lemur/input-text-speaker-labels.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Trj8M34fsvr"
      },
      "source": [
        "<a href=\"https://www.assemblyai.com/\">\n",
        "<img width=\"300\"\n",
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        "</a>\n",
        "</img>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwoGmHLVtt3C"
      },
      "source": [
        "# Processing Speaker Labels with LeMUR's Custom Text Input Parameter\n",
        "\n",
        "In this guide, we'll show you how to use AssemblyAI's [LeMUR](https://www.assemblyai.com/blog/lemur/) (Leveraging Large Language Models to Understand Recognized Speech) framework to process Speaker Labels from your transcript using the `input_text` parameter. The `input_text` option allows you to modify the transcripts before processing it using LeMUR, and in this example, format Speaker Labels in your LeMUR request.\n",
        "\n",
        ">**Note**  \n",
        "Calling LeMUR using `transcript_ids` is preferred as default. Depending on your use case, you can alternatively use the `input_text` parameter to call LeMUR with custom formatted transcript data including edited transcripts, speaker-labelled transcripts and more. \n",
        "\n",
        "### Get Started\n",
        "\n",
        "Before we begin, make sure you have an AssemblyAI account and an API key. You can [sign up](https://assemblyai.com/dashboard/signup) for an account and get your API key from your dashboard.\n",
        "\n",
        "LeMUR features are currently only available to paid users, at two pricing tiers: LeMUR and LeMUR Basic. Refer to [pricing](https://www.assemblyai.com/pricing) for more details.\n",
        "\n",
        "### Step-by-Step Instructions\n",
        "\n",
        "In this guide, we will include Speaker Labels to the conversation by using the `input_text` parameter. We will use the Custom Task endpoint to prompt LeMUR. You can use any LeMUR Endpoint and adjust the prompt parameters to suit your project's needs.\n",
        "\n",
        "Install the SDK."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pip install -U assemblyai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import the `assemblyai` package and set your API key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import assemblyai as aai\n",
        "\n",
        "aai.settings.api_key = \"API_KEY\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create a `TranscriptionConfig` with `speaker_labels` set to `True`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "config = aai.TranscriptionConfig(speaker_labels=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Use the `Transcriber` object's `transcribe` method and parse the configuratin and the audio file's path as a parameter . The `transcribe` method will save the results of the transcription to the `Transcriber` object's `transcript` attribute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "transcriber = aai.Transcriber()\n",
        "\n",
        "transcript = transcriber.transcribe(\"https://github.com/AssemblyAI-Examples/audio-examples/raw/main/20230607_me_canadian_wildfires.mp3\", config=config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create an empty string variable `text` to store the output. Iterate through each utterance in the transcript to append the formatted Speaker Labels to be used as the `input_text` LeMUR parameter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "text = \"\"\n",
        "\n",
        "for utt in transcript.utterances:\n",
        "    text += f\"Speaker {utt.speaker}:\\n{utt.text}\\n\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run `Lemur`'s `task` method on your transcript and parse the `prompt` and `input_text` parameters. The `text` variable contains the formatted Speaker Labels and Text response from your transcript. \n",
        "\n",
        "The result is stored in `response` as a string."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "result = aai.Lemur().task(\n",
        "    '''\n",
        "    This is a speaker-labeled transcript of a podcast episode between Michel Martin, NPR host, and Peter DeCarlo, a professor at Johns Hopkins University. \n",
        "    Please answer the following questions: \n",
        "    1) Who is Speaker A and Speaker B? \n",
        "    2) What questions did the podcast host ask? \n",
        "    3) What were the main concerns of the podcast guest?\"\n",
        "    ''',\n",
        "    input_text=text,\n",
        "    final_model=aai.LemurModel.claude3_5_sonnet,\n",
        ")\n",
        "\n",
        "print(result.response)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The output will look similar to the example below. "
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "1) Who is Speaker A and Speaker B?\n",
        "Speaker A is Michel Martin, the NPR host.\n",
        "Speaker B is Peter DeCarlo, an associate professor in the department of Environmental Health and Engineering at Johns Hopkins University.\n",
        "\n",
        "2) What questions did the podcast host ask?\n",
        "The host asked several questions, including:\n",
        "- What conditions have caused this round of wildfires to affect so many people far away?\n",
        "- What in the haze makes it harmful?\n",
        "- What makes this particularly harmful?\n",
        "- Who is most vulnerable in this situation?\n",
        "- Could this get worse?\n",
        "- How much longer could this last?\n",
        "- Will we be seeing more of these kinds of wide-ranging air quality consequences due to climate change?\n",
        "\n",
        "3) What were the main concerns of the podcast guest?\n",
        "The main concerns expressed by Professor DeCarlo were:\n",
        "- The high levels of particulate matter in the air, which are much higher than normal and can cause health problems.\n",
        "- The impact on vulnerable populations, including children, the elderly, and those with pre-existing health conditions.\n",
        "- The potential for this to become a more frequent occurrence due to climate change, with fire seasons starting earlier and lasting longer.\n",
        "- The unusual nature of the eastern US being affected by wildfire smoke, which is typically more of an issue in the western US.\n",
        "- The need for weather patterns to shift to alleviate the current air quality issues in the affected regions."
      ]
    }
  ],
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