Feedback: guides-speaker-identification

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/speaker-identification
Category: guides
Generated: 05/08/2025, 4:37:39 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:37:38 pm

Technical Documentation Analysis: Speaker Identification Guide

Overall Assessment

This documentation provides a functional walkthrough but has several clarity, completeness, and user experience issues that need addressing. Here’s my detailed analysis:

🚨 Critical Issues

1. Missing Prerequisites & Setup Information

Current Issue: Vague requirements

* An upgraded [AssemblyAI account](https://www.assemblyai.com/dashboard/signup).

Recommended Fix:

## Prerequisites

* Python 3.7 or higher
* An AssemblyAI account with credits available
* API key from your [AssemblyAI dashboard](https://www.assemblyai.com/dashboard)

### Getting Your API Key
1. Sign up for an AssemblyAI account
2. Navigate to your dashboard
3. Copy your API key from the "API Keys" section
4. Replace `"YOUR-API-KEY"` in the code with your actual key

**Important:** This guide uses LeMUR, which requires account credits. Check your balance before proceeding.

2. Code Structure & Flow Issues

Problem: The code is presented as one continuous block without clear sections or error handling.

Solution: Restructure into logical sections:

# Step 1: Setup and Configuration
import assemblyai as aai
import re

# Validate API key is set
if not aai.settings.api_key or aai.settings.api_key == "YOUR-API-KEY":
    raise ValueError("Please set your actual API key")

# Step 2: Configure transcription
def create_transcription_config():
    """Configure transcription with speaker labels enabled."""
    return aai.TranscriptionConfig(
        speaker_labels=True,
        # Optional: Set minimum speakers if known
        # speakers_expected=2
    )

# Step 3: Transcribe audio
def transcribe_audio(audio_url, config):
    """Transcribe audio and return transcript with speaker labels."""
    transcriber = aai.Transcriber()
    transcript = transcriber.transcribe(audio_url, config)

    if transcript.status == aai.TranscriptStatus.error:
        raise Exception(f"Transcription failed: {transcript.error}")

    return transcript

📝 Missing Information

1. Input Format Requirements

Add a section explaining supported audio formats:

## Supported Audio Formats

This guide works with:
- Audio URLs (direct links to audio files)
- Local audio files (replace `audio_url` with file path)
- Supported formats: MP3, WAV, FLAC, M4A, OGG, WEBM

**Example with local file:**
```python
audio_file = "./path/to/your/audio.mp3"
transcript = transcriber.transcribe(audio_file, config)

2. Error Handling

Add comprehensive error handling:

def safe_transcribe_with_speakers(audio_url):
    """Safely transcribe audio with proper error handling."""
    try:
        transcriber = aai.Transcriber()
        config = aai.TranscriptionConfig(speaker_labels=True)

        print("Starting transcription...")
        transcript = transcriber.transcribe(audio_url, config)

        if transcript.status == aai.TranscriptStatus.error:
            print(f"Transcription failed: {transcript.error}")
            return None

        if not transcript.utterances:
            print("No speaker utterances found in transcript")
            return None

        print(f"Transcription completed. Found {len(transcript.utterances)} utterances")
        return transcript

    except Exception as e:
        print(f"Error during transcription: {e}")
        return None

3. Cost Information

Add a cost awareness section:

## 💰 Cost Considerations

This workflow uses two paid services:
1. **Transcription with Speaker Labels:** ~$0.65 per audio hour
2. **LeMUR Processing:** ~$0.03 per request + token usage

**Tip:** Test with short audio files first to understand costs.

🔧 Improved Examples

1. Complete Working Example

import assemblyai as aai
import re

def identify_speakers_in_audio(audio_url, api_key):
    """Complete function to identify speakers by name in audio."""

    # Setup
    aai.settings.api_key = api_key

    # Step 1: Transcribe with speaker labels
    transcriber = aai.Transcriber()
    config = aai.TranscriptionConfig(speaker_labels=True)

    print("🎙️ Transcribing audio...")
    transcript = transcriber.transcribe(audio_url, config)

    if transcript.status == aai.TranscriptStatus.error:
        raise Exception(f"Transcription failed: {transcript.error}")

    # Step 2: Format transcript for LeMUR
    text_with_speaker_labels = format_transcript_for_lemur(transcript)

    # Step 3: Use LeMUR to identify speakers
    speaker_mapping = identify_speakers_with_lemur(transcript, text_with_speaker_labels)

    # Step 4: Return formatted results
    return format_final_transcript(transcript, speaker_mapping)

def format_transcript_for_lemur(transcript):
    """Format transcript with speaker labels for LeMUR processing."""
    formatted_text = ""
    for utterance in transcript.utterances:
        formatted_text += f"Speaker {utterance.speaker}: {utterance.text}\n\n"
    return formatted_text

# Usage example
if __name__ == "__main__":
    audio_url = "https://example.com/your-audio-file.mp3"
    api_key = "your-actual-api-key"

    try:
        results = identify_speakers_in_audio(audio_url, api_key)
        for speaker, text in results[:5]:  # Show first 5 utterances
            print(f"{speaker}: {text[:100]}...")
    except Exception as e:
        print(f"Error: {e}")

🏗️ Structure Improvements

Recommended New Structure:

# Identify Speaker Names From Audio Transcripts

## Overview
Brief explanation of what this accomplishes and when to use it.

## Prerequisites
Detailed requirements and setup steps.

## Quick Start
Minimal working example for users who want to try it immediately.

## Step-by-Step Guide
1. **Transcribe Audio with Speaker Labels**
2. **Format Transcript for LeMUR**
3. **Identify Speakers Using LeMUR**
4. **Map Speaker Names to Transcript**

## Complete Code Example
Full working implementation with error handling.

## Troubleshooting
Common issues and solutions.

## Advanced Usage
- Handling large files
- Customizing LeMUR prompts
- Working with known speaker counts

## Cost Optimization Tips

## API Reference
Links to relevant API documentation.

🎯 User Pain Points & Solutions

1. Unclear LeMUR Context

Issue: Users don’t understand what LeMUR is or why it’s needed.

Fix: Add explanation:

## What is LeMUR?

LeMUR is AssemblyAI's Large Language Model service that can analyze transcripts and answer questions about them. We use it here to:
- Analyze speaker-labeled transcripts
- Infer speaker identities from conversation context
- Map generic "Speaker A/B" labels to actual names

2. Limited Output Examples

Issue: Only shows truncated output.

Fix: Provide complete before/after examples:

## Expected Output

**Before (generic labels):**

Speaker A: Hi everyone, welcome to today’s podcast Speaker B: Thanks for having me, Sarah

**After (identified names):**

Sarah Johnson: Hi everyone, welcome to today’s podcast Dr. Mike Chen: Thanks for having me, Sarah

### 3. **No Validation or Quality Checks**
Add quality assurance section:
```python
def validate_speaker_identification(speaker_mapping, confidence_threshold=0

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