Feedback: guides-speaker_timeline

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/speaker_timeline
Category: guides
Generated: 05/08/2025, 4:36:56 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:36:55 pm

Documentation Analysis: Speaker Timeline Guide

Overall Assessment

This guide provides a functional example but lacks depth, context, and user-friendly explanations. Here’s my detailed feedback:

Critical Missing Information

1. Prerequisites & Dependencies

# Add this section at the beginning
"""
Requirements:
- Python 3.7+
- assemblyai>=0.17.0
- matplotlib>=3.5.0
- Valid AssemblyAI API key with speaker diarization credits
"""

2. Expected Output/Visual Example

• Missing: Screenshot or description of what the final plot looks like
• Add: Sample output image with explanation of visual elements
• Include: Description of how to interpret the timeline

3. Error Handling

The current code will fail silently in several scenarios:

def plot_speaker_timeline(utterances):
    # Add input validation
    if not utterances:
        print("No utterances found. Check if speaker_labels=True and audio contains speech.")
        return

    # Add error handling for transcription failures
    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        return

Unclear Explanations

1. Time Unit Conversion

Current: start = utterance.start / 60000 # in minutes Better:

# Convert from milliseconds to minutes for better readability
start_minutes = utterance.start / 60000  # AssemblyAI returns timestamps in milliseconds

2. Speaker Diarization Concept

Add explanation:

Speaker diarization identifies “who spoke when” in audio recordings. Each utterance represents a continuous speech segment from one speaker.

3. Color Assignment Logic

Current: Confusing modulo operation Better:

# Cycle through colors if we have more speakers than available colors
if speaker not in speaker_colors:
    color_index = len(speaker_colors) % len(colors)
    speaker_colors[speaker] = colors[color_index]
    print(f"Assigned {colors[color_index]} to {speaker}")

Improved Structure Suggestions

1. Reorganize Sections

# Plot A Speaker Timeline with Matplotlib

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

## Prerequisites
- Requirements and setup

## Quick Start
- Complete working example

## Step-by-Step Tutorial
- Detailed breakdown

## Customization Options
- How to modify the visualization

## Troubleshooting
- Common issues and solutions

## Next Steps
- Related features and advanced usage

2. Better Code Organization

# Separate configuration from main logic
class SpeakerTimelinePlotter:
    def __init__(self, figsize=(12, 4), colors=None):
        self.figsize = figsize
        self.colors = colors or ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']

    def plot(self, utterances, title="Speaker Timeline"):
        # Implementation here
        pass

Enhanced Examples

1. Multiple Audio Format Support

# Support various audio formats and URLs
audio_sources = [
    "./my-audio.mp3",           # Local file
    "./interview.wav",          # Different format
    "https://example.com/audio.mp3"  # Remote URL
]

for audio in audio_sources:
    transcript = transcriber.transcribe(audio, config)
    if transcript.utterances:
        plot_speaker_timeline(transcript.utterances)

2. Customization Examples

# Example: Custom styling
def plot_speaker_timeline_custom(utterances, show_text=True, time_unit='seconds'):
    """
    Enhanced version with customization options

    Args:
        utterances: List of utterance objects from AssemblyAI
        show_text: Whether to display speaker labels on bars
        time_unit: 'seconds' or 'minutes' for time axis
    """
    # Implementation with options

User Pain Points & Solutions

1. API Key Management

Problem: Hardcoded API keys Solution:

import os
from dotenv import load_dotenv

load_dotenv()
aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")

if not aai.settings.api_key:
    raise ValueError("Please set ASSEMBLYAI_API_KEY environment variable")

2. File Path Issues

Problem: Relative paths may not work Solution:

import os
from pathlib import Path

# Validate file exists
audio_file = Path("./my-audio.mp3")
if not audio_file.exists():
    raise FileNotFoundError(f"Audio file not found: {audio_file}")

3. Large Audio Files

Problem: No guidance on processing time Solution:

# Add progress indication
print("Starting transcription... This may take a few minutes for large files.")
transcript = transcriber.transcribe(audio_file, config)

# Show processing status
while transcript.status not in [aai.TranscriptStatus.completed, aai.TranscriptStatus.error]:
    print("Processing...")
    time.sleep(5)
    transcript = aai.Transcript.get_by_id(transcript.id)

Additional Improvements

1. Add Interactive Features

# Make plot interactive
import matplotlib.patches as mpatches

def plot_interactive_timeline(utterances):
    # Add click handlers, hover tooltips
    # Show utterance text on hover
    pass

2. Export Options

# Save plot to file
plt.savefig('speaker_timeline.png', dpi=300, bbox_inches='tight')
print("Timeline saved as 'speaker_timeline.png'")

3. Performance Considerations

## Performance Notes
- Speaker diarization adds ~30% to transcription time
- Minimum audio length: 10 seconds for reliable results
- Maximum speakers detected: ~10 (accuracy decreases with more speakers)

4. Add Troubleshooting Section

## Common Issues
- **No utterances returned**: Check audio quality and length
- **All speakers labeled as 'A'**: Audio may have only one speaker
- **Plot not displaying**: Ensure you're in an environment that supports matplotlib display

This enhanced documentation would provide a much better user experience with clearer explanations, better error handling, and more practical examples.

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