Feedback: guides-identifying-highlights-in-audio-or-video-files

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/identifying-highlights-in-audio-or-video-files
Category: guides
Generated: 05/08/2025, 4:40:13 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:40:12 pm

Technical Documentation Analysis & Feedback

Overall Assessment

This documentation provides a comprehensive step-by-step guide for implementing audio highlights, but has several areas that need improvement for better user experience and clarity.

Critical Issues & Recommendations

1. Missing Prerequisites & Setup Information

Issues:

• No clear explanation of what an API key looks like or where exactly to find it
• Missing dependency installation instructions for most languages
• No mention of file size limits or supported audio formats

Recommendations:

## Prerequisites

Before starting, ensure you have:
- An AssemblyAI account ([sign up for free](https://assemblyai.com/dashboard/signup))
- Your API key (found in your [dashboard](https://assemblyai.com/dashboard) - looks like `abc123...`)
- Audio/video file in supported format (MP3, WAV, MP4, etc.)
- File size under 500MB (for larger files, see [our guide on handling large files])

### Supported File Formats
- Audio: MP3, WAV, FLAC, AAC, OGG
- Video: MP4, MOV, AVI, WMV
- Maximum file size: 500MB

2. Inconsistent Step Structure

Issues:

• Step 3 shows different actions across language tabs (config creation vs file upload)
• SDK implementation is dramatically simpler but this isn’t explained
• Steps don’t align logically between SDK and API approaches

Recommendations:

• Split into two separate guides: “Using Python SDK” and “Using REST API”
• Or clearly label sections: “Option A: SDK (Recommended)” and “Option B: Direct API”
• Add a comparison table showing pros/cons of each approach

3. Incomplete Code Examples

Issues:

• Missing import statements in several languages
• Hardcoded file paths without explanation
• No error handling in most examples
• Missing complete, runnable examples

Recommendations:

# Complete Python SDK example
import assemblyai as aai

# Set your API key (get it from https://assemblyai.com/dashboard)
aai.settings.api_key = "your_api_key_here"

def analyze_highlights(audio_url_or_path):
    """
    Analyze audio file and extract key highlights

    Args:
        audio_url_or_path: URL or local file path to audio/video

    Returns:
        List of highlight objects with text, count, rank, and timestamps
    """
    try:
        # Configure transcription with highlights enabled
        config = aai.TranscriptionConfig(auto_highlights=True)
        transcriber = aai.Transcriber(config=config)

        # Transcribe and extract highlights
        transcript = transcriber.transcribe(audio_url_or_path)

        if transcript.status == aai.TranscriptStatus.error:
            print(f"Transcription failed: {transcript.error}")
            return None

        return transcript.auto_highlights.results

    except Exception as e:
        print(f"Error: {e}")
        return None

# Example usage
if __name__ == "__main__":
    highlights = analyze_highlights("https://assembly.ai/wildfires.mp3")

    if highlights:
        for highlight in highlights:
            print(f"📍 {highlight.text}")
            print(f"   Count: {highlight.count} | Relevance: {highlight.rank:.2%}")
            print(f"   Timestamps: {[f'{t.start}ms-{t.end}ms' for t in highlight.timestamps]}")
            print()

4. Poor Response Documentation

Issues:

• JSON structure explanation comes after code implementation
• No explanation of what rank values mean in practical terms
• Missing information about result ordering
• No guidance on interpreting or filtering results

Recommendations:

## Understanding Highlight Results

Each highlight contains:

| Field | Type | Description | Example |
|-------|------|-------------|---------|
| `text` | string | The identified key phrase | "project timeline" |
| `count` | integer | Times mentioned in audio | 3 |
| `rank` | float | Relevance score (0.0-1.0) | 0.87 |
| `timestamps` | array | When phrase occurs (milliseconds) | [{"start": 1200, "end": 2400}] |

### Interpreting Rank Scores
- **0.8-1.0**: Highly relevant, core topics
- **0.6-0.8**: Important supporting details
- **0.4-0.6**: Contextual information
- **0.0-0.4**: Less significant mentions

### Filtering Results
```python
# Get only high-relevance highlights
important_highlights = [h for h in highlights if h.rank > 0.7]

# Get most frequently mentioned phrases
frequent_highlights = sorted(highlights, key=lambda x: x.count, reverse=True)[:5]

5. Missing Error Handling & Troubleshooting

Issues:

• No guidance on common errors
• No information about API rate limits
• Missing validation steps

Recommendations:

## Common Issues & Troubleshooting

### Authentication Errors
```bash
Error: 401 Unauthorized

Solution: Verify your API key is correct and has proper format

File Upload Failures

Error: 400 Bad Request - Invalid audio file

Solutions:

• Check file format is supported (MP3, WAV, MP4, etc.)
• Ensure file size is under 500MB
• Verify file isn’t corrupted

No Highlights Found

If auto_highlights_result.results is empty:

• Audio may be too short (minimum ~30 seconds recommended)
• Content might lack significant key phrases
• Try with different audio content

Rate Limits

• Free tier: 5 concurrent requests
• Paid plans: Higher limits available
• If exceeded, wait and retry

### 6. **Lack of Practical Examples**

**Issues:**
- Only shows console output
- No real-world integration examples
- Missing use case implementations

**Recommendations:**
```markdown
## Real-World Examples

### Call Center Analysis
```python
def analyze_customer_call(call_recording_path):
    """Extract key topics from customer service calls"""
    highlights = analyze_highlights(call_recording_path)

    # Focus on high-relevance highlights
    key_issues = [h for h in highlights if h.rank > 0.6]

    # Generate summary
    summary = {
        'total_highlights': len(highlights),
        'key_issues': [h.text for h in key_issues],
        'most_discussed': max(highlights, key=lambda x: x.count).text if highlights else None
    }

    return summary

Meeting Minutes Generation

def generate_meeting_highlights(meeting_recording):
    """Extract action items and key decisions from meetings"""
    highlights = analyze_highlights(meeting_recording)

    # Sort by relevance
    sorted_highlights = sorted(highlights, key=lambda x: x.rank, reverse=True)

    return {
        'top_topics': [h.text for h in sorted_highlights[:10]],
        'action_items': [h for h in highlights if any(word in h.text.lower()
                        for word in ['action', 'todo', 'assign', 'deadline'])]
    }

7. Improve Navigation & Structure

Current structure is confusing. Recommend:

# Identifying Highlights in Audio & Video Files

> Extract key phrases and important concepts from your audio content automatically

## Quick Start (Recommended)
[Simple SDK example with working code]

## Choose Your Implementation
- **[Python SDK](#python-sdk)** - Easiest, recommended for most users
- **[REST API](#rest-api)** - Direct API access, multiple languages

## Understanding Results
[Detailed explanation of response format]

## Advanced Usage
[Filtering, error handling, real-world examples]

## Troubleshooting
[Common issues and solutions]

8. Missing Performance & Limitations Info

Add section:

## Performance & Limitations

- **Processing Time**: ~25% of audio duration (

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