openai-cookbook

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   "source": [
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    "# Azure audio whisper (preview) example\n",
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    "\n",
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    "The example shows how to use the Azure OpenAI Whisper model to transcribe audio files.\n"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## Setup\n",
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    "\n",
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    "First, we install the necessary dependencies and import the libraries we will be using."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "! pip install \"openai>=1.0.0,<2.0.0\"\n",
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    "! pip install python-dotenv"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "import os\n",
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    "import openai\n",
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    "import dotenv\n",
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    "\n",
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    "dotenv.load_dotenv()"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Authentication\n",
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    "\n",
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    "The Azure OpenAI service supports multiple authentication mechanisms that include API keys and Azure Active Directory token credentials."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "use_azure_active_directory = False  # Set this flag to True if you are using Azure Active Directory"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "#### Authentication using API key\n",
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    "\n",
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    "To set up the OpenAI SDK to use an *Azure API Key*, we need to 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)). You'll also find the endpoint for your resource here."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "if not use_azure_active_directory:\n",
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    "    endpoint = os.environ[\"AZURE_OPENAI_ENDPOINT\"]\n",
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    "    api_key = os.environ[\"AZURE_OPENAI_API_KEY\"]\n",
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    "\n",
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    "    client = openai.AzureOpenAI(\n",
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    "        azure_endpoint=endpoint,\n",
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    "        api_key=api_key,\n",
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    "        api_version=\"2023-09-01-preview\"\n",
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    "    )"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "#### Authentication using Azure Active Directory\n",
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    "Let's now see how we can autheticate via Azure Active Directory. We'll start by installing the `azure-identity` library. This library will provide the token credentials we need to authenticate and help us build a token credential provider through the `get_bearer_token_provider` helper function. It's recommended to use `get_bearer_token_provider` over providing a static token to `AzureOpenAI` because this API will automatically cache and refresh tokens for you. \n",
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    "\n",
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    "For more information on how to set up Azure Active Directory authentication with Azure OpenAI, see the [documentation](https://learn.microsoft.com/azure/ai-services/openai/how-to/managed-identity)."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "! pip install \"azure-identity>=1.15.0\""
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
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    "\n",
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    "if use_azure_active_directory:\n",
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    "    endpoint = os.environ[\"AZURE_OPENAI_ENDPOINT\"]\n",
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    "    api_key = os.environ[\"AZURE_OPENAI_API_KEY\"]\n",
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    "\n",
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    "    client = openai.AzureOpenAI(\n",
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    "        azure_endpoint=endpoint,\n",
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    "        azure_ad_token_provider=get_bearer_token_provider(DefaultAzureCredential(), \"https://cognitiveservices.azure.com/.default\"),\n",
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    "        api_version=\"2023-09-01-preview\"\n",
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    "    )"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "> Note: the AzureOpenAI infers the following arguments from their corresponding environment variables if they are not provided:\n",
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    "\n",
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    "- `api_key` from `AZURE_OPENAI_API_KEY`\n",
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    "- `azure_ad_token` from `AZURE_OPENAI_AD_TOKEN`\n",
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    "- `api_version` from `OPENAI_API_VERSION`\n",
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    "- `azure_endpoint` from `AZURE_OPENAI_ENDPOINT`\n"
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   ]
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  },
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  {
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   "attachments": {},
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## Deployments\n",
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    "\n",
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    "In this section we are going to create a deployment using the `whisper-1` model to transcribe audio files."
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "### Deployments: Create in the Azure OpenAI Studio\n",
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    "Let's deploy a model to use with whisper. Go to https://portal.azure.com, find your Azure OpenAI resource, and then navigate to the Azure OpenAI Studio. Click on the \"Deployments\" tab and then create a deployment for the model you want to use for whisper. The deployment name that you give the model will be used in the code below."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "deployment = \"whisper-deployment\" # Fill in the deployment name from the portal here"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "## Audio transcription\n",
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    "\n",
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    "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",
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    "\n",
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    "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)."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "# download sample audio file\n",
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    "import requests\n",
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    "\n",
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    "sample_audio_url = \"https://github.com/Azure-Samples/cognitive-services-speech-sdk/raw/master/sampledata/audiofiles/wikipediaOcelot.wav\"\n",
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    "audio_file = requests.get(sample_audio_url)\n",
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    "with open(\"wikipediaOcelot.wav\", \"wb\") as f:\n",
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    "    f.write(audio_file.content)"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "transcription = client.audio.transcriptions.create(\n",
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    "    file=open(\"wikipediaOcelot.wav\", \"rb\"),\n",
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    "    model=deployment,\n",
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    ")\n",
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    "print(transcription.text)"
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   ]
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  }
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