AutoVC_training_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoVC training .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1zL5O_UHp9FMhn4AqNTf5c-_46aCB78g5",
      "authorship_tag": "ABX9TyN936oOrQasCqhzr517rjM0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gkv856/KaggleData/blob/main/AutoVC_training_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlD_l2VGnlMq",
        "outputId": "5fa8ae30-e794-4491-fbb8-49402db93255"
      },
      "source": [
        "!git clone https://github.com/gkv856/end2end_auto_voice_conversion.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'end2end_auto_voice_conversion'...\n",
            "remote: Enumerating objects: 720, done.\u001b[K\n",
            "remote: Counting objects: 100% (720/720), done.\u001b[K\n",
            "remote: Compressing objects: 100% (521/521), done.\u001b[K\n",
            "remote: Total 720 (delta 257), reused 616 (delta 153), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (720/720), 266.45 MiB | 31.29 MiB/s, done.\n",
            "Resolving deltas: 100% (257/257), done.\n",
            "Checking out files: 100% (213/213), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9F-Jn5-brXJP"
      },
      "source": [
        "!mv end2end_auto_voice_conversion/ AVC/"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iIVOtzwUqLkp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03d25bd9-6ddc-4566-9301-355b416a0b56"
      },
      "source": [
        "pip install webrtcvad"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting webrtcvad\n",
            "  Downloading webrtcvad-2.0.10.tar.gz (66 kB)\n",
            "\u001b[?25l\r\u001b[K     |█████                           | 10 kB 23.1 MB/s eta 0:00:01\r\u001b[K     |██████████                      | 20 kB 22.2 MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 30 kB 11.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████▉            | 40 kB 9.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 51 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▊  | 61 kB 5.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 66 kB 2.8 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: webrtcvad\n",
            "  Building wheel for webrtcvad (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for webrtcvad: filename=webrtcvad-2.0.10-cp37-cp37m-linux_x86_64.whl size=72381 sha256=d85a7b82aeb1d30339feabc54147db687457fadaf29ff9f172277b7d7f152858\n",
            "  Stored in directory: /root/.cache/pip/wheels/11/f9/67/a3158d131f57e1c0a7d8d966a707d4a2fb27567a4fe47723ad\n",
            "Successfully built webrtcvad\n",
            "Installing collected packages: webrtcvad\n",
            "Successfully installed webrtcvad-2.0.10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IjU5f1KrngW",
        "outputId": "f86fe1f2-6513-48c5-9a84-1a83d4f93ed7"
      },
      "source": [
        "from AVC.strings.constants import hp"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device type available = 'cuda:0'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "Ikq17qcOn1W_",
        "outputId": "2aebfae8-cc44-44e7-8176-741561ac093b"
      },
      "source": [
        "import torch\n",
        "\n",
        "from AVC.s3_auto_voice_cloner.s1_create_emb_per_speaker import create_embbedings_per_speaker\n",
        "\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# hp.m_ge2e.best_model_path = \"static/model_chk_pts/ge2e/final_epoch_1000_L_0.0390.pth\"\n",
        "\n",
        "utterances = create_embbedings_per_speaker(hp)\n",
        "\n",
        "\n",
        "labels = []\n",
        "embs = []\n",
        "for k, v in utterances.items():\n",
        "    embs.append(v)\n",
        "    labels.append(k)\n",
        "\n",
        "\n",
        "embeddings = torch.tensor(embs)\n",
        "\n",
        "scatters = TSNE(n_components=2, random_state=0).fit_transform(embeddings.cpu().detach().numpy())\n",
        "fig = plt.figure(figsize=(5, 5))\n",
        "\n",
        "current_Label = labels[0]\n",
        "current_Index = 0\n",
        "for index, label in enumerate(labels[1:], 1):\n",
        "    if label != current_Label:\n",
        "        plt.scatter(scatters[current_Index:index, 0], scatters[current_Index:index, 1],\n",
        "                    label='{}'.format(current_Label))\n",
        "        current_Label = label\n",
        "        current_Index = index\n",
        "\n",
        "plt.scatter(scatters[current_Index:, 0], scatters[current_Index:, 1], label='{}'.format(current_Label))\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-trained model loaded /content/AVC/static/model_chk_pts/ge2e/embedding_model_GE2E_loss_epoch_1000_L_0.0003.pth\n",
            "File saved!!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtIewt8kpdR6"
      },
      "source": [
        "from AVC.s3_auto_voice_cloner.s5_auto_vc_train import TrainAutoVCNetwork"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nr0mhvWUq7_e",
        "outputId": "17235df3-d6d5-434d-e555-ffe19884f8f6"
      },
      "source": [
        "\n",
        "hp.m_avc.tpm.lambda_cd = 1\n",
        "hp.m_avc.tpm.num_iters = 1000\n",
        "hp.m_avc.tpm.log_step = 100\n",
        "hp.m_avc.tpm.dot_print = 10\n",
        "hp.m_avc.tpm.checkpoint_interval = 200\n",
        "hp.m_avc.tpm.lr = 0.001\n",
        "hp.m_avc.tpm.reduce_lr_interval = 100\n",
        "hp.m_avc.tpm.data_batch_size = 2\n",
        "\n",
        "solver = TrainAutoVCNetwork(hp)\n",
        "solver.optimizer = torch.optim.Adam(solver.auto_vc_net.parameters(), \n",
        "                                    lr=solver.lr,\n",
        "                                    betas=(0.9, 0.999),\n",
        "                                    eps=1e-7,\n",
        "                                    weight_decay=0)\n",
        "# start the training\n",
        "auto_vc_model, lst_loss_tuple = solver.start_training()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Batched Training...\n",
            "Epoch:[100/1000] .."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9d3hSD23gvi"
      },
      "source": [
        "hp.m_avc.gen.best_model_path = \"/content/AVC/static/model_chk_pts/autovc/final_1000.pth\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXgEIKxlB17a"
      },
      "source": [
        "!pip install wavenet_vocoder\n",
        "!pip install webrtcvad"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig_jn3Z2q9is"
      },
      "source": [
        "import torch\n",
        "\n",
        "from AVC.s3_auto_voice_cloner.s1_create_emb_per_speaker import create_embbedings_per_speaker\n",
        "\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# hp.m_ge2e.best_model_path = \"static/model_chk_pts/ge2e/final_epoch_1000_L_0.0390.pth\"\n",
        "\n",
        "utterances = create_embbedings_per_speaker(hp)\n",
        "\n",
        "\n",
        "labels = []\n",
        "embs = []\n",
        "for k, v in utterances.items():\n",
        "    embs.append(v)\n",
        "    labels.append(k)\n",
        "\n",
        "\n",
        "embeddings = torch.tensor(embs)\n",
        "\n",
        "scatters = TSNE(n_components=2, random_state=0).fit_transform(embeddings.cpu().detach().numpy())\n",
        "fig = plt.figure(figsize=(5, 5))\n",
        "\n",
        "current_Label = labels[0]\n",
        "current_Index = 0\n",
        "for index, label in enumerate(labels[1:], 1):\n",
        "    if label != current_Label:\n",
        "        plt.scatter(scatters[current_Index:index, 0], scatters[current_Index:index, 1],\n",
        "                    label='{}'.format(current_Label))\n",
        "        current_Label = label\n",
        "        current_Index = index\n",
        "\n",
        "plt.scatter(scatters[current_Index:, 0], scatters[current_Index:, 1], label='{}'.format(current_Label))\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EcBFWkg3Wie"
      },
      "source": [
        "import tqdm\n",
        "\n",
        "from AVC.s3_auto_voice_cloner.s6_create_cross_speaker_mel_specs import VoiceCloner\n",
        "import soundfile as sf\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oee-8QuUB_bD"
      },
      "source": [
        "hp.m_avc.gen.best_model_path = \"/content/AVC/static/model_chk_pts/autovc/final_1000.pth\"\n",
        "hp.m_wave_net.gen.best_model_path = \"/content/drive/MyDrive/AI_ML_DL/model_chk_pts/wavenet_model/wavenet_pretrained_step001000000_ema.pth\"\n",
        "vcs_obj = VoiceCloner(hp, tqdm, absolute_path=True)\n",
        "\n",
        "path_audio = \"static/raw_data/wavs/p225/p225_003.wav\"\n",
        "path_audio = os.path.join(hp.general.project_root, path_audio)\n",
        "spkr_p225_mel_spec = vcs_obj.au.get_mel_spects_from_audio(path_audio, partial_slices=False)\n",
        "\n",
        "path_audio = \"static/raw_data/wavs/p226/p226_003.wav\"\n",
        "path_audio = os.path.join(hp.general.project_root, path_audio)\n",
        "spkr_p226_mel_spec = vcs_obj.au.get_mel_spects_from_audio(path_audio, partial_slices=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8UzGtndCLeV"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import math\n",
        "import librosa\n",
        "import librosa.display"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4sgHQ2bCXCs"
      },
      "source": [
        "# Plot mel spectrograms\n",
        "fig, ax = plt.subplots(1,2, figsize = (20,10))\n",
        "\n",
        "ax[0].set(title = 'Mel Spectrogram of Guitar')\n",
        "i = librosa.display.specshow(spkr_p225_mel_spec, ax=ax[0])\n",
        "\n",
        "ax[1].set(title = 'Mel Spectrogram of Kick')\n",
        "librosa.display.specshow(spkr_p226_mel_spec, ax=ax[1])\n",
        "\n",
        "plt.colorbar(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlNXkRO0Cfsz"
      },
      "source": [
        "avc_mel_specs = vcs_obj.create_cross_spkr_mel_spects(\"p225\", \"p226\", spkr_p225_mel_spec[:320, :])\n",
        "avc_mel_specs.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C63S2fAiEMzQ"
      },
      "source": [
        "# Plot mel spectrograms\n",
        "fig, ax = plt.subplots(1,2, figsize = (20,10))\n",
        "\n",
        "ax[0].set(title = 'Mel Spectrogram of P225')\n",
        "i = librosa.display.specshow(spkr_p225_mel_spec[:320, :], ax=ax[0])\n",
        "\n",
        "ax[1].set(title = 'AVC mel spect')\n",
        "librosa.display.specshow(avc_mel_specs, ax=ax[1])\n",
        "\n",
        "plt.colorbar(i)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}