Feedback: guides-identifying-hate-speech-in-audio-or-video-files

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/identifying-hate-speech-in-audio-or-video-files
Category: guides
Generated: 05/08/2025, 4:40:03 pm

---

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:40:02 pm

I’ve analyzed this documentation and identified several areas for improvement. Here’s my detailed feedback:

Critical Issues

1. Misleading Title and Scope

• Issue: Title promises “hate speech detection” but content covers general content moderation
• Fix: Either rename to “Content Moderation in Audio Files” or focus specifically on hate speech
• Impact: Users expecting hate speech specifics get generic content moderation info

2. Inconsistent Code Examples

• Issue: Different languages show different approaches (upload vs. direct URL)
• Fix: Standardize all examples to use the same audio file (https://assembly.ai/wildfires.mp3)
• Pain Point: Users can’t follow along consistently across languages

3. Missing Critical Information

Authentication Details

## Before You Begin
- **API Key Location**: Find your API key in [Dashboard Settings](link)
- **Rate Limits**: X requests per minute for free tier
- **Supported Formats**: MP3, WAV, M4A, etc. (max file size: X MB)

Error Handling

Add comprehensive error handling examples:

try:
    transcript = transcriber.transcribe(FILE_URL)
except aai.TranscriptionError as e:
    print(f"Transcription failed: {e}")
except aai.AuthenticationError as e:
    print(f"Invalid API key: {e}")

Structure Improvements

1. Add Prerequisites Section

## Prerequisites
- AssemblyAI account ([sign up free](link))
- API key from your dashboard
- Audio/video file (supported formats: MP3, WAV, M4A, FLAC)
- Programming environment set up

2. Reorganize Steps

Current flow jumps between concepts. Suggest:

1. Setup and authentication
2. Prepare your audio file
3. Configure content moderation
4. Submit transcription request
5. Retrieve and parse results

3. Add Quick Start Section

# 30-second quick start
import assemblyai as aai
aai.settings.api_key = "your-api-key"

config = aai.TranscriptionConfig(content_safety=True)
transcriber = aai.Transcriber(config=config)
transcript = transcriber.transcribe("https://assembly.ai/wildfires.mp3")

for result in transcript.content_safety.results:
    if any(label.label == "hate_speech" for label in result.labels):
        print(f"Hate speech detected: {result.text}")

Content Improvements

1. Better Examples and Context

• Current: Generic placeholder paths like ./my-audio.mp3
• Improved: Use the provided example URL consistently
• Add: Real-world use cases (podcast moderation, user-generated content, etc.)

2. Enhanced Response Documentation

Add field-by-field explanation:

| Field | Type | Description | Example |
|-------|------|-------------|---------|
| `confidence` | float | Model confidence (0-1) | 0.926 |
| `severity` | float | Content severity (0-1) | 0.52 |
| `timestamp` | object | Start/end times in milliseconds | `{start: 650, end: 4970}` |

3. Practical Implementation Guidance

### Recommended Thresholds
- **Low sensitivity**: confidence > 0.7, severity > 0.3
- **Medium sensitivity**: confidence > 0.8, severity > 0.5
- **High sensitivity**: confidence > 0.9, severity > 0.7

### Common Patterns
```python
def flag_content(results):
    flagged_segments = []
    for result in results:
        hate_speech_labels = [l for l in result.labels if l.label == "hate_speech"]
        if hate_speech_labels and hate_speech_labels[0].severity > 0.5:
            flagged_segments.append({
                'text': result.text,
                'timestamp': result.timestamp,
                'severity': hate_speech_labels[0].severity
            })
    return flagged_segments

User Experience Fixes

1. Add Troubleshooting Section

## Common Issues
- **"Invalid API key"**: Check your key in the dashboard
- **"File too large"**: Maximum size is X MB
- **"Unsupported format"**: Use MP3, WAV, M4A, or FLAC
- **"No results returned"**: Audio may not contain detectable speech

2. Add Progress Indicators

import time
print("Uploading audio file...")
# upload code
print("Starting transcription...")
# transcription code
print("Processing content moderation...")
while transcript.status != "completed":
    print(".", end="", flush=True)
    time.sleep(2)
print("\nAnalysis complete!")

3. Link to Related Resources

• Add navigation to related content moderation features
• Link to API reference for each mentioned endpoint
• Provide links to SDKs and GitHub repositories

Missing Technical Details

1. Performance Information

• Processing time estimates
• File size limitations
• Concurrent request limits

2. Security Considerations

• Data retention policies
• HTTPS requirements
• API key security best practices

3. Integration Patterns

• Webhook setup for large files
• Batch processing recommendations
• Real-time streaming options

Quick Wins

1. Fix the empty <Tabs /> tag in Step 2
2. Standardize variable naming across languages
3. Add copy buttons to code blocks
4. Include expected output examples
5. Add estimated reading time
6. Include “What’s Next” section with related guides

This documentation has good bones but needs these improvements to provide a smooth, comprehensive user experience focused on the promised hate speech detection functionality.

---