AI_Song_Cover_SOVITS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#@title 0. Check GPU\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "J5ES3aCa503T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "DM9Y_tjiA2Yf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 1. Install Library for Youtube WAV Download\n",
        "!pip install yt_dlp\n",
        "!pip install ffmpeg\n",
        "!mkdir youtubeaudio\n"
      ],
      "metadata": {
        "id": "td-Hhq5n6SFj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "doHo0Gzb5ePk"
      },
      "outputs": [],
      "source": [
        "#@title Download Youtube WAV\n",
        "from __future__ import unicode_literals\n",
        "import yt_dlp\n",
        "import ffmpeg\n",
        "import sys\n",
        "\n",
        "ydl_opts = {\n",
        "    'format': 'bestaudio/best',\n",
        "#    'outtmpl': 'output.%(ext)s',\n",
        "    'postprocessors': [{\n",
        "        'key': 'FFmpegExtractAudio',\n",
        "        'preferredcodec': 'wav',\n",
        "    }],\n",
        "    \"outtmpl\": 'youtubeaudio/audio',  # this is where you can edit how you'd like the filenames to be formatted\n",
        "}\n",
        "def download_from_url(url):\n",
        "    ydl.download([url])\n",
        "    # stream = ffmpeg.input('output.m4a')\n",
        "    # stream = ffmpeg.output(stream, 'output.wav')\n",
        "\n",
        "\n",
        "with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n",
        "      url = \"https://www.youtube.com/watch?v=cQGfLDnmWS8&pp=ygULYXNtYWxpYnJhc2k%3D\" #@param {type:\"string\"}\n",
        "      download_from_url(url)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 2. Install Demucs for Separating Audio\n",
        "!python3 -m pip install -U demucs"
      ],
      "metadata": {
        "id": "-RGvSkYv9_aL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Separate Vocal and Instrument/Noise using Demucs\n",
        "import subprocess\n",
        "AUDIO_INPUT = \"/content/youtubeaudio/audio.wav\" #@param {type:\"string\"}\n",
        "\n",
        "command = f\"demucs --two-stems=vocals {AUDIO_INPUT}\"\n",
        "result = subprocess.run(command.split(), stdout=subprocess.PIPE)\n",
        "print(result.stdout.decode())"
      ],
      "metadata": {
        "id": "cZidoluC-EZ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 3. Split The Audio into Smaller Duration Before Training\n",
        "SPEAKER_NAME = \"Hutao\" #@param {type:\"string\"}\n",
        "!mkdir -p dataset_raw/{SPEAKER_NAME}"
      ],
      "metadata": {
        "id": "texSOl1mEaNL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from scipy.io import wavfile\n",
        "import os\n",
        "import numpy as np\n",
        "import argparse\n",
        "from tqdm import tqdm\n",
        "import json\n",
        "\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "# Utility functions\n",
        "\n",
        "def GetTime(video_seconds):\n",
        "\n",
        "    if (video_seconds < 0) :\n",
        "        return 00\n",
        "\n",
        "    else:\n",
        "        sec = timedelta(seconds=float(video_seconds))\n",
        "        d = datetime(1,1,1) + sec\n",
        "\n",
        "        instant = str(d.hour).zfill(2) + ':' + str(d.minute).zfill(2) + ':' + str(d.second).zfill(2) + str('.001')\n",
        "    \n",
        "        return instant\n",
        "\n",
        "def GetTotalTime(video_seconds):\n",
        "\n",
        "    sec = timedelta(seconds=float(video_seconds))\n",
        "    d = datetime(1,1,1) + sec\n",
        "    delta = str(d.hour) + ':' + str(d.minute) + \":\" + str(d.second)\n",
        "    \n",
        "    return delta\n",
        "\n",
        "def windows(signal, window_size, step_size):\n",
        "    if type(window_size) is not int:\n",
        "        raise AttributeError(\"Window size must be an integer.\")\n",
        "    if type(step_size) is not int:\n",
        "        raise AttributeError(\"Step size must be an integer.\")\n",
        "    for i_start in range(0, len(signal), step_size):\n",
        "        i_end = i_start + window_size\n",
        "        if i_end >= len(signal):\n",
        "            break\n",
        "        yield signal[i_start:i_end]\n",
        "\n",
        "def energy(samples):\n",
        "    return np.sum(np.power(samples, 2.)) / float(len(samples))\n",
        "\n",
        "def rising_edges(binary_signal):\n",
        "    previous_value = 0\n",
        "    index = 0\n",
        "    for x in binary_signal:\n",
        "        if x and not previous_value:\n",
        "            yield index\n",
        "        previous_value = x\n",
        "        index += 1\n",
        "\n",
        "'''\n",
        "Last Acceptable Values\n",
        "\n",
        "min_silence_length = 0.3\n",
        "silence_threshold = 1e-3\n",
        "step_duration = 0.03/10\n",
        "\n",
        "'''\n",
        "# Change the arguments and the input file here\n",
        "input_file = \"/content/separated/htdemucs/audio/vocals.wav\" #@param {type:\"string\"}\n",
        "output_dir = f\"/content/dataset_raw/{SPEAKER_NAME}\"\n",
        "min_silence_length = 0.6  # The minimum length of silence at which a split may occur [seconds]. Defaults to 3 seconds.\n",
        "silence_threshold = 1e-4  # The energy level (between 0.0 and 1.0) below which the signal is regarded as silent.\n",
        "step_duration = 0.03/10   # The amount of time to step forward in the input file after calculating energy. Smaller value = slower, but more accurate silence detection. Larger value = faster, but might miss some split opportunities. Defaults to (min-silence-length / 10.).\n",
        "\n",
        "\n",
        "input_filename = input_file\n",
        "window_duration = min_silence_length\n",
        "if step_duration is None:\n",
        "    step_duration = window_duration / 10.\n",
        "else:\n",
        "    step_duration = step_duration\n",
        "\n",
        "output_filename_prefix = os.path.splitext(os.path.basename(input_filename))[0]\n",
        "dry_run = False\n",
        "\n",
        "print(\"Splitting {} where energy is below {}% for longer than {}s.\".format(\n",
        "    input_filename,\n",
        "    silence_threshold * 100.,\n",
        "    window_duration\n",
        "    )\n",
        ")\n",
        "\n",
        "# Read and split the file\n",
        "\n",
        "sample_rate, samples = input_data=wavfile.read(filename=input_filename, mmap=True)\n",
        "\n",
        "max_amplitude = np.iinfo(samples.dtype).max\n",
        "print(max_amplitude)\n",
        "\n",
        "max_energy = energy([max_amplitude])\n",
        "print(max_energy)\n",
        "\n",
        "window_size = int(window_duration * sample_rate)\n",
        "step_size = int(step_duration * sample_rate)\n",
        "\n",
        "signal_windows = windows(\n",
        "    signal=samples,\n",
        "    window_size=window_size,\n",
        "    step_size=step_size\n",
        ")\n",
        "\n",
        "window_energy = (energy(w) / max_energy for w in tqdm(\n",
        "    signal_windows,\n",
        "    total=int(len(samples) / float(step_size))\n",
        "))\n",
        "\n",
        "window_silence = (e > silence_threshold for e in window_energy)\n",
        "\n",
        "cut_times = (r * step_duration for r in rising_edges(window_silence))\n",
        "\n",
        "# This is the step that takes long, since we force the generators to run.\n",
        "print(\"Finding silences...\")\n",
        "cut_samples = [int(t * sample_rate) for t in cut_times]\n",
        "cut_samples.append(-1)\n",
        "\n",
        "cut_ranges = [(i, cut_samples[i], cut_samples[i+1]) for i in range(len(cut_samples) - 1)]\n",
        "\n",
        "video_sub = {str(i) : [str(GetTime(((cut_samples[i])/sample_rate))), \n",
        "                       str(GetTime(((cut_samples[i+1])/sample_rate)))] \n",
        "             for i in range(len(cut_samples) - 1)}\n",
        "\n",
        "for i, start, stop in tqdm(cut_ranges):\n",
        "    output_file_path = \"{}_{:03d}.wav\".format(\n",
        "        os.path.join(output_dir, output_filename_prefix),\n",
        "        i\n",
        "    )\n",
        "    if not dry_run:\n",
        "        print(\"Writing file {}\".format(output_file_path))\n",
        "        wavfile.write(\n",
        "            filename=output_file_path,\n",
        "            rate=sample_rate,\n",
        "            data=samples[start:stop]\n",
        "        )\n",
        "    else:\n",
        "        print(\"Not writing file {}\".format(output_file_path))\n",
        "        \n",
        "with open (output_dir+'\\\\'+output_filename_prefix+'.json', 'w') as output:\n",
        "    json.dump(video_sub, output)"
      ],
      "metadata": {
        "id": "taNJV4l-_ZZt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 4. Install dependencies for Training\n",
        "#@markdown makesure there are no \"so-vits-svc-fork\" folder on your Drive before you run this\n",
        "!python -m pip install -U pip wheel\n",
        "%pip install -U ipython\n",
        "%pip install -U so-vits-svc-fork\n",
        "!mkdir drive/MyDrive/so-vits-svc-fork"
      ],
      "metadata": {
        "id": "RWk6EKflAvAS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Automatic preprocessing\n",
        "!svc pre-resample"
      ],
      "metadata": {
        "id": "sD6Rmv7SD24X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!svc pre-config"
      ],
      "metadata": {
        "id": "IiM26DmFD5Of"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Copy configs file\n",
        "!cp configs/44k/config.json drive/MyDrive/so-vits-svc-fork"
      ],
      "metadata": {
        "id": "S-2xib_3D7Oy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "F0_METHOD = \"dio\" #@param [\"crepe\", \"crepe-tiny\", \"parselmouth\", \"dio\", \"harvest\"]\n",
        "!svc pre-hubert -fm {F0_METHOD}"
      ],
      "metadata": {
        "id": "odKQPmXiD8gz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Train\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir drive/MyDrive/so-vits-svc-fork/logs/44k\n",
        "!svc train --model-path drive/MyDrive/so-vits-svc-fork/logs/44k"
      ],
      "metadata": {
        "id": "C1NukdvBEBNm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 5. Inference\n",
        "#@markdown remove \".wav\" on AUDIO\n",
        "from IPython.display import Audio\n",
        "\n",
        "AUDIO = \"/content/separated/htdemucs/audio/vocals\" #@param {type:\"string\"}\n",
        "MODEL = \"/content/drive/MyDrive/so-vits-svc-fork/logs/44k/G_178.pth\" #@param {type:\"string\"}\n",
        "CONFIG = \"/content/drive/MyDrive/so-vits-svc-fork/logs/44k/config.json\" #@param {type:\"string\"}\n",
        "#@markdown Change According to Your Voice Tone. 12 = 1 Octave | -12 = -1 Octave\n",
        "PITCH = 0 #@param {type:\"integer\"}\n",
        "\n",
        "!svc infer {AUDIO}.wav -c {CONFIG} -m {MODEL} -na -t {PITCH}\n",
        "# Try comment this line below if you got Runtime Error\n",
        "try:\n",
        "display(Audio(f\"{AUDIO}.out.wav\", autoplay=True))\n",
        "except Exception as e:"
        "print(\"Error:\", str(e))\n",
      ],
      "metadata": {
        "id": "dLDnmZ9pH0mG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 5.1 Inference Using Pretrained Model"
      ],
      "metadata": {
        "id": "pHwuY3NH73n7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title TachibanaKimika - https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models (Hololive Model Deleted)\n",
        "!mkdir so-vits-test\n",
        "!wget -N \"https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models/resolve/main/riri/G_riri_220.pth\" -P so-vits-test/\n",
        "!wget -N \"https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models/resolve/main/riri/config.json\" -P so-vits-test/"
      ],
      "metadata": {
        "id": "MAEujsmx7-U8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30ceb002-89a9-485e-ab40-05a905388bb0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-04-25 18:34:44--  https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models/resolve/main/riri/G_riri_220.pth\n",
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            "Location: https://cdn-lfs.huggingface.co/repos/29/9d/299dc503dd70e9f70e46b2f76f123a3c54dc7cf3016592b42bbbd32fef581441/ea35e1be6406dee11d7c05e540aec4f63fe41eea0ca76112234e692fef9314b0?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27G_riri_220.pth%3B+filename%3D%22G_riri_220.pth%22%3B&Expires=1682706760&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI5LzlkLzI5OWRjNTAzZGQ3MGU5ZjcwZTQ2YjJmNzZmMTIzYTNjNTRkYzdjZjMwMTY1OTJiNDJiYmJkMzJmZWY1ODE0NDEvZWEzNWUxYmU2NDA2ZGVlMTFkN2MwNWU1NDBhZWM0ZjYzZmU0MWVlYTBjYTc2MTEyMjM0ZTY5MmZlZjkzMTRiMD9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2ODI3MDY3NjB9fX1dfQ__&Signature=r9skSdxXYF24-O8AIg1JosN-UFrYppnBqAr3ril5viti9jSiNIUEhv3sqKefauEosYZadERfftqU%7ENPA4gRuy8HP8TBnyzULh8lZcfzH9u9yokBWaoa7rJYFp70NgW0QKYuRINa%7EOMkncgsyDb6AVVeYJVY5K-uB0tICXVWdCgTtmLpPwC4N6Ddyugk83zv3WM0mtrGeMvN8rOZKrNAmp8kwdRaWeXLADDSFYZ1d%7EjJGfL8L5S9a4wbI98Rzv1uJPyrS-NdPBuqPNHq7OM-OjaVBKYLyp9629EFMXP9tE1QVsSQUXsW2iTGl%7Ew1OeBrax7PljxHe3lEc3sWM3PsLqg__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
            "--2023-04-25 18:34:45--  https://cdn-lfs.huggingface.co/repos/29/9d/299dc503dd70e9f70e46b2f76f123a3c54dc7cf3016592b42bbbd32fef581441/ea35e1be6406dee11d7c05e540aec4f63fe41eea0ca76112234e692fef9314b0?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27G_riri_220.pth%3B+filename%3D%22G_riri_220.pth%22%3B&Expires=1682706760&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI5LzlkLzI5OWRjNTAzZGQ3MGU5ZjcwZTQ2YjJmNzZmMTIzYTNjNTRkYzdjZjMwMTY1OTJiNDJiYmJkMzJmZWY1ODE0NDEvZWEzNWUxYmU2NDA2ZGVlMTFkN2MwNWU1NDBhZWM0ZjYzZmU0MWVlYTBjYTc2MTEyMjM0ZTY5MmZlZjkzMTRiMD9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2ODI3MDY3NjB9fX1dfQ__&Signature=r9skSdxXYF24-O8AIg1JosN-UFrYppnBqAr3ril5viti9jSiNIUEhv3sqKefauEosYZadERfftqU%7ENPA4gRuy8HP8TBnyzULh8lZcfzH9u9yokBWaoa7rJYFp70NgW0QKYuRINa%7EOMkncgsyDb6AVVeYJVY5K-uB0tICXVWdCgTtmLpPwC4N6Ddyugk83zv3WM0mtrGeMvN8rOZKrNAmp8kwdRaWeXLADDSFYZ1d%7EjJGfL8L5S9a4wbI98Rzv1uJPyrS-NdPBuqPNHq7OM-OjaVBKYLyp9629EFMXP9tE1QVsSQUXsW2iTGl%7Ew1OeBrax7PljxHe3lEc3sWM3PsLqg__&Key-Pair-Id=KVTP0A1DKRTAX\n",
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            "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|13.35.7.14|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 542789405 (518M) [application/zip]\n",
            "Saving to: ‘so-vits-test/G_riri_220.pth’\n",
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            "G_riri_220.pth      100%[===================>] 517.64M  94.4MB/s    in 5.5s    \n",
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            "2023-04-25 18:34:50 (94.6 MB/s) - ‘so-vits-test/G_riri_220.pth’ saved [542789405/542789405]\n",
            "\n",
            "--2023-04-25 18:34:50--  https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models/resolve/main/riri/config.json\n",
            "Resolving huggingface.co (huggingface.co)... 13.35.7.13, 13.35.7.28, 13.35.7.88, ...\n",
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            "Length: 1569 (1.5K) [text/plain]\n",
            "Saving to: ‘so-vits-test/config.json’\n",
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            "config.json         100%[===================>]   1.53K  --.-KB/s    in 0s      \n",
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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 5.3 Combine Vocal and Instrument (Song Cover)\n",
        "from pydub import AudioSegment\n",
        "\n",
        "VOCAL = \"/content/separated/htdemucs/audio/vocals.out.wav\" #@param {type:\"string\"}\n",
        "INSTRUMENT = \"/content/separated/htdemucs/audio/no_vocals.wav\" #@param {type:\"string\"}\n",
        "\n",
        "sound1 = AudioSegment.from_file(VOCAL)\n",
        "sound2 = AudioSegment.from_file(INSTRUMENT)\n",
        "\n",
        "combined = sound1.overlay(sound2)\n",
        "\n",
        "combined.export(\"/content/FinalCover.wav\", format='wav')"
      ],
      "metadata": {
        "id": "w781LsPh_2Xf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 6. Additional : Audio Recording\n",
        "!pip install ffmpeg-python"
      ],
      "metadata": {
        "id": "In4WzV9iMVp4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "To write this piece of code I took inspiration/code from a lot of places.\n",
        "It was late night, so I'm not sure how much I created or just copied o.O\n",
        "Here are some of the possible references:\n",
        "https://blog.addpipe.com/recording-audio-in-the-browser-using-pure-html5-and-minimal-javascript/\n",
        "https://stackoverflow.com/a/18650249\n",
        "https://hacks.mozilla.org/2014/06/easy-audio-capture-with-the-mediarecorder-api/\n",
        "https://air.ghost.io/recording-to-an-audio-file-using-html5-and-js/\n",
        "https://stackoverflow.com/a/49019356\n",
        "\"\"\"\n",
        "from IPython.display import HTML, Audio\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "import numpy as np\n",
        "from scipy.io.wavfile import read as wav_read\n",
        "import io\n",
        "import ffmpeg\n",
        "\n",
        "AUDIO_HTML = \"\"\"\n",
        "<script>\n",
        "var my_div = document.createElement(\"DIV\");\n",
        "var my_p = document.createElement(\"P\");\n",
        "var my_btn = document.createElement(\"BUTTON\");\n",
        "var t = document.createTextNode(\"Press to start recording\");\n",
        "\n",
        "my_btn.appendChild(t);\n",
        "//my_p.appendChild(my_btn);\n",
        "my_div.appendChild(my_btn);\n",
        "document.body.appendChild(my_div);\n",
        "\n",
        "var base64data = 0;\n",
        "var reader;\n",
        "var recorder, gumStream;\n",
        "var recordButton = my_btn;\n",
        "\n",
        "var handleSuccess = function(stream) {\n",
        "  gumStream = stream;\n",
        "  var options = {\n",
        "    //bitsPerSecond: 8000, //chrome seems to ignore, always 48k\n",
        "    mimeType : 'audio/webm;codecs=opus'\n",
        "    //mimeType : 'audio/webm;codecs=pcm'\n",
        "  };            \n",
        "  //recorder = new MediaRecorder(stream, options);\n",
        "  recorder = new MediaRecorder(stream);\n",
        "  recorder.ondataavailable = function(e) {            \n",
        "    var url = URL.createObjectURL(e.data);\n",
        "    var preview = document.createElement('audio');\n",
        "    preview.controls = true;\n",
        "    preview.src = url;\n",
        "    document.body.appendChild(preview);\n",
        "\n",
        "    reader = new FileReader();\n",
        "    reader.readAsDataURL(e.data); \n",
        "    reader.onloadend = function() {\n",
        "      base64data = reader.result;\n",
        "      //console.log(\"Inside FileReader:\" + base64data);\n",
        "    }\n",
        "  };\n",
        "  recorder.start();\n",
        "  };\n",
        "\n",
        "recordButton.innerText = \"Recording... press to stop\";\n",
        "\n",
        "navigator.mediaDevices.getUserMedia({audio: true}).then(handleSuccess);\n",
        "\n",
        "\n",
        "function toggleRecording() {\n",
        "  if (recorder && recorder.state == \"recording\") {\n",
        "      recorder.stop();\n",
        "      gumStream.getAudioTracks()[0].stop();\n",
        "      recordButton.innerText = \"Saving the recording... pls wait!\"\n",
        "  }\n",
        "}\n",
        "\n",
        "// https://stackoverflow.com/a/951057\n",
        "function sleep(ms) {\n",
        "  return new Promise(resolve => setTimeout(resolve, ms));\n",
        "}\n",
        "\n",
        "var data = new Promise(resolve=>{\n",
        "//recordButton.addEventListener(\"click\", toggleRecording);\n",
        "recordButton.onclick = ()=>{\n",
        "toggleRecording()\n",
        "\n",
        "sleep(2000).then(() => {\n",
        "  // wait 2000ms for the data to be available...\n",
        "  // ideally this should use something like await...\n",
        "  //console.log(\"Inside data:\" + base64data)\n",
        "  resolve(base64data.toString())\n",
        "\n",
        "});\n",
        "\n",
        "}\n",
        "});\n",
        "      \n",
        "</script>\n",
        "\"\"\"\n",
        "\n",
        "def get_audio():\n",
        "  display(HTML(AUDIO_HTML))\n",
        "  data = eval_js(\"data\")\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  \n",
        "  process = (ffmpeg\n",
        "    .input('pipe:0')\n",
        "    .output('pipe:1', format='wav')\n",
        "    .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True, quiet=True, overwrite_output=True)\n",
        "  )\n",
        "  output, err = process.communicate(input=binary)\n",
        "  \n",
        "  riff_chunk_size = len(output) - 8\n",
        "  # Break up the chunk size into four bytes, held in b.\n",
        "  q = riff_chunk_size\n",
        "  b = []\n",
        "  for i in range(4):\n",
        "      q, r = divmod(q, 256)\n",
        "      b.append(r)\n",
        "\n",
        "  # Replace bytes 4:8 in proc.stdout with the actual size of the RIFF chunk.\n",
        "  riff = output[:4] + bytes(b) + output[8:]\n",
        "\n",
        "  sr, audio = wav_read(io.BytesIO(riff))\n",
        "\n",
        "  return audio, sr"
      ],
      "metadata": {
        "id": "0pFo47QTMMX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "audio, sr = get_audio()"
      ],
      "metadata": {
        "id": "hqcCW1h-NUB4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.io import wavfile\n",
        "wavfile.write(\"my_audio.wav\", sr, audio)"
      ],
      "metadata": {
        "id": "IakSo65tOPme"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}