Feedback: guides-speaker-diarization-with-async-chunking

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/speaker-diarization-with-async-chunking
Category: guides
Generated: 05/08/2025, 4:37:37 pm

---

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:37:36 pm

Technical Documentation Analysis & Improvement Recommendations

Critical Issues

1. Missing Prerequisites & Setup Information

Problems:

• No system requirements specified
• Installation commands are incomplete and potentially incorrect
• No environment setup guidance

Solutions:

## Prerequisites

### System Requirements
- Python 3.8 or higher
- CUDA-compatible GPU (recommended for TitaNet model)
- At least 4GB RAM
- 2GB free disk space

### Installation
```bash
# Install PyTorch (check pytorch.org for your system)
pip install torch torchvision torchaudio

# Install NeMo toolkit
pip install nemo-toolkit[asr]

# Install other dependencies
pip install assemblyai pydub requests

# Install system dependencies
# On Ubuntu/Debian:
sudo apt update && sudo apt install ffmpeg

# On macOS:
brew install ffmpeg

#### 2. **Unclear Workflow and Use Case**
**Problems:**
- No explanation of what "async chunking" means
- Missing context about when to use this approach
- No overview of the complete workflow

**Solutions:**
Add this section after the introduction:

```markdown
## What is Async Chunking with Speaker Diarization?

Async chunking allows you to process multiple audio segments independently while maintaining speaker identity consistency across segments. This is useful for:

- Long recordings split into chunks for processing
- Multiple recordings from the same meeting or conversation
- Streaming audio that needs to be processed in real-time segments

### How It Works
1. **Transcribe** each audio chunk with AssemblyAI's speaker diarization
2. **Extract** the longest monologue from each speaker per chunk
3. **Compare** speaker embeddings using TitaNet across chunks
4. **Unify** speaker labels across all chunks based on voice similarity

### Workflow Overview

Audio Chunks → AssemblyAI Transcription → Speaker Monologue Extraction → TitaNet Embedding Comparison → Unified Speaker Labels → Final Transcript

3. Incomplete Input/Output Documentation

Problems:

• No explanation of required input formats
• Missing information about transcript ID generation
• No examples of expected outputs

Solutions:

## Input Requirements

### Audio Files
- **Format**: WAV, MP3, MP4, or other common audio formats
- **Duration**: Any length (will be processed in chunks)
- **Quality**: 16kHz or higher sampling rate recommended
- **Channels**: Mono or stereo (will be converted to mono automatically)

### AssemblyAI Transcript IDs
Before using this guide, you need to submit your audio files to AssemblyAI with speaker diarization enabled:

```python
import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

config = aai.TranscriptionConfig(
    speaker_labels=True,  # Enable speaker diarization
    speakers_expected=2   # Optional: hint about number of speakers
)

transcriber = aai.Transcriber(config=config)
transcript = transcriber.transcribe("path/to/your/audio.wav")

print(f"Transcript ID: {transcript.id}")

Expected Output

The final output will be utterances with consistent speaker labels across all chunks:

{
    0: [  # First chunk
        {'speaker': 'A', 'text': 'Hello, how are you?', 'start': 100, 'end': 2000},
        {'speaker': 'B', 'text': 'I am doing well, thanks!', 'start': 2100, 'end': 4000}
    ],
    1: [  # Second chunk
        {'speaker': 'A', 'text': 'That is great to hear.', 'start': 100, 'end': 1800},
        {'speaker': 'B', 'text': 'How about you?', 'start': 1900, 'end': 2500}
    ]
}

### **Code Quality Issues**

#### 4. **Poor Error Handling and Edge Cases**

**Problems:**
- No error handling for file operations
- Missing validation for inputs
- No handling of edge cases

**Solutions:**

```python
def get_transcript(transcript_id):
    """
    Retrieve a completed transcript from AssemblyAI.

    Args:
        transcript_id (str): The AssemblyAI transcript ID

    Returns:
        dict: The completed transcript data

    Raises:
        ValueError: If transcript_id is invalid
        RuntimeError: If transcription failed or timed out
    """
    if not transcript_id or not isinstance(transcript_id, str):
        raise ValueError("transcript_id must be a non-empty string")

    polling_endpoint = f"https://api.assemblyai.com/v2/transcript/{transcript_id}"
    max_attempts = 120  # 6 minutes maximum wait time
    attempt = 0

    while attempt < max_attempts:
        try:
            response = requests.get(polling_endpoint, headers=headers, timeout=30)
            response.raise_for_status()
            transcription_result = response.json()

            if transcription_result['status'] == 'completed':
                if not transcription_result.get('utterances'):
                    raise RuntimeError("Transcript completed but contains no utterances. Ensure speaker_labels=True was set.")
                return transcription_result

            elif transcription_result['status'] == 'error':
                raise RuntimeError(f"Transcription failed: {transcription_result.get('error', 'Unknown error')}")

            else:
                print(f"Transcript status: {transcription_result['status']}. Waiting...")
                time.sleep(3)
                attempt += 1

        except requests.RequestException as e:
            raise RuntimeError(f"Network error while fetching transcript: {e}")

    raise RuntimeError(f"Transcript did not complete within {max_attempts * 3} seconds")

5. Configuration and Customization

Problems:

• Hard-coded values throughout the code
• No configuration options explained
• Missing parameter documentation

Solutions:

class SpeakerDiarizationConfig:
    """Configuration class for speaker diarization settings."""

    def __init__(self,
                 min_monologue_length_ms: int = 2000,
                 similarity_threshold: float = 0.7,
                 max_speakers_per_chunk: int = 10,
                 audio_sample_rate: int = 16000):
        """
        Initialize configuration.

        Args:
            min_monologue_length_ms: Minimum length of monologue to consider for speaker comparison
            similarity_threshold: Threshold for speaker similarity (0.0-1.0)
            max_speakers_per_chunk: Maximum expected speakers per audio chunk
            audio_sample_rate: Target sample rate for audio processing
        """
        self.min_monologue_length_ms = min_monologue_length_ms
        self.similarity_threshold = similarity_threshold
        self.max_speakers_per_chunk = max_speakers_per_chunk
        self.audio_sample_rate = audio_sample_rate

# Usage example
config = SpeakerDiarizationConfig(
    min_monologue_length_ms=3000,  # Require 3 seconds minimum
    similarity_threshold=0.8       # Higher threshold for stricter matching
)

Documentation Structure Improvements

6. Better Section Organization

Recommended Structure:

# Use Speaker Diarization with Async Chunking

## Overview
[What this guide covers and use cases]

## Prerequisites
[System requirements, accounts needed]

## Quick Start
[Minimal working example with explanation]

## Installation & Setup
[Detailed installation steps]

## Step-by-Step Tutorial
### 1. Prepare Your Audio Files
### 2. Generate AssemblyAI Transcripts
### 3. Configure Speaker Comparison
### 4. Process Audio Chunks
### 5. Review Results

## Configuration Options
[All customizable parameters explained]

## Troubleshooting
[Common issues and solutions]

## Advanced Usage
[

---