Feedback: audio-intelligence-topic-detection

Documentation Feedback

Original URL: https://assemblyai.com/docs/audio-intelligence/topic-detection
Category: audio-intelligence
Generated: 05/08/2025, 4:33:00 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:32:59 pm

Documentation Analysis: Topic Detection

🎯 Overall Assessment

This documentation is technically solid but has several areas for improvement in clarity, structure, and user experience. The content is comprehensive but could be better organized and more user-friendly.

🔍 Specific Issues & Recommendations

1. Missing Critical Information

Issue: No information about pricing, rate limits, or usage quotas.

Add a section covering:
- Pricing per minute/hour
- API rate limits
- Usage quotas or restrictions
- Processing time expectations

Issue: No error handling examples.

Add error handling examples for each language:
- Network timeouts
- Invalid audio formats
- Quota exceeded errors
- Authentication failures

2. Unclear Explanations

Issue: The IAB Content Taxonomy explanation is vague.

"consists of 698 comprehensive topics"
"consists of 698 hierarchical topics organized in categories like 'NewsAndPolitics>Weather'
and 'Home&Garden>IndoorEnvironmentalQuality'. Topics are structured as parent>child
relationships, allowing for both broad and specific categorization."

Issue: Relevance scores lack explanation.

Add explanation:
"Relevance scores range from 0.0 to 1.0, where:
- 0.8-1.0: Highly relevant
- 0.5-0.7: Moderately relevant
- 0.0-0.4: Low relevance
Multiple topics can be detected simultaneously with different relevance scores."

3. Better Examples Needed

Current example output is hard to parse:

Smoke from hundreds of wildfires in Canada is triggering air quality alerts...
Timestamp: 250 - 28920
Home&Garden>IndoorEnvironmentalQuality (0.9881)

**Example 1: News Interview**
Text: "Smoke from hundreds of wildfires in Canada is triggering air quality alerts..."
Timeframe: 0:04 - 0:28 (4.2 seconds to 28.9 seconds)
Detected Topics:
├── Home&Garden>IndoorEnvironmentalQuality (98.8% confidence)
├── NewsAndPolitics>Weather (55.6% confidence)
└── MedicalHealth>DiseasesAndConditions>LungAndRespiratoryHealth (0.4% confidence)

4. Improved Structure

Reorganize the content flow:

Current: Introduction → Quickstart → API Reference → FAQ
Better: Introduction → Key Concepts → Quickstart → Advanced Usage → API Reference → Troubleshooting → FAQ

Add a “Key Concepts” section before Quickstart:

## Key Concepts

### IAB Content Taxonomy
The Internet Advertising Bureau (IAB) taxonomy is a standardized classification system with 698 topics organized hierarchically:
- **Level 1**: Broad categories (e.g., "NewsAndPolitics")
- **Level 2**: Subcategories (e.g., "NewsAndPolitics>Weather")
- **Level 3**: Specific topics (e.g., "NewsAndPolitics>Weather>Storms")

### Relevance Scoring
Each detected topic receives a relevance score (0.0-1.0):
- **High relevance (0.8-1.0)**: Primary topic of the audio segment
- **Medium relevance (0.4-0.7)**: Secondary or related topic
- **Low relevance (0.0-0.4)**: Tangentially related content

### Results Structure
- **Segment-level results**: Topics detected in specific time ranges
- **Summary results**: Overall topic relevance for the entire audio file

5. User Pain Points & Solutions

Pain Point: Users don’t know what audio works best.

## Audio Requirements & Best Practices

### Supported Audio Formats
- MP3, WAV, MP4, M4A, FLAC, OGG
- Maximum file size: 2.2GB
- Minimum duration: 0.5 seconds

### Optimization Tips
- Clear speech with minimal background noise
- Audio with substantial spoken content (>30 seconds recommended)
- Avoid music-only or ambient sound files
- Higher quality audio (44.1kHz, 16-bit) produces better results

Pain Point: Code examples are too long and intimidating.

Add a "Quick Start" with minimal code:

## 30-Second Quick Start

```python
import assemblyai as aai
aai.settings.api_key = "YOUR_API_KEY"

transcript = aai.Transcriber().transcribe(
    "your_audio.mp3",
    config=aai.TranscriptionConfig(iab_categories=True)
)

# Print detected topics
for topic, relevance in transcript.iab_categories.summary.items():
    print(f"{topic}: {relevance:.1%}")
```

Pain Point: No guidance on interpreting results.

## Interpreting Results

### Understanding Topic Hierarchies
Topics use `>` to show parent-child relationships:
- `NewsAndPolitics>Weather>Storms` = News about weather storms
- `MedicalHealth>DiseasesAndConditions>Cancer` = Medical content about cancer

### Working with Relevance Scores
```python
# Filter for highly relevant topics only
high_confidence_topics = {
    topic: score for topic, score in summary.items()
    if score > 0.8
}

# Get primary topic
primary_topic = max(summary.items(), key=lambda x: x[1])
```

6. Additional Improvements

Add troubleshooting section:

## Troubleshooting

### No Topics Detected
- **Audio too short**: Minimum 30 seconds recommended for reliable detection
- **Poor audio quality**: Ensure clear speech, minimal background noise
- **Non-English content**: Check if your language is supported
- **Music/ambient audio**: Model works best with speech content

### Unexpected Results
- **Irrelevant topics**: May indicate background noise or cross-talk
- **Low confidence scores**: Normal for tangentially related content
- **Missing expected topics**: Audio may not contain enough relevant keywords

Add integration examples:

## Common Integration Patterns

### Content Categorization
```python
def categorize_podcast(audio_file):
    transcript = transcriber.transcribe(audio_file, config)
    primary_category = max(transcript.iab_categories.summary.items(), key=lambda x: x[1])
    return primary_category[0].split('>')[0]  # Get top-level category

Topic-based Search

def find_segments_about_topic(transcript, target_topic):
    relevant_segments = []
    for result in transcript.iab_categories.results:
        for label in result.labels:
            if target_topic.lower() in label.label.lower() and label.relevance > 0.5:
                relevant_segments.append({
                    'text': result.text,
                    'start_time': result.timestamp.start / 1000,  # Convert to seconds
                    'relevance': label.relevance
                })
    return relevant_segments

7. Language-Specific Issues

Several code examples have minor issues:

• Python SDK example has inconsistent variable naming
• JavaScript example missing error handling
• C# example could be simplified with better structure
• PHP example has a copy-paste error referencing content_safety_labels

🏆 Priority Recommendations

1. High Priority: Add Key Concepts section and improve example formatting
2. High Priority: Add error handling examples for all languages
3. Medium Priority: Create troubleshooting section
4. Medium Priority: Add audio requirements and best practices
5. Low Priority: Fix minor code issues and add integration patterns

These improvements would significantly enhance user experience

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