitignore  \n",
      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/uvr5_weights/\n",
      " extracting: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/uvr5_weights/.gitignore  \n",
      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/weights/\n",
      " extracting: /home/haive/voice_clone_rvc/rvc_implementation/project-main/assets/weights/.gitignore  \n",
      "   creating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/audios/\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  inflating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/logs/mute/3_feature768/mute.npy  \r\n",
      "  inflating: /home/haive/voice_clone_rvc/rvc_implementation/project-main/poetry.lock  \r\n",
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     ]
    }
   ],
   "source": [
    "if not os.path.exists('/home/haive/voice_clone_rvc'):\n",
    "    print(\"Folder Not Available. Creating New Folder.\")\n",
    "    os.makedirs('/home/haive/voice_clone_rvc')\n",
    "source = \"Rejekts\"\n",
    "!wget https://huggingface.co/{source}/project/resolve/main/project-main.zip -O '/home/haive/voice_clone_rvc/project-main.zip' && unzip -n 'project-main.zip' -d /home/haive/voice_clone_rvc/rvc_implementation/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1bbc6f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed6ee79a9645425f827960d9b7ab81e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='success', description='✔ Success', style=ButtonStyle())"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!cd '/home/haive/voice_clone_rvc/rvc_implementation/project-main' && python download_files.py && pip install -r 'requirements-safe.txt'\n",
    "!pip install pyngrok\n",
    "!rm /home/haive/voice_clone_rvc/project-main.zip\n",
    "!rm -r /home/haive/voice_clone_rvc/sample_data\n",
    "!mkdir -p /home/haive/voice_clone_rvc/dataset\n",
    "clear_output()\n",
    "Button(description=\"\\u2714 Success\", button_style=\"success\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "692fd3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3699bafea0cd40e19b709fefd9b4fddc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='success', description='✔ Success', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title 1.Preprocess Data\n",
    "%cd /home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
    "model_name = 'test2' #@param {type:\"string\"}\n",
    "#@markdown <small> Enter the path to your dataset folder (a folder with audios of the vocals you will train on), or if you want just upload the audios using the File Manager into the 'dataset' folder.\n",
    "dataset_folder = '/home/haive/voice_clone_rvc/dataset/test2' #@param {type:\"string\"}\n",
    "while len(os.listdir(dataset_folder)) < 1:\n",
    "    input(\"Your dataset folder is empty.\")\n",
    "!mkdir -p ./logs/{model_name}\n",
    "with open(f'./logs/{model_name}/preprocess.log','w') as f:\n",
    "    print(\"Starting...\")\n",
    "!python infer/modules/train/preprocess.py {dataset_folder} 40000 2 ./logs/{model_name} False 3.0 > /dev/null 2>&1\n",
    "with open(f'./logs/{model_name}/preprocess.log','r') as f:\n",
    "    if 'end preprocess' in f.read():\n",
    "        clear_output()\n",
    "        display(Button(description=\"\\u2714 Success\", button_style=\"success\"))\n",
    "    else:\n",
    "        print(\"Error preprocessing data... Make sure your dataset folder is correct.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1699772d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6eeaffb9c6c4252a2bb8a3d828a2d02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='success', description='✔ Success', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title 2.Extract Features\n",
    "f0method = \"rmvpe_gpu\" # @param [\"pm\", \"harvest\", \"rmvpe\", \"rmvpe_gpu\"]\n",
    "%cd /home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
    "with open(f'./logs/{model_name}/extract_f0_feature.log','w') as f:\n",
    "    print(\"Starting...\")\n",
    "if f0method != \"rmvpe_gpu\":\n",
    "    !python infer/modules/train/extract/extract_f0_print.py ./logs/{model_name} 2 {f0method}\n",
    "else:\n",
    "    !python infer/modules/train/extract/extract_f0_rmvpe.py 1 0 0 ./logs/{model_name} True\n",
    "!python infer/modules/train/extract_feature_print.py cuda:0 1 0 0 ./logs/{model_name} v2\n",
    "with open(f'./logs/{model_name}/extract_f0_feature.log','r') as f:\n",
    "    if 'all-feature-done' in f.read():\n",
    "        clear_output()\n",
    "        display(Button(description=\"\\u2714 Success\", button_style=\"success\"))\n",
    "    else:\n",
    "        print(\"Error preprocessing data... Make sure your data was preprocessed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fbe7950c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e93dd320bea0408cb7bb2974b3f5ecf4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='success', description='✔ Success', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title 3.Train Index\n",
    "import numpy as np\n",
    "import faiss\n",
    "%cd /home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
    "def train_index(exp_dir1, version19):\n",
    "    exp_dir = \"logs/%s\" % (exp_dir1)\n",
    "    os.makedirs(exp_dir, exist_ok=True)\n",
    "    feature_dir = (\n",
    "        \"%s/3_feature256\" % (exp_dir)\n",
    "        if version19 == \"v1\"\n",
    "        else \"%s/3_feature768\" % (exp_dir)\n",
    "    )\n",
    "    if not os.path.exists(feature_dir):\n",
    "        return \"feature_dir path doesn't exist\"\n",
    "    listdir_res = list(os.listdir(feature_dir))\n",
    "    if len(listdir_res) == 0:\n",
    "        return \"feature_dir path doesn't contain any data\"\n",
    "    infos = []\n",
    "    npys = []\n",
    "    for name in sorted(listdir_res):\n",
    "        phone = np.load(\"%s/%s\" % (feature_dir, name))\n",
    "        npys.append(phone)\n",
    "    big_npy = np.concatenate(npys, 0)\n",
    "    big_npy_idx = np.arange(big_npy.shape[0])\n",
    "    np.random.shuffle(big_npy_idx)\n",
    "    big_npy = big_npy[big_npy_idx]\n",
    "    if big_npy.shape[0] > 2e5:\n",
    "        infos.append(\"Trying doing kmeans %s shape to 10k centers.\" % big_npy.shape[0])\n",
    "        yield \"\\n\".join(infos)\n",
    "        try:\n",
    "            big_npy = (\n",
    "                MiniBatchKMeans(\n",
    "                    n_clusters=10000,\n",
    "                    verbose=True,\n",
    "                    batch_size=256 * config.n_cpu,\n",
    "                    compute_labels=False,\n",
    "                    init=\"random\",\n",
    "                )\n",
    "                .fit(big_npy)\n",
    "                .cluster_centers_\n",
    "            )\n",
    "        except:\n",
    "            info = traceback.format_exc()\n",
    "            logger.info(info)\n",
    "            infos.append(info)\n",
    "            yield \"\\n\".join(infos)\n",
    "\n",
    "    np.save(\"%s/total_fea.npy\" % exp_dir, big_npy)\n",
    "    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)\n",
    "    infos.append(\"%s,%s\" % (big_npy.shape, n_ivf))\n",
    "    yield \"\\n\".join(infos)\n",
    "    index = faiss.index_factory(256 if version19 == \"v1\" else 768, \"IVF%s,Flat\" % n_ivf)\n",
    "    infos.append(\"training\")\n",
    "    yield \"\\n\".join(infos)\n",
    "    index_ivf = faiss.extract_index_ivf(index)  #\n",
    "    index_ivf.nprobe = 1\n",
    "    index.train(big_npy)\n",
    "    faiss.write_index(\n",
    "        index,\n",
    "        \"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index\"\n",
    "        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),\n",
    "    )\n",
    "\n",
    "    infos.append(\"adding\")\n",
    "    yield \"\\n\".join(infos)\n",
    "    batch_size_add = 8192\n",
    "    for i in range(0, big_npy.shape[0], batch_size_add):\n",
    "        index.add(big_npy[i : i + batch_size_add])\n",
    "    faiss.write_index(\n",
    "        index,\n",
    "        \"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index\"\n",
    "        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),\n",
    "    )\n",
    "    infos.append(\n",
    "        \"成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index\"\n",
    "        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)\n",
    "    )\n",
    "\n",
    "training_log = train_index(model_name, 'v2')\n",
    "\n",
    "for line in training_log:\n",
    "    print(line)\n",
    "    if 'adding' in line:\n",
    "        clear_output()\n",
    "        display(Button(description=\"\\u2714 Success\", button_style=\"success\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "837f2efe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Authtoken saved to configuration file: /home/haive/.ngrok2/ngrok.yml\n",
      "/home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 8888 (pid 2720241), started 4:48:04 ago. (Use '!kill 2720241' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-36254e2cd81499cd\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-36254e2cd81499cd\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 8888;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorboard NGROK URL:NgrokTunnel: \"https://b5a7-162-244-83-121.ngrok-free.app\" -> \"http://localhost:8888\"\n",
      "Write filelist done\n",
      "Use gpus: 0\n",
      "INFO:test2:{'data': {'filter_length': 2048, 'hop_length': 400, 'max_wav_value': 32768.0, 'mel_fmax': None, 'mel_fmin': 0.0, 'n_mel_channels': 125, 'sampling_rate': 40000, 'win_length': 2048, 'training_files': './logs/test2/filelist.txt'}, 'model': {'filter_channels': 768, 'gin_channels': 256, 'hidden_channels': 192, 'inter_channels': 192, 'kernel_size': 3, 'n_heads': 2, 'n_layers': 6, 'p_dropout': 0, 'resblock': '1', 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'resblock_kernel_sizes': [3, 7, 11], 'spk_embed_dim': 109, 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'upsample_rates': [10, 10, 2, 2], 'use_spectral_norm': False}, 'train': {'batch_size': 7, 'betas': [0.8, 0.99], 'c_kl': 1.0, 'c_mel': 45, 'epochs': 20000, 'eps': 1e-09, 'fp16_run': True, 'init_lr_ratio': 1, 'learning_rate': 0.0001, 'log_interval': 200, 'lr_decay': 0.999875, 'seed': 1234, 'segment_size': 12800, 'warmup_epochs': 0}, 'model_dir': './logs/test2', 'experiment_dir': './logs/test2', 'save_every_epoch': 50, 'name': 'test2', 'total_epoch': 250, 'pretrainG': 'assets/pretrained_v2/f0G40k.pth', 'pretrainD': 'assets/pretrained_v2/f0D40k.pth', 'version': 'v2', 'gpus': '0', 'sample_rate': '40k', 'if_f0': 1, 'if_latest': 1, 'save_every_weights': '1', 'if_cache_data_in_gpu': 0}\n",
      "/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\n",
      "warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\n",
      "DEBUG:infer.lib.infer_pack.models:gin_channels: 256, self.spk_embed_dim: 109\n",
      "DEBUG:test2:./logs/test2/D_2333333.pth\n",
      "INFO:test2:Loaded model weights\n",
      "INFO:test2:Loaded checkpoint './logs/test2/D_2333333.pth' (epoch 250)\n",
      "INFO:test2:loaded D\n",
      "DEBUG:test2:./logs/test2/G_2333333.pth\n",
      "INFO:test2:Loaded model weights\n",
      "INFO:test2:Loaded checkpoint './logs/test2/G_2333333.pth' (epoch 250)\n",
      "/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/torch/functional.py:650: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\n",
      "Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:863.)\n",
      "return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]\n",
      "/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/torch/autograd/__init__.py:251: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed.  This is not an error, but may impair performance.\n",
      "grad.sizes() = [64, 1, 4], strides() = [4, 1, 1]\n",
      "bucket_view.sizes() = [64, 1, 4], strides() = [4, 4, 1] (Triggered internally at ../torch/csrc/distributed/c10d/reducer.cpp:320.)\n",
      "Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
      "INFO:test2:Saving model and optimizer state at epoch 250 to ./logs/test2/G_2333333.pth\n",
      "INFO:test2:Saving model and optimizer state at epoch 250 to ./logs/test2/D_2333333.pth\n",
      "INFO:test2:saving ckpt test2_e250:Success.\n",
      "INFO:test2:====> Epoch: 250 [2023-11-16 13:23:10] | (0:00:20.716771)\n",
      "INFO:test2:Training is done. The program is closed.\n",
      "INFO:test2:saving final ckpt:Success.\n",
      "/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown\n",
      "warnings.warn('resource_tracker: There appear to be %d '\n",
      "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log\n"
     ]
    }
   ],
   "source": [
    "#@title 4.Train Model\n",
    "#@markdown <small> Enter your ngrok authtoken to open tensorboard. Get one here: https://dashboard.ngrok.com/get-started/your-authtoken\n",
    "ngrok_authtoken = \"2VsTppE0Z0NEkYGk5BiSotnQRhT_3fqLxdic8Aqg9EZ29cpnL\"#@param {type:\"string\"}\n",
    "!ngrok config add-authtoken {ngrok_authtoken}\n",
    "%cd /home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
    "from random import shuffle\n",
    "import json\n",
    "import os\n",
    "import pathlib\n",
    "from subprocess import Popen, PIPE, STDOUT\n",
    "from pyngrok import ngrok\n",
    "now_dir=os.getcwd()\n",
    "#@markdown <small> Enter the name of your model again. It must be the same you chose before.\n",
    "model_name = 'test2' #@param {type:\"string\"}\n",
    "#@markdown <small> Choose how often to save the model and how much training you want it to have.\n",
    "save_frequency = 50 # @param {type:\"slider\", min:5, max:50, step:5}\n",
    "epochs = 250 # @param {type:\"slider\", min:10, max:1000, step:10}\n",
    "#@markdown <small> ONLY cache datasets under 10 minutes long. Otherwise leave this unchecked.\n",
    "cache = False #@param {type:\"boolean\"}\n",
    "# Remove the logging setup\n",
    "\n",
    "def click_train(\n",
    "    exp_dir1,\n",
    "    sr2,\n",
    "    if_f0_3,\n",
    "    spk_id5,\n",
    "    save_epoch10,\n",
    "    total_epoch11,\n",
    "    batch_size12,\n",
    "    if_save_latest13,\n",
    "    pretrained_G14,\n",
    "    pretrained_D15,\n",
    "    gpus16,\n",
    "    if_cache_gpu17,\n",
    "    if_save_every_weights18,\n",
    "    version19,\n",
    "):\n",
    "    # 生成filelist\n",
    "    exp_dir = \"%s/logs/%s\" % (now_dir, exp_dir1)\n",
    "    os.makedirs(exp_dir, exist_ok=True)\n",
    "    gt_wavs_dir = \"%s/0_gt_wavs\" % (exp_dir)\n",
    "    feature_dir = (\n",
    "        \"%s/3_feature256\" % (exp_dir)\n",
    "        if version19 == \"v1\"\n",
    "        else \"%s/3_feature768\" % (exp_dir)\n",
    "    )\n",
    "    if if_f0_3:\n",
    "        f0_dir = \"%s/2a_f0\" % (exp_dir)\n",
    "        f0nsf_dir = \"%s/2b-f0nsf\" % (exp_dir)\n",
    "        names = (\n",
    "            set([name.split(\".\")[0] for name in os.listdir(gt_wavs_dir)])\n",
    "            & set([name.split(\".\")[0] for name in os.listdir(feature_dir)])\n",
    "            & set([name.split(\".\")[0] for name in os.listdir(f0_dir)])\n",
    "            & set([name.split(\".\")[0] for name in os.listdir(f0nsf_dir)])\n",
    "        )\n",
    "    else:\n",
    "        names = set([name.split(\".\")[0] for name in os.listdir(gt_wavs_dir)]) & set(\n",
    "            [name.split(\".\")[0] for name in os.listdir(feature_dir)]\n",
    "        )\n",
    "    opt = []\n",
    "    for name in names:\n",
    "        if if_f0_3:\n",
    "            opt.append(\n",
    "                \"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s\"\n",
    "                % (\n",
    "                    gt_wavs_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
    "                    name,\n",
    "                    feature_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
    "                    name,\n",
    "                    f0_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
    "                    name,\n",
    "                    f0nsf_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
    "                    name,\n",
    "                    spk_id5,\n",
    "                )\n",
    "            )\n",
    "        else:\n",
    "            opt.append(\n",
    "                \"%s/%s.wav|%s/%s.npy|%s\"\n",
    "                % (\n",
    "                    gt_wavs_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
    "                    name,\n",
    "                    feature_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
    "                    name,\n",
    "                    spk_id5,\n",
    "                )\n",
    "            )\n",
    "    fea_dim = 256 if version19 == \"v1\" else 768\n",
    "    if if_f0_3:\n",
    "        for _ in range(2):\n",
    "            opt.append(\n",
    "                \"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s\"\n",
    "                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)\n",
    "            )\n",
    "    else:\n",
    "        for _ in range(2):\n",
    "            opt.append(\n",
    "                \"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s\"\n",
    "                % (now_dir, sr2, now_dir, fea_dim, spk_id5)\n",
    "            )\n",
    "    shuffle(opt)\n",
    "    with open(\"%s/filelist.txt\" % exp_dir, \"w\") as f:\n",
    "        f.write(\"\\n\".join(opt))\n",
    "\n",
    "    # Replace logger.debug, logger.info with print statements\n",
    "    print(\"Write filelist done\")\n",
    "    print(\"Use gpus:\", str(gpus16))\n",
    "    if pretrained_G14 == \"\":\n",
    "        print(\"No pretrained Generator\")\n",
    "    if pretrained_D15 == \"\":\n",
    "        print(\"No pretrained Discriminator\")\n",
    "    if version19 == \"v1\" or sr2 == \"40k\":\n",
    "        config_path = \"configs/v1/%s.json\" % sr2\n",
    "    else:\n",
    "        config_path = \"configs/v2/%s.json\" % sr2\n",
    "    config_save_path = os.path.join(exp_dir, \"config.json\")\n",
    "    if not pathlib.Path(config_save_path).exists():\n",
    "        with open(config_save_path, \"w\", encoding=\"utf-8\") as f:\n",
    "            with open(config_path, \"r\") as config_file:\n",
    "                config_data = json.load(config_file)\n",
    "                json.dump(\n",
    "                    config_data,\n",
    "                    f,\n",
    "                    ensure_ascii=False,\n",
    "                    indent=4,\n",
    "                    sort_keys=True,\n",
    "                )\n",
    "            f.write(\"\\n\")\n",
    "\n",
    "    cmd = (\n",
    "        'python infer/modules/train/train.py -e \"%s\" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'\n",
    "        % (\n",
    "            exp_dir1,\n",
    "            sr2,\n",
    "            1 if if_f0_3 else 0,\n",
    "            batch_size12,\n",
    "            gpus16,\n",
    "            total_epoch11,\n",
    "            save_epoch10,\n",
    "            \"-pg %s\" % pretrained_G14 if pretrained_G14 != \"\" else \"\",\n",
    "            \"-pd %s\" % pretrained_D15 if pretrained_D15 != \"\" else \"\",\n",
    "            1 if if_save_latest13 == True else 0,\n",
    "            1 if if_cache_gpu17 == True else 0,\n",
    "            1 if if_save_every_weights18 == True else 0,\n",
    "            version19,\n",
    "        )\n",
    "    )\n",
    "    # Use PIPE to capture the output and error streams\n",
    "    p = Popen(cmd, shell=True, cwd=now_dir, stdout=PIPE, stderr=STDOUT, bufsize=1, universal_newlines=True)\n",
    "\n",
    "    # Print the command's output as it runs\n",
    "    for line in p.stdout:\n",
    "        print(line.strip())\n",
    "\n",
    "    # Wait for the process to finish\n",
    "    p.wait()\n",
    "    return \"训练结束, 您可查看控制台训练日志或实验文件夹下的train.log\"\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir ./logs --port=8888\n",
    "print(\"Tensorboard NGROK URL:\",end=\"\")\n",
    "try:\n",
    "    ngrok_url = ngrok.connect(8888)\n",
    "    print(ngrok_url)\n",
    "    training_log = click_train(\n",
    "        model_name,\n",
    "        '40k',\n",
    "        True,\n",
    "        0,\n",
    "        save_frequency,\n",
    "        epochs,\n",
    "        7,\n",
    "        True,\n",
    "        'assets/pretrained_v2/f0G40k.pth',\n",
    "        'assets/pretrained_v2/f0D40k.pth',\n",
    "        0,\n",
    "        cache,\n",
    "        True,\n",
    "        'v2',\n",
    "    )\n",
    "    print(training_log)\n",
    "except:\n",
    "    ngrok.kill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d04566ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
      "2023-11-16 09:36:40 | INFO | faiss.loader | Loading faiss with AVX2 support.\n",
      "2023-11-16 09:36:40 | INFO | faiss.loader | Successfully loaded faiss with AVX2 support.\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/haive/voice_clone_rvc/rvc_implementation/project-main/app.py\", line 33, in <module>\n",
      "    from infer.modules.uvr5.modules import uvr\n",
      "  File \"/home/haive/voice_clone_rvc/rvc_implementation/project-main/infer/modules/uvr5/modules.py\", line 11, in <module>\n",
      "    from infer.modules.uvr5.mdxnet import MDXNetDereverb\n",
      "  File \"/home/haive/voice_clone_rvc/rvc_implementation/project-main/infer/modules/uvr5/mdxnet.py\", line 6, in <module>\n",
      "    import librosa\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/librosa/__init__.py\", line 209, in <module>\n",
      "    from . import core\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/librosa/core/__init__.py\", line 5, in <module>\n",
      "    from .convert import *  # pylint: disable=wildcard-import\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/librosa/core/convert.py\", line 7, in <module>\n",
      "    from . import notation\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/librosa/core/notation.py\", line 8, in <module>\n",
      "    from ..util.exceptions import ParameterError\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/librosa/util/__init__.py\", line 77, in <module>\n",
      "    from .utils import *  # pylint: disable=wildcard-import\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/librosa/util/utils.py\", line 9, in <module>\n",
      "    import numba\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/numba/__init__.py\", line 42, in <module>\n",
      "    from numba.np.ufunc import (vectorize, guvectorize, threading_layer,\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/numba/np/ufunc/__init__.py\", line 3, in <module>\n",
      "    from numba.np.ufunc.decorators import Vectorize, GUVectorize, vectorize, guvectorize\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/numba/np/ufunc/decorators.py\", line 3, in <module>\n",
      "    from numba.np.ufunc import _internal\n",
      "SystemError: initialization of _internal failed without raising an exception\n"
     ]
    }
   ],
   "source": [
    "%cd /home/haive/voice_clone_rvc/src/rvc_implementation\n",
    "!python app.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c3eb06c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
      "2023-11-16 11:21:41 | INFO | faiss.loader | Loading faiss with AVX2 support.\n",
      "2023-11-16 11:21:41 | INFO | faiss.loader | Successfully loaded faiss with AVX2 support.\n",
      "2023-11-16 11:21:44 | INFO | configs.config | Found GPU NVIDIA A100-PCIE-40GB\n",
      "2023-11-16 11:21:44 | INFO | __main__ | Use Language: en_US\n",
      "\u001b[31mERROR\u001b[0m:    [Errno 98] error while attempting to bind on address ('162.244.83.121', 7885): address already in use\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/haive/voice_clone_rvc/rvc_implementation/project-main/infer-web.py\", line 1501, in <module>\n",
      "    app.queue().launch(\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/gradio/blocks.py\", line 1865, in launch\n",
      "    ) = networking.start_server(\n",
      "  File \"/home/haive/miniconda3/envs/rvc_clone/lib/python3.9/site-packages/gradio/networking.py\", line 206, in start_server\n",
      "    raise OSError(\n",
      "OSError: Cannot find empty port in range: 7885-7885. You can specify a different port by setting the GRADIO_SERVER_PORT environment variable or passing the `server_port` parameter to `launch()`.\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%cd /home/haive/voice_clone_rvc/rvc_implementation/project-main\n",
    "!python infer-web.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5760aabf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;35ma.png\u001b[0m                    infer-web.py\r\n",
      "app.py                   LICENSE\r\n",
      "\u001b[01;34massets\u001b[0m/                  \u001b[01;34mlogs\u001b[0m/\r\n",
      "\u001b[01;34maudios\u001b[0m/                  MIT协议暨相关引用库协议\r\n",
      "\u001b[01;34mconfigs\u001b[0m/                 poetry.lock\r\n",
      "docker-compose.yml       pyproject.toml\r\n",
      "Dockerfile               README.md\r\n",
      "\u001b[01;34mdocs\u001b[0m/                    requirements-dml.txt\r\n",
      "download_files.py        requirements-ipex.txt\r\n",
      "environment_dml.yaml     requirements-safe.txt\r\n",
      "go-realtime-gui.bat      requirements.txt\r\n",
      "go-realtime-gui-dml.bat  requirements-win-for-realtime_vc_gui-dml.txt\r\n",
      "go-web.bat               requirements-win-for-realtime_vc_gui.txt\r\n",
      "go-web-dml.bat           Retrieval_based_Voice_Conversion_WebUI.ipynb\r\n",
      "GUI.py                   Retrieval_based_Voice_Conversion_WebUI_v2.ipynb\r\n",
      "gui_v1.py                \u001b[01;32mrun.sh\u001b[0m*\r\n",
      "\u001b[01;34mi18n\u001b[0m/                    \u001b[01;34mtools\u001b[0m/\r\n",
      "\u001b[01;34minfer\u001b[0m/                   \u001b[01;32mvenv.sh\u001b[0m*\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fca22f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title OR Open the GUI (Banned for free Colab Notebooks)\n",
    "if not 'installed' in locals():\n",
    "    %cd /content\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "    from IPython.display import clear_output\n",
    "    from ipywidgets import Button\n",
    "    import os\n",
    "    if not os.path.exists('/content/drive'):\n",
    "        print(\"Your drive is not mounted. Creating Fake Drive.\")\n",
    "        os.makedirs('/content/drive/MyDrive')\n",
    "    if not os.path.exists('/content/drive/MyDrive/project-main'):\n",
    "        !wget https://huggingface.co/Rejekts/project/resolve/main/project-main.zip -O '/content/project-main.zip' && unzip 'project-main.zip' -d /content/drive/MyDrive\n",
    "    !cd '/content/drive/MyDrive/project-main' && python download_files.py && pip install -r 'requirements.txt'\n",
    "    !rm /content/project-main.zip\n",
    "    !rm -r /content/sample_data\n",
    "    !mkdir -p /content/dataset\n",
    "    clear_output()\n",
    "    Button(description=\"\\u2714 Success\", button_style=\"success\")\n",
    "tensorboard = True #@param {type:\"boolean\"}\n",
    "if tensorboard:\n",
    "    %load_ext tensorboard\n",
    "    %tensorboard --logdir ./logs\n",
    "%cd /content/drive/MyDrive/project-main\n",
    "!python app.py --colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b53cc9b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5ae2343",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15199059",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c86ac935-2aea-4621-a78a-74d74157995b\n",
      "API request successful.\n",
      "Response: None\n",
      "/var/www/html/output/public/AI_voice/rvc_implc86ac935-2aea-4621-a78a-74d74157995b.mp3\n",
      "https://cloud.haive.online/public/AI_voice/rvc_implc86ac935-2aea-4621-a78a-74d74157995b.mp3\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import requests\n",
    "import uuid\n",
    "\n",
    "def main_TTS(text,id,uid, speaker_iden):\n",
    "    payload = {\n",
    "        \"text\": text,\n",
    "        \"speaker_iden\" : speaker_iden,\n",
    "        \"id\" : id,\n",
    "        \"uid\": uid\n",
    "    }\n",
    "    \n",
    "    gg = text_to_audio_api_request(payload = payload)\n",
    "    media_url = \"https://cloud.haive.online/public/AI_voice/\"+id+uid+\".mp3\"\n",
    "    output_file_mp3 = \"/var/www/html/output/public/AI_voice/\"+id+str(uid)+\".mp3\"\n",
    "    return output_file_mp3, media_url\n",
    "\n",
    "def text_to_audio_api_request(payload):\n",
    "\n",
    "    url = \"http://162.244.83.121:8008/text-to-audio-msen\"\n",
    "    headers = {\"Content-Type\": \"application/json\"}\n",
    "    \n",
    "    try:\n",
    "        response = requests.post(url, headers=headers, data=json.dumps(payload))\n",
    "        response_data = response.json()\n",
    "        \n",
    "        if response.status_code == 200:\n",
    "            print(\"API request successful.\")\n",
    "            print(\"Response:\", response_data)\n",
    "        else:\n",
    "            print(\"API request failed. Status code:\", response.status_code)\n",
    "            print(\"Response:\", response_data)\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred:\", str(e))\n",
    "    return \"File Created\"\n",
    "\n",
    "\n",
    "text = \"Hello, my name is Haive. I work in Codegama\"\n",
    "model_name = \"rvc_impl\"\n",
    "uid = str(uuid.uuid4())\n",
    "print(uid)\n",
    "speaker_iden = 19\n",
    "output_file_mp3, media_url = main_TTS(text,model_name,uid, speaker_iden)\n",
    "print(output_file_mp3)\n",
    "print(media_url)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8151884f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "\n",
    "def read_audio_file(file_path):\n",
    "    # Read the audio file\n",
    "    audio, sample_rate = librosa.load(file_path, sr=None)\n",
    "\n",
    "    return audio, sample_rate\n",
    "\n",
    "# Example usage\n",
    "file_path = 'path/to/your/audio/file.wav'  # replace with the actual file path\n",
    "audio, sample_rate = read_audio_file(file_path)\n",
    "\n",
    "# Now, you can use 'audio' and 'sample_rate' in your further processing\n",
    "print(f\"Audio duration: {len(audio)/sample_rate} seconds\")\n"
   ]
  }
 ],
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