DEEPFAKE_AUDIO.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DEEPFAKE-AUDIO.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iameythakur/DEEPFAKE-AUDIO/blob/main/DEEPFAKE_AUDIO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhDehA7sT-Gx"
      },
      "source": [
        "# DEEPFAKE-AUDIO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfkTM9TjUCRx"
      },
      "source": [
        "# @markdown STEPS\n",
        "\n",
        "#@markdown 1 Clone The Project\n",
        "\n",
        "#@markdown 2 Download Pretrained Models\n",
        "\n",
        "#@markdown 3 Initialize The Voice Cloning Models\n",
        "\n",
        "%tensorflow_version 1.x\n",
        "import os\n",
        "from os.path import exists, join, basename, splitext\n",
        "\n",
        "git_repo_url = 'https://github.com/CorentinJ/Real-Time-Voice-Cloning.git'\n",
        "project_name = splitext(basename(git_repo_url))[0]\n",
        "if not exists(project_name):\n",
        "  # clone and install\n",
        "  !git clone -q --recursive {git_repo_url}\n",
        "  # install dependencies\n",
        "  !cd {project_name} && pip install -q -r requirements.txt\n",
        "  !pip install -q gdown\n",
        "  !apt-get install -qq libportaudio2\n",
        "  !pip install -q https://github.com/tugstugi/dl-colab-notebooks/archive/colab_utils.zip\n",
        "\n",
        "  # download pretrained model\n",
        "  !cd {project_name} && wget https://github.com/blue-fish/Real-Time-Voice-Cloning/releases/download/v1.0/pretrained.zip && unzip -o pretrained.zip\n",
        "\n",
        "import sys\n",
        "sys.path.append(project_name)\n",
        "\n",
        "from IPython.display import display, Audio, clear_output\n",
        "from IPython.utils import io\n",
        "import ipywidgets as widgets\n",
        "import numpy as np\n",
        "from dl_colab_notebooks.audio import record_audio, upload_audio\n",
        "\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",
        "\n",
        "encoder.load_model(project_name / Path(\"encoder/saved_models/pretrained.pt\"))\n",
        "synthesizer = Synthesizer(project_name / Path(\"synthesizer/saved_models/pretrained/pretrained.pt\"))\n",
        "vocoder.load_model(project_name / Path(\"vocoder/saved_models/pretrained/pretrained.pt\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBeMoBxLkDKN"
      },
      "source": [
        "#@title RECORD OR UPLOAD\n",
        "#@markdown * Either Record Audio from Microphone or Upload Audio from File (.mp3 or .wav) \n",
        "\n",
        "SAMPLE_RATE = 22050\n",
        "record_or_upload = \"Record\" #@param [\"Record\", \"Upload (.mp3 or .wav)\"]\n",
        "record_seconds =   10#@param {type:\"number\", min:1, max:10, step:1}\n",
        "\n",
        "embedding = None\n",
        "def _compute_embedding(audio):\n",
        "  display(Audio(audio, rate=SAMPLE_RATE, autoplay=True))\n",
        "  global embedding\n",
        "  embedding = None\n",
        "  embedding = encoder.embed_utterance(encoder.preprocess_wav(audio, SAMPLE_RATE))\n",
        "def _record_audio(b):\n",
        "  clear_output()\n",
        "  audio = record_audio(record_seconds, sample_rate=SAMPLE_RATE)\n",
        "  _compute_embedding(audio)\n",
        "def _upload_audio(b):\n",
        "  clear_output()\n",
        "  audio = upload_audio(sample_rate=SAMPLE_RATE)\n",
        "  _compute_embedding(audio)\n",
        "\n",
        "if record_or_upload == \"Record\":\n",
        "  button = widgets.Button(description=\"Record Your Voice\")\n",
        "  button.on_click(_record_audio)\n",
        "  display(button)\n",
        "else:\n",
        "  #button = widgets.Button(description=\"Upload Voice File\")\n",
        "  #button.on_click(_upload_audio)\n",
        "  _upload_audio(\"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZjKkvGF1Y-i"
      },
      "source": [
        "#@title SYNTHESIZE A TEXT { run: \"auto\" }\n",
        "text = \"A falling star or a shooting star has nothing at all to do with a star! These amazing streaks of light you can sometimes see in the night sky are caused by tiny bits of dust and rock called meteoroids falling into the Earth's atmosphere and burning up.\" #@param {type:\"string\"}\n",
        "  \n",
        "def synthesize(embed, text):\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",
        "  clear_output()\n",
        "  display(Audio(generated_wav, rate=synthesizer.sample_rate, autoplay=True))\n",
        "\n",
        "if embedding is None:\n",
        "  print(\"first record a voice or upload a voice file!\")\n",
        "else:\n",
        "  synthesize(embedding, text)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}