Deep_Fakes_for_voice_Colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deep Fakes for voice - Colab .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfTsth4Uz6zi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "a608695a-2451-4dc8-f4d1-da6d85f8d434"
      },
      "source": [
        "!git clone https://github.com/CorentinJ/Real-Time-Voice-Cloning.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Real-Time-Voice-Cloning'...\n",
            "remote: Enumerating objects: 5, done.\u001b[K\n",
            "remote: Counting objects: 100% (5/5), done.\u001b[K\n",
            "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
            "remote: Total 2508 (delta 0), reused 3 (delta 0), pack-reused 2503\u001b[K\n",
            "Receiving objects: 100% (2508/2508), 360.78 MiB | 29.67 MiB/s, done.\n",
            "Resolving deltas: 100% (1388/1388), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAiyiuyw0Hjd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "47049b41-3b3b-4c6c-979b-02fc1ffe4f5d"
      },
      "source": [
        "cd Real-Time-Voice-Cloning/"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Real-Time-Voice-Cloning\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CP-TfAer1d_c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "c8fbbb23-8c1d-4279-92e0-bce3e141439c"
      },
      "source": [
        "!pip install -q -r requirements.txt"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 412.3MB 43kB/s \n",
            "\u001b[K     |████████████████████████████████| 686kB 30.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 245kB 38.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 76.6MB 38kB/s \n",
            "\u001b[K     |████████████████████████████████| 3.8MB 30.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 512kB 34.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 204kB 38.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 286kB 38.5MB/s \n",
            "\u001b[?25h  Building wheel for visdom (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for torchfile (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: tensorflow-probability 0.10.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzsDe3Ea1g0F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "a5ca3c0b-8879-4f47-d68c-4b0f9c72a2ca"
      },
      "source": [
        "!apt-get install -qq libportaudio2"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selecting previously unselected package libportaudio2:amd64.\n",
            "(Reading database ... 144465 files and directories currently installed.)\n",
            "Preparing to unpack .../libportaudio2_19.6.0-1_amd64.deb ...\n",
            "Unpacking libportaudio2:amd64 (19.6.0-1) ...\n",
            "Setting up libportaudio2:amd64 (19.6.0-1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdhwjwQ82Vt_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "da69f47f-b650-4d07-dfc8-5f0bf8139681"
      },
      "source": [
        "!gdown https://drive.google.com/uc?id=1n1sPXvT34yXFLT47QZA6FIRGrwMeSsZc"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1n1sPXvT34yXFLT47QZA6FIRGrwMeSsZc\n",
            "To: /content/Real-Time-Voice-Cloning/pretrained.zip\n",
            "384MB [00:02, 163MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOCMfzyt2cFH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "655f89c8-9776-4f12-acb8-4950aa1d7463"
      },
      "source": [
        "!unzip pretrained.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  pretrained.zip\n",
            "   creating: encoder/saved_models/\n",
            "  inflating: encoder/saved_models/pretrained.pt  \n",
            "   creating: synthesizer/saved_models/\n",
            "   creating: synthesizer/saved_models/logs-pretrained/\n",
            "   creating: synthesizer/saved_models/logs-pretrained/taco_pretrained/\n",
            " extracting: synthesizer/saved_models/logs-pretrained/taco_pretrained/checkpoint  \n",
            "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.data-00000-of-00001  \n",
            "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.index  \n",
            "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.meta  \n",
            "   creating: vocoder/saved_models/\n",
            "   creating: vocoder/saved_models/pretrained/\n",
            "  inflating: vocoder/saved_models/pretrained/pretrained.pt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLN_Z4qW2pe0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code for recording audio from the browser\n",
        "from IPython.display import Javascript\n",
        "from google.colab import output\n",
        "from base64 import b64decode\n",
        "import IPython\n",
        "import uuid\n",
        "from google.colab import output\n",
        "\n",
        "\n",
        "class InvokeButton(object):\n",
        "  def __init__(self, title, callback):\n",
        "    self._title = title\n",
        "    self._callback = callback\n",
        "\n",
        "  def _repr_html_(self):\n",
        "    from google.colab import output\n",
        "    callback_id = 'button-' + str(uuid.uuid4())\n",
        "    output.register_callback(callback_id, self._callback)\n",
        "\n",
        "    template = \"\"\"<button id=\"{callback_id}\" style=\"cursor:pointer;background-color:#EEEEEE;border-color:#E0E0E0;padding:5px 15px;font-size:14px\">{title}</button>\n",
        "        <script>\n",
        "          document.querySelector(\"#{callback_id}\").onclick = (e) => {{\n",
        "            google.colab.kernel.invokeFunction('{callback_id}', [], {{}})\n",
        "            e.preventDefault();\n",
        "          }};\n",
        "        </script>\"\"\"\n",
        "    html = template.format(title=self._title, callback_id=callback_id)\n",
        "    return html\n",
        "\n",
        "RECORD = \"\"\"\n",
        "const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\n",
        "const b2text = blob => new Promise(resolve => {\n",
        "  const reader = new FileReader()\n",
        "  reader.onloadend = e => resolve(e.srcElement.result)\n",
        "  reader.readAsDataURL(blob)\n",
        "})\n",
        "var record = time => new Promise(async resolve => {\n",
        "  stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n",
        "  recorder = new MediaRecorder(stream)\n",
        "  chunks = []\n",
        "  recorder.ondataavailable = e => chunks.push(e.data)\n",
        "  recorder.start()\n",
        "  await sleep(time)\n",
        "  recorder.onstop = async ()=>{\n",
        "    blob = new Blob(chunks)\n",
        "    text = await b2text(blob)\n",
        "    resolve(text)\n",
        "  }\n",
        "  recorder.stop()\n",
        "})\n",
        "\"\"\"\n",
        "\n",
        "def record(sec=3):\n",
        "  display(Javascript(RECORD))\n",
        "  s = output.eval_js('record(%d)' % (sec*1000))\n",
        "  b = b64decode(s.split(',')[1])\n",
        "  with open('audio.wav','wb+') as f:\n",
        "    f.write(b)\n",
        "  return 'audio.wav'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcxVTJDT2udB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "outputId": "a499f752-1788-48f5-9151-e66686965ee6"
      },
      "source": [
        "# To determine which version you're using:\n",
        "!pip show tensorflow\n",
        "# # For the current version: \n",
        "# !pip install --upgrade tensorflow\n",
        "# For a specific version:\n",
        "# !pip install tensorflow==1.2\n",
        "# !pip install tensorflow==1.15.0\n",
        "!pip install tensorflow==1.14.0"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name: tensorflow\n",
            "Version: 1.15.0\n",
            "Summary: TensorFlow is an open source machine learning framework for everyone.\n",
            "Home-page: https://www.tensorflow.org/\n",
            "Author: Google Inc.\n",
            "Author-email: packages@tensorflow.org\n",
            "License: Apache 2.0\n",
            "Location: /usr/local/lib/python3.6/dist-packages\n",
            "Requires: tensorboard, wheel, google-pasta, numpy, keras-preprocessing, absl-py, astor, opt-einsum, tensorflow-estimator, protobuf, termcolor, keras-applications, wrapt, six, grpcio, gast\n",
            "Required-by: fancyimpute\n",
            "Collecting tensorflow==1.14.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/f0/96fb2e0412ae9692dbf400e5b04432885f677ad6241c088ccc5fe7724d69/tensorflow-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (109.2MB)\n",
            "\u001b[K     |████████████████████████████████| 109.2MB 103kB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.8.1)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.2.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.18.5)\n",
            "Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)\n",
            "\u001b[K     |████████████████████████████████| 491kB 32.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.30.0)\n",
            "Collecting tensorboard<1.15.0,>=1.14.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 39.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.9.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.34.2)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.0.8)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.2.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.15.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.2)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.12.1)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (3.12.2)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (49.1.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.2.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.14.0) (2.10.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (1.7.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.1.0)\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 1.15.1\n",
            "    Uninstalling tensorflow-estimator-1.15.1:\n",
            "      Successfully uninstalled tensorflow-estimator-1.15.1\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "  Found existing installation: tensorflow 1.15.0\n",
            "    Uninstalling tensorflow-1.15.0:\n",
            "      Successfully uninstalled tensorflow-1.15.0\n",
            "Successfully installed tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvX2gbNT2vSd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "5037a299-31c2-45f2-ef7e-6beb8878239d"
      },
      "source": [
        "!pip install unidecode \n",
        "!pip install webrtcvad\n",
        "from IPython.display import Audio\n",
        "from IPython.utils import io\n",
        "from synthesizer.inference import Synthesizer\n",
        "from encoder import inference as encoder\n",
        "from vocoder import inference as vocoder\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import librosa\n",
        "encoder_weights = Path(\"encoder/saved_models/pretrained.pt\")\n",
        "vocoder_weights = Path(\"vocoder/saved_models/pretrained/pretrained.pt\")\n",
        "syn_dir = Path(\"synthesizer/saved_models/logs-pretrained/taco_pretrained\")\n",
        "encoder.load_model(encoder_weights)\n",
        "synthesizer = Synthesizer(syn_dir)\n",
        "vocoder.load_model(vocoder_weights)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: unidecode in /usr/local/lib/python3.6/dist-packages (1.1.1)\n",
            "Collecting webrtcvad\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/89/34/e2de2d97f3288512b9ea56f92e7452f8207eb5a0096500badf9dfd48f5e6/webrtcvad-2.0.10.tar.gz (66kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 2.3MB/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-cp36-cp36m-linux_x86_64.whl size=71237 sha256=efe6e1f9cab69b35f02fef6e1e034075e9d10a72c9d578201d8934dbd63cb558\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/2a/18/bd1aec41cac7c3051fe95d92a6ed446122ea31dc713c432fa1\n",
            "Successfully built webrtcvad\n",
            "Installing collected packages: webrtcvad\n",
            "Successfully installed webrtcvad-2.0.10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loaded encoder \"pretrained.pt\" trained to step 1564501\n",
            "Found synthesizer \"pretrained\" trained to step 278000\n",
            "Building Wave-RNN\n",
            "Trainable Parameters: 4.481M\n",
            "Loading model weights at vocoder/saved_models/pretrained/pretrained.pt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0nFu9JZ4LuN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "d2708d21-9ec3-4106-b760-3aa33fb11a46"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "audio.wav\t\t   requirements.txt\n",
            "demo_cli.py\t\t   synthesizer\n",
            "demo_toolbox_collab.ipynb  synthesizer_preprocess_audio.py\n",
            "demo_toolbox.py\t\t   synthesizer_preprocess_embeds.py\n",
            "encoder\t\t\t   synthesizer_train.py\n",
            "encoder_preprocess.py\t   toolbox\n",
            "encoder_train.py\t   utils\n",
            "LICENSE.txt\t\t   vocoder\n",
            "pretrained.zip\t\t   vocoder_preprocess.py\n",
            "README.md\t\t   vocoder_train.py\n",
            "requirements_gpu.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVLGpCHGvLrt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HbTCs0jvNEt",
        "colab_type": "text"
      },
      "source": [
        "# Two options \n",
        "if u have a .wav file you can do next 4 cells else go to the next section if u want to generate in live with u r audio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPJmCP8O5XcC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "5365cd7a-611d-45e4-c15f-d67e2baabf4a"
      },
      "source": [
        "!wget https://s116.123apps.com/aconv/d/s116akdR1hiH_mp3_8sw6gtZb.wav"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-23 17:21:56--  https://s116.123apps.com/aconv/d/s116akdR1hiH_mp3_8sw6gtZb.wav\n",
            "Resolving s116.123apps.com (s116.123apps.com)... 78.46.41.119\n",
            "Connecting to s116.123apps.com (s116.123apps.com)|78.46.41.119|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 41038098 (39M) [application/octet-stream]\n",
            "Saving to: ‘s116akdR1hiH_mp3_8sw6gtZb.wav’\n",
            "\n",
            "s116akdR1hiH_mp3_8s 100%[===================>]  39.14M  18.7MB/s    in 2.1s    \n",
            "\n",
            "2020-07-23 17:21:59 (18.7 MB/s) - ‘s116akdR1hiH_mp3_8sw6gtZb.wav’ saved [41038098/41038098]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zyE-BPEsmSH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv s116akdR1hiH_mp3_8sw6gtZb.wav audio.wav"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o60uF0ujspXi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDqlUFJc-Lks",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "57198393-584b-4f42-e4de-63c328f66e8a"
      },
      "source": [
        "#!wget https://s116.123apps.com/aconv/d/s1160eNbEElT_mp3_5MjyKRtz.wav #ritika sighn"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-23 17:32:40--  https://s116.123apps.com/aconv/d/s1160eNbEElT_mp3_5MjyKRtz.wav\n",
            "Resolving s116.123apps.com (s116.123apps.com)... 78.46.41.119\n",
            "Connecting to s116.123apps.com (s116.123apps.com)|78.46.41.119|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 63186594 (60M) [application/octet-stream]\n",
            "Saving to: ‘s1160eNbEElT_mp3_5MjyKRtz.wav’\n",
            "\n",
            "s1160eNbEElT_mp3_5M 100%[===================>]  60.26M  20.5MB/s    in 2.9s    \n",
            "\n",
            "2020-07-23 17:32:43 (20.5 MB/s) - ‘s1160eNbEElT_mp3_5MjyKRtz.wav’ saved [63186594/63186594]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJrZchtX5a0u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !mv s1160eNbEElT_mp3_5MjyKRtz.wav audio.wav"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lThNu3X565OZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "60d72273-6077-4bb1-b949-51ceca06c18b"
      },
      "source": [
        "#!ls -sh audio.wav "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "59M audio.wav\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwOqt8rlr-J1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "a75d22e1-7f3a-479f-e4bf-450097ce8876"
      },
      "source": [
        "#!wget https://s108.123apps.com/aconv/d/s1087NkuYeUT_mp3_oA9lZiiF.wav #rohan sharavan"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-23 17:42:12--  https://s108.123apps.com/aconv/d/s1087NkuYeUT_mp3_oA9lZiiF.wav\n",
            "Resolving s108.123apps.com (s108.123apps.com)... 88.99.96.46\n",
            "Connecting to s108.123apps.com (s108.123apps.com)|88.99.96.46|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 136983718 (131M) [application/octet-stream]\n",
            "Saving to: ‘s1087NkuYeUT_mp3_oA9lZiiF.wav’\n",
            "\n",
            "s1087NkuYeUT_mp3_oA 100%[===================>] 130.64M  25.3MB/s    in 5.7s    \n",
            "\n",
            "2020-07-23 17:42:18 (22.9 MB/s) - ‘s1087NkuYeUT_mp3_oA9lZiiF.wav’ saved [136983718/136983718]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYtkV46IxMpq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!mv s1087NkuYeUT_mp3_oA9lZiiF.wav audio.wav"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tz-uw42sAHO",
        "colab_type": "text"
      },
      "source": [
        "# Instruction \n",
        "when u run below cell it will ask you to click \"start recording\" . Please click it and wait for some time speak some thing to it . after 20 or 30seconds it will give u  outputs . ( both male and female voices supported)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xh74tIRBqcYf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# please add within 12 words .. and if u want to generate more save the generated audio in another name and then generate another audio"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TjzdEmv23pl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "d35ead7d-2372-44bc-bcb8-4385e1f5253a"
      },
      "source": [
        "def synth():\n",
        "  text = \"Thank you Elon musk  Thank you Steve jobs  Thanks Narendra Modi Have a good day\" #@param {type:\"string\"}\n",
        "  print(\"Now recording for 10 seconds, say what you will...\")\n",
        "  \n",
        "  ### record\n",
        "  # record(30)\n",
        "  # print(\"Audio recording complete\")\n",
        "  in_fpath = Path(\"audio.wav\")\n",
        "  reprocessed_wav = encoder.preprocess_wav(in_fpath)\n",
        "  original_wav, sampling_rate = librosa.load(in_fpath)\n",
        "  preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)\n",
        "  embed = encoder.embed_utterance(preprocessed_wav)\n",
        "  print(\"Synthesizing new audio...\")\n",
        "  with io.capture_output() as captured:\n",
        "    specs = synthesizer.synthesize_spectrograms([text], [embed])\n",
        "  generated_wav = vocoder.infer_waveform(specs[0])\n",
        "  generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode=\"constant\")\n",
        "  display(Audio(generated_wav, rate=synthesizer.sample_rate))\n",
        "#InvokeButton('Start recording', synth)\n",
        "synth()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Now recording for 10 seconds, say what you will...\n",
            "Synthesizing new audio...\n",
            "{| ███████████████░ 78300/86400 | Batch Size: 9 | Gen Rate: 6.3kHz | }"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEZMA-cd2v_m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40Az5h-F2wNc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWK0D9aU2wbQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uaBbVJCs2wp6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWyj8ZKQ2w2F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqotncdy2cgW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIcesKx42crM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAQXJcch2c3Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}