Feedback: speech-to-text-pre-recorded-audio-speaker-diarization

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/speech-to-text/pre-recorded-audio/speaker-diarization
Category: speech-to-text
Generated: 05/08/2025, 4:24:15 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:24:14 pm

I’ll provide a comprehensive analysis of this Speaker Diarization documentation with specific, actionable feedback.

Overall Assessment

The documentation provides solid technical coverage but has several areas for improvement in clarity, structure, and user experience. Here’s my detailed analysis:

1. Structure & Organization Issues

Missing Content Hierarchy

Problem: The document jumps from basic setup to advanced parameters without clear progression.

Solution: Restructure with clear sections:

## Getting Started
- What is Speaker Diarization?
- When to use it
- Prerequisites

## Basic Implementation
- Simple setup examples
- Understanding the output

## Advanced Configuration
- Setting speaker counts
- Performance optimization

## Troubleshooting & Best Practices

Poor Parameter Documentation Flow

Problem: speakers_expected and speaker_options are introduced separately without explaining their relationship.

Solution: Add a “Parameter Overview” section:

## Configuration Parameters

Speaker Diarization offers three approaches for speaker detection:

| Parameter | Use Case | Example |
|-----------|----------|---------|
| `speaker_labels: true` | Auto-detect (1-10 speakers) | General use |
| `speakers_expected: N` | Known exact count | Meeting with 3 people |
| `speaker_options: {min, max}` | Known range | Panel discussion (4-6 speakers) |

⚠️ **Important**: Don't use `speakers_expected` and `speaker_options` together.

2. Missing Critical Information

Pricing & Usage Limits

Problem: No mention of cost implications or usage restrictions.

Solutions:

• Add pricing information or link to pricing page
• Mention any rate limits or quota restrictions
• Clarify if this is a premium feature

Audio Requirements

Problem: Vague guidance on optimal audio conditions.

Solution: Add specific requirements:

## Audio Requirements & Best Practices

### Optimal Conditions
- **Audio quality**: 16kHz+ sampling rate, minimal background noise
- **Speaker duration**: Each speaker should speak for 30+ seconds total
- **Speaker separation**: Avoid overlapping speech when possible
- **File formats**: Supports MP3, WAV, M4A, FLAC

### Scenarios That May Reduce Accuracy
- Cross-talk or interruptions
- Similar-sounding speakers
- Echo or poor acoustics
- Single-word responses ("yes", "okay")

Error Handling

Problem: No guidance on handling common errors.

Solution: Add error handling section:

## Error Handling

Common errors and solutions:

| Error | Cause | Solution |
|-------|-------|----------|
| `400: Invalid speaker configuration` | Using both `speakers_expected` and `speaker_options` | Use only one parameter |
| `422: Multichannel conflict` | Both diarization and multichannel enabled | Disable one feature |

3. Code Examples Issues

Inconsistent Error Handling

Problem: Some language examples lack proper error handling.

Solution: Standardize error handling across all examples:

# Add to Python examples
try:
    transcript = aai.Transcriber().transcribe(audio_file, config)
    if transcript.status == 'error':
        print(f"Error: {transcript.error}")
        return

    for utterance in transcript.utterances:
        print(f"Speaker {utterance.speaker}: {utterance.text}")
except Exception as e:
    print(f"Request failed: {e}")

Missing Real-World Examples

Problem: Examples use placeholder audio without context.

Solution: Add scenario-based examples:

## Use Case Examples

### Meeting Transcription (Known Participants)
```python
# 3 people in a business meeting
config = aai.TranscriptionConfig(
    speaker_labels=True,
    speakers_expected=3
)

Podcast Interview (Variable Guests)

# Host + 1-3 guests
config = aai.TranscriptionConfig(
    speaker_labels=True,
    speaker_options={
        "min_speakers_expected": 2,
        "max_speakers_expected": 4
    }
)

4. User Experience Pain Points

Unclear Output Format

Problem: The JSON response example is overwhelming and lacks explanation.

Solution: Add progressive examples:

## Understanding the Output

### Simple Example
For a 2-speaker conversation, you'll receive:
```json
{
  "utterances": [
    {
      "speaker": "A",
      "text": "Hello, how are you?",
      "start": 250,
      "end": 1500,
      "confidence": 0.95
    },
    {
      "speaker": "B",
      "text": "I'm doing well, thanks!",
      "start": 2000,
      "end": 3200,
      "confidence": 0.92
    }
  ]
}

Key Points:

• Speakers are labeled A, B, C, etc.
• Times are in milliseconds
• Confidence ranges from 0-1 (higher is better)

### No Performance Expectations
**Problem**: Users don't know what to expect in terms of processing time or accuracy.

**Solution**: Add expectations section:
```markdown
## What to Expect

### Processing Time
- Typically 15-30% of audio length
- Longer for complex audio with many speakers

### Accuracy Guidelines
- **High accuracy**: Clear audio, distinct speakers, minimal overlap
- **Reduced accuracy**: Background noise, similar voices, frequent interruptions

### Speaker Limits
- Default: 1-10 speakers automatically detected
- Maximum recommended: 20 speakers (may impact accuracy)

5. Specific Technical Improvements

Add Validation Guidelines

## Validating Results

Check your results for quality:
```python
def validate_diarization(transcript):
    if not transcript.utterances:
        print("Warning: No utterances detected")
        return False

    speakers = set(u.speaker for u in transcript.utterances)
    avg_confidence = sum(u.confidence for u in transcript.utterances) / len(transcript.utterances)

    print(f"Detected {len(speakers)} speakers: {sorted(speakers)}")
    print(f"Average confidence: {avg_confidence:.2f}")

    if avg_confidence < 0.7:
        print("Warning: Low confidence scores detected")

    return True

Add Integration Patterns

## Common Integration Patterns

### Batch Processing
```python
def process_multiple_files(audio_files):
    results = []
    for file in audio_files:
        config = aai.TranscriptionConfig(speaker_labels=True)
        transcript = aai.Transcriber().transcribe(file, config)
        results.append({
            'file': file,
            'speakers': len(set(u.speaker for u in transcript.utterances)),
            'utterances': transcript.utterances
        })
    return results

6. Quick Fixes

1. Add navigation links at the top to jump to sections
2. Include estimated read time (currently ~8-10 minutes)
3. Add “Next Steps” section linking to related features
4. Create a troubleshooting checklist for common issues
5. Add copy buttons to all code examples
6. Include audio file size recommendations (optimal range)

Priority Implementation Order

1. High Priority: Add missing audio requirements and error handling
2. Medium Priority: Restructure content hierarchy and improve examples
3. Low Priority: Add performance expectations and advanced integration patterns

This restructure would significantly improve user success rates and reduce support requests while maintaining the technical depth developers need.

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