Feedback: audio-intelligence-content-moderation

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/audio-intelligence/content-moderation
Category: audio-intelligence
Generated: 05/08/2025, 4:33:41 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:33:40 pm

Technical Documentation Analysis: AssemblyAI Content Moderation

Overall Assessment

This documentation provides comprehensive code examples and covers multiple programming languages, but it suffers from several structural and clarity issues that could significantly impact user experience.

Critical Issues to Address

1. Missing Essential Information

Problem: The documentation lacks fundamental details users need before implementation.

Missing Information:

• What types of content are actually detected? The supported labels table is buried at the end
• Processing time expectations beyond the brief FAQ mention
• Pricing/usage limits for this feature
• Audio format requirements and limitations
• Minimum audio quality thresholds
• Maximum file size limits

Recommendation: Add a “Before You Start” section with:

## Before You Start

### Requirements
- Audio files must be in supported formats (MP3, WAV, FLAC, etc.)
- Minimum audio quality: 8kHz sample rate
- Maximum file size: 5GB
- Clear speech audio (background music/noise may affect accuracy)

### What Content is Detected
Content Moderation identifies 15+ categories including profanity, hate speech, violence, drugs, and more. See [Supported Labels](#supported-labels) for the complete list.

### Processing Time
- Typical processing: 15-30% of audio duration
- Real-time applications: Segments processed in <1 second

2. Poor Information Architecture

Problem: Critical information is poorly organized, making it hard to find key details.

Issues:

• Supported labels table is at the very end (should be near the beginning)
• No clear section on response interpretation
• Confidence threshold explanation comes after complex code examples

Recommended Structure:

# Content Moderation

## Overview
[Brief description + use cases]

## Supported Content Types
[Move labels table here]

## Quick Start
[Simplest possible example]

## Configuration Options
[Confidence threshold, etc.]

## Understanding Results
[Response interpretation guide]

## Complete Examples
[Full code examples by language]

## API Reference
[Technical details]

3. Unclear Result Interpretation

Problem: Users won’t understand how to interpret the complex response structure.

Current Issue: The example output shows numbers like 0.8141 - 0.4014 but doesn’t explain what these mean in practical terms.

Solution: Add a dedicated section:

## Understanding Your Results

### Confidence Scores
- **0.9+**: Very likely contains this content type
- **0.7-0.9**: Likely contains this content type
- **0.5-0.7**: Possibly contains this content type
- **Below 0.5**: Unlikely (filtered out by default)

### Severity Scores
- **0.0-0.3**: Low severity - mild references
- **0.3-0.7**: Medium severity - clear discussion
- **0.7-1.0**: High severity - explicit or intense content

### Example Interpretation
```json
"disasters - 0.8141 - 0.4014"

This means: 81% confident the segment discusses disasters with low-medium severity (0.4).

### 4. Inadequate Error Handling

**Problem**: Code examples have minimal error handling guidance.

**Current State**: Only shows basic status checking
**Needed**: Comprehensive error scenarios and handling

**Add Section**:
```markdown
## Error Handling

### Common Issues
- `invalid_audio`: Audio file corrupted or unsupported format
- `audio_too_short`: Minimum 0.5 seconds of speech required
- `content_safety_unavailable`: Model temporarily unavailable

### Implementation Example
```python
if transcript.status == 'error':
    error_code = transcript.error
    if 'invalid_audio' in error_code:
        # Handle audio format issues
    elif 'audio_too_short' in error_code:
        # Handle insufficient audio

5. Missing Practical Guidance

Problems:

• No guidance on choosing confidence thresholds
• No examples of common use cases
• No performance optimization tips

Add Sections:

## Choosing the Right Confidence Threshold

### Recommended Settings by Use Case
- **Content moderation for public platforms**: 25-40% (catch more potential issues)
- **Internal content review**: 50-60% (balanced approach)
- **High-precision filtering**: 70%+ (fewer false positives)

## Common Use Cases

### Example: Podcast Content Screening
```python
# Screen for brand-safe content
config = aai.TranscriptionConfig(
    content_safety=True,
    content_safety_confidence=30  # Lower threshold for brand safety
)

# Focus on high-risk categories
risk_categories = ['hate_speech', 'profanity', 'nsfw']
for result in transcript.content_safety.results:
    for label in result.labels:
        if label.label in risk_categories and label.confidence > 0.5:
            print(f"⚠️  Found {label.label} at {result.timestamp.start}ms")

Performance Tips

Optimizing for Speed

• Use streaming for long audio files
• Process in segments for real-time applications
• Cache results for repeated analysis

Optimizing for Accuracy

• Ensure clear audio quality
• Use appropriate confidence thresholds
• Review edge cases manually

### 6. Code Example Improvements

**Current Issues**:
- Examples are too complex for getting started
- Missing practical filtering examples
- No guidance on handling results programmatically

**Recommendations**:

**Add Simple Example First**:
```python
## Simple Example

```python
import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

# Basic content moderation
transcript = aai.Transcriber().transcribe(
    "audio.mp3",
    config=aai.TranscriptionConfig(content_safety=True)
)

# Check if any sensitive content was found
if transcript.content_safety.results:
    print(f"⚠️  Found {len(transcript.content_safety.results)} flagged segments")
    for result in transcript.content_safety.results:
        print(f"- {result.labels[0].label} at {result.timestamp.start//1000}s")
else:
    print("✅ No sensitive content detected")

7. Missing Integration Guidance

Add Section:

## Integration Patterns

### Batch Processing
```python
def moderate_audio_files(file_paths):
    results = {}
    for file_path in file_paths:
        transcript = transcriber.transcribe(file_path, config)
        results[file_path] = {
            'safe': len(transcript.content_safety.results) == 0,
            'issues': [r.labels[0].label for r in transcript.content_safety.results]
        }
    return results

Real-time Moderation

def real_time_content_check(audio_chunk):
    # Process small chunks for real-time feedback
    if is_sensitive_content(audio_chunk):
        return {"action": "flag", "confidence": 0.85}
    return {"action": "allow"}

Priority Fixes

1. Immediate: Move supported labels table to the top
2. Immediate: Add “Understanding Results” section with clear explanations
3. High: Create simple example before complex ones
4. High: Add error handling guidance
5. Medium: Reorganize entire structure as outlined above
6. Medium: Add use case examples and integration patterns

These changes would transform this from a code-heavy reference into user-friendly documentation that guides users from concept to implementation.

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