{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["# Import required libraries"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import requests\n",
        "import json\n",
        "from gtts import gTTS\n",
        "import os\n",
        "from pydub import AudioSegment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["# 1. Function to take in audio output and return text output"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def transcribe_audio_to_text(file_path):\n",
        "    # Replace with your implementation for transcribing audio to text\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 2. Function to call the ChatGPT API with a text and system prompt and return the response"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def call_chatgpt_api(prompt, system_prompt):\n",
        "    # Replace with your OpenAI API Key\n",
        "    api_key = 'your-api-key'\n",
        "\n",
        "    headers = {\n",
        "        'Content-Type': 'application/json',\n",
        "        'Authorization': f'Bearer {api_key}',\n",
        "    }\n",
        "\n",
        "    data = json.dumps({\n",
        "        'model': 'text-davinci-002',\n",
        "        'prompt': f'{system_prompt} {prompt}',\n",
        "        'max_tokens': 150,\n",
        "        'n': 1,\n",
        "        'stop': None,\n",
        "        'temperature': 0.5,\n",
        "    })\n",
        "\n",
        "    response = requests.post('https://api.openai.com/v1/engines/davinci-codex/completions', headers=headers, data=data)\n",
        "    response_text = response.json()['choices'][0]['text'].strip()\n",
        "\n",
        "    return response_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 3. Function to convert text to speech using a suitable library and add intonation for Yoda's voice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def text_to_speech_yoda(text, output_file):\n",
        "    tts = gTTS(text, lang='en')\n",
        "    tts.save(output_file)\n",
        "\n",
        "    # Add intonation for Yoda voice (you may need to customize this for better results)\n",
        "    audio = AudioSegment.from_file(output_file, format=\"mp3\")\n",
        "    audio = audio.speedup(playback_speed=1.2)\n",
        "    audio.export(output_file, format=\"mp3\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 4. Wrapper function that calls all of these functions in order"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def process_audio(input_audio_file, output_audio_file, system_prompt):\n",
        "    transcribed_text = transcribe_audio_to_text(input_audio_file)\n",
        "    chatgpt_response = call_chatgpt_api(transcribed_text, system_prompt)\n",
        "    text_to_speech_yoda(chatgpt_response, output_audio_file)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}