Feedback: guides-Use_AssemblyAI_with_Pyannote_to_generate_custom_Speaker_Labels

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/Use_AssemblyAI_with_Pyannote_to_generate_custom_Speaker_Labels
Category: guides
Generated: 05/08/2025, 4:43:56 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:43:55 pm

Technical Documentation Analysis & Recommendations

Overall Assessment

This documentation provides a functional code example but lacks the structure and clarity needed for effective technical documentation. The current format jumps between complete code and step-by-step explanations in an inconsistent manner.

Critical Issues & Recommendations

1. Structural Problems

Issues:

• Duplicated code between “Quickstart” and “Step-by-Step Instructions”
• Inconsistent organization with complete code shown twice
• Missing clear separation between overview and implementation

Recommendations:

# Suggested Structure:
1. Introduction & Use Cases
2. Prerequisites & Setup
3. Quick Start (minimal working example)
4. Detailed Implementation Guide
5. Configuration Options
6. Troubleshooting
7. Advanced Usage

2. Missing Critical Information

Prerequisites Section Needs:

• Python version requirements
• System requirements (GPU vs CPU considerations)
• Audio file format requirements and limitations
• Expected processing time estimates
• Memory requirements

Add this information:

## System Requirements
- Python 3.8+
- GPU recommended for faster processing (optional)
- Minimum 4GB RAM for typical audio files
- Supported audio formats: WAV, MP3, M4A, FLAC
- Maximum file size: 512MB

3. Setup Instructions Are Incomplete

Current Issues:

• No guidance on environment variable setup
• Missing HuggingFace model acceptance process details
• No troubleshooting for common setup issues

Improved Setup Section:

## Environment Setup

### 1. Install Dependencies
```bash
pip install assemblyai pyannote.audio torch pandas numpy

2. Set Environment Variables

Create a .env file or set environment variables:

export ASSEMBLYAI_API_KEY="your_assemblyai_key_here"
export HF_TOKEN="your_huggingface_token_here"

3. Accept HuggingFace Model Terms

1. Visit pyannote/speaker-diarization
2. Click “Agree and access repository”
3. Fill out the required form with your details
4. Repeat for pyannote/segmentation

### 4. **Code Examples Need Improvement**

**Issues:**
- Hardcoded file paths without explanation
- No error handling examples
- Missing input validation
- No example of handling different audio formats

**Enhanced Code Example:**
```python
import os
from pathlib import Path

def validate_audio_file(audio_file):
    """Validate audio file exists and has supported format."""
    if not Path(audio_file).exists():
        raise FileNotFoundError(f"Audio file not found: {audio_file}")

    supported_formats = ['.wav', '.mp3', '.m4a', '.flac']
    if not any(audio_file.lower().endswith(fmt) for fmt in supported_formats):
        raise ValueError(f"Unsupported format. Use: {', '.join(supported_formats)}")

# Usage example with error handling
try:
    audio_file = "path/to/your/audio.wav"
    validate_audio_file(audio_file)
    transcript = transcribe_audio(audio_file, language="hr")
    result = get_speaker_labels(audio_file, transcript)
    print(result)
except Exception as e:
    print(f"Error: {e}")

5. Missing Configuration Options

Add comprehensive configuration section:

## Configuration Options

### AssemblyAI Options
- `speech_model`: Choose between 'best', 'nano' (faster, less accurate)
- `language_code`: ISO language code (e.g., 'en', 'hr', 'es', 'fr')

### Pyannote Options
- `num_speakers`: Set exact number of speakers
- `min_speakers` / `max_speakers`: Set speaker range
- Device selection: GPU vs CPU

### Example Configurations
```python
# For faster processing
config = aai.TranscriptionConfig(
    speech_model='nano',
    language_code='hr'
)

# For better accuracy
config = aai.TranscriptionConfig(
    speech_model='best',
    language_code='hr'
)

6. Add Troubleshooting Section

## Troubleshooting

### Common Issues

**"Failed to initialize pipeline" Error**
- Verify HuggingFace token is valid
- Ensure you've accepted model terms and conditions
- Check internet connection

**Out of Memory Errors**
- Use CPU instead of GPU: `device = torch.device("cpu")`
- Process shorter audio segments
- Reduce audio quality/sample rate

**Poor Speaker Separation**
- Ensure clear audio with distinct speakers
- Try setting `num_speakers` parameter
- Check audio isn't mono (stereo preferred)

**Slow Processing**
- Use GPU if available
- Use 'nano' model for faster transcription
- Process shorter audio segments

7. Output Format Documentation

Add section explaining output:

## Output Format

The generated transcript follows this format:

[HH:MM:SS] SPEAKER XX: Transcribed text here

### Customizing Output Format
You can modify the output format by changing the `format_timestamp` function:

```python
# Alternative formats
f"[{speaker_label}] ({timestamp}): {text}"  # Speaker first
f"{timestamp} | {speaker_label}: {text}"    # Pipe separator

8. Performance Considerations

Add performance section:

## Performance Guidelines

### File Size Recommendations
- **Small files (< 10 minutes)**: Process directly
- **Medium files (10-60 minutes)**: Expect 2-5x processing time
- **Large files (> 60 minutes)**: Consider splitting into segments

### Hardware Recommendations
- **CPU only**: 4+ cores recommended
- **With GPU**: NVIDIA GPU with 4GB+ VRAM
- **RAM**: 8GB+ for files longer than 30 minutes

9. Language Support Clarity

Clarify language support:

## Supported Languages

This solution works with any language supported by AssemblyAI for transcription. Common codes:
- `en`: English
- `es`: Spanish
- `fr`: French
- `de`: German
- `hr`: Croatian
- `pt`: Portuguese

[View full language list](link-to-assemblyai-language-docs)

10. Add Real-World Examples

Include practical examples:

• Meeting transcription
• Podcast processing
• Interview analysis
• Different audio quality scenarios

These improvements would transform this from a code dump into comprehensive, user-friendly documentation that guides users through successful implementation while anticipating and addressing common issues.

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