{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure audio whisper (preview) example\n",
    "\n",
    "> Note: There is a newer version of the openai library available. See https://github.com/openai/openai-python/discussions/742\n",
    "\n",
    "The example shows how to use the Azure OpenAI Whisper model to transcribe audio files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First, we install the necessary dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install \"openai>=0.28.1,<1.0.0\"\n",
    "! pip install python-dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll import our libraries and configure the Python OpenAI SDK to work with the Azure OpenAI service."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: In this example, we configured the library to use the Azure API by setting the variables in code. For development, consider setting the environment variables instead:\n",
    "\n",
    "```\n",
    "OPENAI_API_BASE\n",
    "OPENAI_API_KEY\n",
    "OPENAI_API_TYPE\n",
    "OPENAI_API_VERSION\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import dotenv\n",
    "import openai\n",
    "\n",
    "\n",
    "dotenv.load_dotenv()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To properly access the Azure OpenAI Service, we need to create the proper resources at the [Azure Portal](https://portal.azure.com) (you can check a detailed guide on how to do this in the [Microsoft Docs](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal))\n",
    "\n",
    "Once the resource is created, the first thing we need to use is its endpoint. You can get the endpoint by looking at the *\"Keys and Endpoints\"* section under the *\"Resource Management\"* section. Having this, we will set up the SDK using this information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "openai.api_base = os.environ[\"OPENAI_API_BASE\"]\n",
    "\n",
    "# Min API version that supports Whisper\n",
    "openai.api_version = \"2023-09-01-preview\"\n",
    "\n",
    "# Enter the deployment_id to use for the Whisper model\n",
    "deployment_id = \"<deployment-id-for-your-whisper-model>\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authentication\n",
    "\n",
    "The Azure OpenAI service supports multiple authentication mechanisms that include API keys and Azure credentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set to True if using Azure Active Directory authentication\n",
    "use_azure_active_directory = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Authentication using API key\n",
    "\n",
    "To set up the OpenAI SDK to use an *Azure API Key*, we need to set up the `api_type` to `azure` and set `api_key` to a key associated with your endpoint (you can find this key in *\"Keys and Endpoints\"* under *\"Resource Management\"* in the [Azure Portal](https://portal.azure.com))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not use_azure_active_directory:\n",
    "    openai.api_type = 'azure'\n",
    "    openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Authentication using Azure Active Directory\n",
    "Let's now see how we can get a key via Microsoft Active Directory Authentication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.identity import DefaultAzureCredential\n",
    "\n",
    "if use_azure_active_directory:\n",
    "    default_credential = DefaultAzureCredential()\n",
    "    token = default_credential.get_token(\"https://cognitiveservices.azure.com/.default\")\n",
    "\n",
    "    openai.api_type = 'azure_ad'\n",
    "    openai.api_key = token.token"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A token is valid for a period of time, after which it will expire. To ensure a valid token is sent with every request, you can refresh an expiring token by hooking into requests.auth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import typing\n",
    "import time\n",
    "import requests\n",
    "\n",
    "if typing.TYPE_CHECKING:\n",
    "    from azure.core.credentials import TokenCredential\n",
    "\n",
    "class TokenRefresh(requests.auth.AuthBase):\n",
    "\n",
    "    def __init__(self, credential: \"TokenCredential\", scopes: typing.List[str]) -> None:\n",
    "        self.credential = credential\n",
    "        self.scopes = scopes\n",
    "        self.cached_token: typing.Optional[str] = None\n",
    "\n",
    "    def __call__(self, req):\n",
    "        if not self.cached_token or self.cached_token.expires_on - time.time() < 300:\n",
    "            self.cached_token = self.credential.get_token(*self.scopes)\n",
    "        req.headers[\"Authorization\"] = f\"Bearer {self.cached_token.token}\"\n",
    "        return req\n",
    "\n",
    "if use_azure_active_directory:\n",
    "    session = requests.Session()\n",
    "    session.auth = TokenRefresh(default_credential, [\"https://cognitiveservices.azure.com/.default\"])\n",
    "\n",
    "    openai.requestssession = session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Audio transcription\n",
    "\n",
    "Audio transcription, or speech-to-text, is the process of converting spoken words into text. Use the `openai.Audio.transcribe` method to transcribe an audio file stream to text.\n",
    "\n",
    "You can get sample audio files from the [Azure AI Speech SDK repository at GitHub](https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/sampledata/audiofiles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download sample audio file\n",
    "import requests\n",
    "\n",
    "sample_audio_url = \"https://github.com/Azure-Samples/cognitive-services-speech-sdk/raw/master/sampledata/audiofiles/wikipediaOcelot.wav\"\n",
    "audio_file = requests.get(sample_audio_url)\n",
    "with open(\"wikipediaOcelot.wav\", \"wb\") as f:\n",
    "    f.write(audio_file.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcription = openai.Audio.transcribe(\n",
    "    file=open(\"wikipediaOcelot.wav\", \"rb\"),\n",
    "    model=\"whisper-1\",\n",
    "    deployment_id=deployment_id,\n",
    ")\n",
    "print(transcription.text)"
   ]
  }
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