> ## Documentation Index
> Fetch the complete documentation index at: https://assembly-preview.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Process Speaker Labels With LeMUR's Custom Text Input Parameter

> Utilize LeMUR feature

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.

<Note>
  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.
</Note>

## Get Started

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.

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.

## Step-by-Step Instructions

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.

1. Install the SDK.

```sh theme={"system"}
pip install-U assemblyai
```

Import the `assemblyai` and set your API key:

```python theme={"system"}
import assemblyai as aai
aai.settings.api_key = "API_KEY" 
```

Create a `TranscriptionConfig` with `speaker_labels` set to `True`.

```python theme={"system"}
config = aai.TranscriptionConfig(speaker_labels=True)
```

Use the `Transcriber` object's `transcribe` method and parse the configuration 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.

```python theme={"system"}
transcriber = aai.Transcriber()
transcript = transcriber.transcribe("https://github.com/AssemblyAI-Examples/audio-examples/raw/main/20230607_me_canadian_wildfires.mp3", config=config)
```

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.

```python theme={"system"}
text = ""

for utt in transcript.utterances:
    text += f"Speaker {utt.speaker}:\n{utt.text}\n"
```

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.

The result is stored in `response` as a string.

```python theme={"system"}
result = aai.Lemur().task(
    '''
    This is a speaker-labeled transcript of a podcast episode between Michel Martin, NPR host, and Peter DeCarlo, a professor at Johns Hopkins University. 
    Please answer the following questions: 
    1) Who is Speaker A and Speaker B? 
    2) What questions did the podcast host ask? 
    3) What were the main concerns of the podcast guest?"
    ''',
    input_text=text,
    final_model=aai.LemurModel.claude3_5_sonnet,
)

print(result.response)
```