OpenAI-Whisper-ChatGPTAPI-Audio-Output-Notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvi6cAEHY5sh"
      },
      "source": [
        "# Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZsJUxc0aRsAf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98042512-9122-45b6-cee2-829d8e447d74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/openai/whisper.git\n",
        "!pip install -q gradio\n",
        "!pip install -q openai\n",
        "!pip install -q gTTS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usUSep_lY7q8"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Kr5faKybKi4p"
      },
      "outputs": [],
      "source": [
        "import whisper\n",
        "import gradio as gr \n",
        "import time\n",
        "import warnings\n",
        "import json\n",
        "import openai\n",
        "import os\n",
        "from gtts import gTTS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EyW6weauY9gf"
      },
      "source": [
        "# Defining Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "u_6_s2iHboR4"
      },
      "outputs": [],
      "source": [
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2zj7g1C381VJ"
      },
      "outputs": [],
      "source": [
        "with open('GPT_SECRET_KEY.json') as f:\n",
        "    data = json.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "mihgqxDv81VJ"
      },
      "outputs": [],
      "source": [
        "openai.api_key = data[\"API_KEY\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "cMNnv3oHaDt8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d11fe12-c0e8-4373-ccdf-2a08271bcef1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|████████████████████████████████████████| 139M/139M [00:00<00:00, 180MiB/s]\n"
          ]
        }
      ],
      "source": [
        "model = whisper.load_model(\"base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "e3bjy1E3R-MN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ce4bb4d-cefd-4706-c4fc-d5815a4b9664"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "model.device"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 10 -q:a 9 -acodec libmp3lame Temp.mp3"
      ],
      "metadata": {
        "id": "zlhNLyS3B22D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5798ffb9-769f-4793-e961-7b697c5e59ec"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\n",
            "  built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n",
            "  libavutil      56. 31.100 / 56. 31.100\n",
            "  libavcodec     58. 54.100 / 58. 54.100\n",
            "  libavformat    58. 29.100 / 58. 29.100\n",
            "  libavdevice    58.  8.100 / 58.  8.100\n",
            "  libavfilter     7. 57.100 /  7. 57.100\n",
            "  libavresample   4.  0.  0 /  4.  0.  0\n",
            "  libswscale      5.  5.100 /  5.  5.100\n",
            "  libswresample   3.  5.100 /  3.  5.100\n",
            "  libpostproc    55.  5.100 / 55.  5.100\n",
            "Input #0, lavfi, from 'anullsrc=r=44100:cl=mono':\n",
            "  Duration: N/A, start: 0.000000, bitrate: 352 kb/s\n",
            "    Stream #0:0: Audio: pcm_u8, 44100 Hz, mono, u8, 352 kb/s\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (pcm_u8 (native) -> mp3 (libmp3lame))\n",
            "Press [q] to stop, [?] for help\n",
            "Output #0, mp3, to 'Temp.mp3':\n",
            "  Metadata:\n",
            "    TSSE            : Lavf58.29.100\n",
            "    Stream #0:0: Audio: mp3 (libmp3lame), 44100 Hz, mono, s16p\n",
            "    Metadata:\n",
            "      encoder         : Lavc58.54.100 libmp3lame\n",
            "size=      39kB time=00:00:10.00 bitrate=  32.1kbits/s speed= 236x    \n",
            "video:0kB audio:39kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.568409%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ChatGPT_API_Function"
      ],
      "metadata": {
        "id": "_gNUvwL2BzD4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4QXJeLiy81VL"
      },
      "outputs": [],
      "source": [
        "def chatgpt_api(input_text):\n",
        "    messages = [\n",
        "    {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}]\n",
        "    \n",
        "    if input_text:\n",
        "        messages.append(\n",
        "            {\"role\": \"user\", \"content\": input_text},\n",
        "        )\n",
        "        chat_completion = openai.ChatCompletion.create(\n",
        "            model=\"gpt-3.5-turbo\", messages=messages\n",
        "        )\n",
        "    \n",
        "    reply = chat_completion.choices[0].message.content\n",
        "    return reply"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14CPZJMxY_Ks"
      },
      "source": [
        "# Transcribe Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "JtTvvQQPcOZZ"
      },
      "outputs": [],
      "source": [
        "def transcribe(audio):\n",
        "\n",
        "    language = 'en'\n",
        "\n",
        "    audio = whisper.load_audio(audio)\n",
        "    audio = whisper.pad_or_trim(audio)\n",
        "\n",
        "    mel = whisper.log_mel_spectrogram(audio).to(model.device)\n",
        "\n",
        "    _, probs = model.detect_language(mel)\n",
        "\n",
        "    options = whisper.DecodingOptions()\n",
        "    result = whisper.decode(model, mel, options)\n",
        "    result_text = result.text\n",
        "    \n",
        "    out_result = chatgpt_api(result_text)\n",
        "    \n",
        "    audioobj = gTTS(text = out_result, \n",
        "                    lang = language, \n",
        "                    slow = False)\n",
        "    \n",
        "    audioobj.save(\"Temp.mp3\")\n",
        "\n",
        "    return [result_text, out_result, \"Temp.mp3\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJaFmE9aZB_8"
      },
      "source": [
        "# Gradio Interface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "deSAVvfJcWBo",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "a8a51e43-5dce-4c91-f56f-51541e849fc1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "output_1 = gr.Textbox(label=\"Speech to Text\")\n",
        "output_2 = gr.Textbox(label=\"ChatGPT Output\")\n",
        "output_3 = gr.Audio(\"Temp.mp3\")\n",
        "\n",
        "gr.Interface(\n",
        "    title = 'OpenAI Whisper and ChatGPT ASR Gradio Web UI', \n",
        "    fn=transcribe, \n",
        "    inputs=[\n",
        "        gr.inputs.Audio(source=\"microphone\", type=\"filepath\")\n",
        "    ],\n",
        "\n",
        "    outputs=[\n",
        "        output_1,  output_2, output_3\n",
        "    ],\n",
        "    live=True).launch()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y2Zid2MKdPxK"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
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