Feedback: guides-input-text-speaker-labels

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/input-text-speaker-labels
Category: guides
Generated: 05/08/2025, 4:40:00 pm

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:39:59 pm

Technical Documentation Analysis & Improvement Recommendations

Critical Issues Requiring Immediate Attention

1. Missing Prerequisites and Setup Information

Issue: No clear explanation of what LeMUR is or its relationship to transcription
Fix: Add a “What is LeMUR?” section explaining the framework and its use cases
Add: System requirements, Python version compatibility, and dependency information

2. Incomplete Code Examples

Issue: Missing expected output example and error handling
Fix: Include complete sample output and common error scenarios with solutions

3. Inconsistent API Key Handling

Issue: Shows both "YOUR_API_KEY" and "API_KEY" without clear guidance
Fix: Standardize format and add security best practices section

Structural Improvements

Current Structure Issues:

Quickstart appears before setup instructions
Step-by-step section repeats quickstart content
No clear learning progression

Recommended Structure:

1. Overview & Prerequisites
2. What is LeMUR?
3. Setup & Installation
4. Basic Example (without speaker labels)
5. Advanced Example (with speaker labels)
6. Parameter Reference
7. Troubleshooting
8. Next Steps

Specific Content Improvements

1. Enhanced Introduction

# Process Speaker Labels with LeMUR's Custom Text Input

LeMUR (Leveraging Large Language Models to Understand Recognized Speech) allows you to analyze transcripts using AI. This guide demonstrates how to format speaker-labeled transcripts for LeMUR processing using the `input_text` parameter.

## When to Use This Guide
- You need to identify different speakers in your audio
- You want to analyze conversations or interviews
- You need custom formatting before LeMUR processing

## Prerequisites
- AssemblyAI account with API key
- Python 3.7+
- Paid LeMUR subscription (Basic or Full)

2. Missing Parameter Documentation

Add a reference section:

## Parameter Reference

### TranscriptionConfig Parameters
- `speaker_labels` (bool): Enable speaker identification in transcripts

### LeMUR Task Parameters
- `input_text` (str): Custom formatted text instead of transcript_ids
- `final_model` (LemurModel): AI model to use (claude3_5_sonnet, etc.)
- `prompt` (str): Instructions for the AI model

3. Add Error Handling Example

try:
    transcript = transcriber.transcribe(audio_url, config=config)

    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        exit(1)

    # Process speaker labels...

except Exception as e:
    print(f"Error: {e}")

4. Include Expected Output

## Expected Output

Based on the speaker-labeled transcript, here are the answers:

Speaker Identification:
- Speaker A: Michel Martin (NPR host)
- Speaker B: Peter DeCarlo (Professor at Johns Hopkins University)
Host Questions:
- “Can you explain the current wildfire situation in Canada?”
- “How might this affect air quality in the US?” [Additional questions from the actual audio]
Guest’s Main Concerns:
- Air quality impact on public health
- Long-term environmental effects [Additional concerns from the actual audio]

User Experience Pain Points

1. Unclear Value Proposition

Problem: Users don’t understand why they’d use input_text vs transcript_ids
Solution: Add comparison table showing when to use each approach

2. No Validation Guidance

Problem: No way to verify the speaker labeling worked correctly
Solution: Add section on validating results before LeMUR processing

3. Missing Cost Information

Problem: No mention of API costs or usage limits
Solution: Add cost estimation and optimization tips

Additional Recommendations

1. Add Interactive Elements

<Tabs>
  <Tab title="Python">
    [Python code example]
  </Tab>
  <Tab title="JavaScript">
    [JavaScript equivalent]
  </Tab>
</Tabs>

2. Include Troubleshooting Section

## Common Issues

### Speaker Labels Not Detected
- **Cause**: Audio has only one speaker or poor audio quality
- **Solution**: Verify multiple speakers exist and consider audio preprocessing

### LeMUR Request Fails
- **Cause**: Insufficient account credits or invalid API key
- **Solution**: Check account status and API key validity

## Next Steps
- [Speaker Diarization Best Practices](link)
- [LeMUR Prompt Engineering Guide](link)
- [Advanced Audio Processing](link)

4. Performance Optimization Tips

## Tips for Better Results
- Use high-quality audio with clear speaker separation
- Keep prompts specific and focused
- Consider chunking long transcripts for better performance
- Validate speaker labels before processing with LeMUR

These improvements will significantly enhance user comprehension, reduce support requests, and provide a more professional documentation experience.