Feedback: speech-to-text-pre-recorded-audio-word-level-timestamps

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/speech-to-text/pre-recorded-audio/word-level-timestamps
Category: speech-to-text
Generated: 05/08/2025, 4:23:30 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:23:29 pm

Technical Documentation Analysis: Word-Level Timestamps

Overall Assessment

This documentation demonstrates how to access word-level timestamps from speech-to-text transcriptions across multiple programming languages. While comprehensive in code examples, it has several areas for improvement in structure, clarity, and user guidance.

Specific Actionable Feedback

1. Missing Information

Critical Gaps:

• No introduction or overview explaining what word-level timestamps are and their use cases
• Missing configuration requirements - Does this feature need to be explicitly enabled?
• No error handling guidance for when word data is unavailable
• Pricing/limitations information - Are there additional costs or usage limits?
• Audio format compatibility - Which formats support word-level timestamps?

Add this section at the beginning:

## Overview
Word-level timestamps provide precise start and end times for each word in your transcription, enabling:
- Synchronized subtitle generation
- Audio highlighting and navigation
- Precise content editing
- Accessibility features

This feature is automatically included with all transcriptions at no additional cost.

2. Unclear Explanations

Issues:

• Timestamp units unclear - The JSON shows values like 240, 640 but doesn’t specify these are milliseconds
• Confidence score interpretation - What do values like 0.70473 mean in practice?
• Speaker field explanation - Why is it null in examples?

Improvements needed:

## Understanding the Data
- **start/end**: Timestamps in milliseconds from audio beginning
- **confidence**: Accuracy score from 0.0-1.0 (0.8+ considered high confidence)
- **speaker**: Speaker identifier when Speaker Diarization is enabled, otherwise null

3. Better Examples

Current issues:

• All examples use the same generic audio file
• No real-world use case demonstrations
• Missing example output formatting

Recommended additions:

# Example: Generate SRT subtitles
def create_srt_from_words(words, words_per_subtitle=8):
    subtitles = []
    for i in range(0, len(words), words_per_subtitle):
        chunk = words[i:i+words_per_subtitle]
        start_time = format_timestamp(chunk[0].start)
        end_time = format_timestamp(chunk[-1].end)
        text = ' '.join([word.text for word in chunk])
        subtitles.append(f"{start_time} --> {end_time}\n{text}")
    return subtitles

# Example: Filter low-confidence words
high_confidence_words = [
    word for word in transcript.words
    if word.confidence > 0.8
]

4. Improved Structure

Current structure issues:

• Code examples come before explanation
• No logical flow from basic to advanced usage
• Missing navigation aids

Recommended structure:

# Word-Level Timestamps

## Overview
[What it is and why use it]

## Quick Start
[Minimal working example]

## Understanding the Response
[Data structure explanation]

## Code Examples
[Multiple language examples]

## Common Use Cases
[Practical applications with code]

## Troubleshooting
[Common issues and solutions]

## API Reference
[Technical specifications]

5. Potential User Pain Points

Identified issues:

a) No activation guidance:

## Configuration
Word-level timestamps are automatically included in all transcription responses. No additional configuration required.

However, ensure your transcription request includes:
- Valid audio format (MP3, WAV, FLAC, etc.)
- Audio quality sufficient for word-level detection

b) Missing error scenarios:

# Handle missing word data
if hasattr(transcript, 'words') and transcript.words:
    for word in transcript.words:
        print(f"Word: {word.text}")
else:
    print("Word-level data not available for this transcription")

c) No performance guidance:

## Best Practices
- **Large files**: Consider processing words in chunks to avoid memory issues
- **Real-time applications**: Buffer word data to prevent UI blocking
- **Storage**: Word arrays can be large; consider storing only essential fields

d) Inconsistent code patterns:

• Some examples use different variable names
• Error handling varies between languages
• Missing imports in some examples

6. Additional Recommendations

Add these sections:

Filtering and Processing:

## Working with Word Data

### Filter by confidence
### Group words by time ranges
### Export to common formats (SRT, VTT, JSON)

Integration Examples:

## Common Integrations
- Video player synchronization
- Search and highlight functionality
- Automated captioning systems

Troubleshooting Section:

## Troubleshooting
- **Empty words array**: Check audio quality and format compatibility
- **Inaccurate timestamps**: Verify audio encoding and sample rate
- **Missing confidence scores**: Normal for some audio types

Priority Implementation Order

1. High Priority: Add overview section and timestamp unit clarification
2. Medium Priority: Restructure content flow and add use case examples
3. Low Priority: Add advanced filtering examples and troubleshooting guide

This documentation has good technical coverage but needs better user onboarding and practical guidance to reduce implementation friction.

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