Transcription File Formats: Complete Guide to TXT, SRT, VTT & JSON

Format Overview & Quick Comparison

TXT (Plain Text) Format

What is TXT Format?

Plain text (TXT) is the simplest and most universally accepted transcript format. It contains only the spoken text without timing information, formatting, or metadata. TXT files are easily read by screen readers and compatible with all text editors and word processors.

File Structure Example

Speaker 1: Welcome to today's podcast episode.

Speaker 2: Thanks for having me. I'm excited to discuss the future of AI transcription.

Speaker 1: Let's start with the basics. What makes modern transcription different from traditional methods?

Speaker 2: The key difference is accuracy and speed. AI-powered systems can now achieve 95-98% accuracy in real-time.

When to Use TXT Format

• ✓ Accessibility compliance: TXT files are perfectly accessible to screen readers and assistive technologies
• ✓ Simple documentation: Meeting notes, interview records, lecture documentation
• ✓ Content creation: Easy to copy/paste into blog posts, articles, or content management systems
• ✓ SEO optimization: Search engines easily index TXT content for better discoverability
• ✓ Universal compatibility: Works on any device, any platform, any text editor

Best Practices for TXT Transcripts

SRT (SubRip) Format

What is SRT Format?

SRT (SubRip Text) is the most widely supported subtitle format, originating from DVD-authoring software in the early 2000s. It stores subtitle text with precise timing information, making it ideal for video content. SRT files work with virtually all video players and platforms, from legacy DVD players to modern streaming services.

File Structure Example

1
00:00:00,000 --> 00:00:03,500
Welcome to today's podcast episode.

2
00:00:03,500 --> 00:00:07,200
Thanks for having me. I'm excited to
discuss the future of AI transcription.

3
00:00:07,500 --> 00:00:12,000
Let's start with the basics. What makes
modern transcription different?

4
00:00:12,000 --> 00:00:17,800
The key difference is accuracy and speed.
AI systems can now achieve 95-98% accuracy.

Technical Specifications

When to Use SRT Format

• ✓ YouTube & social media: Universally supported across all major video platforms
• ✓ Offline video playback: Works with VLC, Windows Media Player, and legacy DVD players
• ✓ Video editing workflows: Compatible with Adobe Premiere, Final Cut Pro, DaVinci Resolve
• ✓ Maximum compatibility: Supported by the widest range of devices and platforms
• ✓ SEO for videos: Search engines can index SRT content to improve video discoverability

SRT Best Practices

VTT (WebVTT) Format

What is VTT Format?

WebVTT (Web Video Text Tracks) is the HTML5 standard for web-based video captions, created around 2010 as an evolution of SRT. VTT supports advanced styling, positioning, and metadata capabilities, making it ideal for modern web applications and interactive video content.

File Structure Example

WEBVTT

NOTE This is a sample VTT file with styling

STYLE
::cue {
  background-color: rgba(0,0,0,0.8);
  color: white;
  font-family: Arial;
}

1
00:00:00.000 --> 00:00:03.500
Welcome to today's podcast episode.

2
00:00:03.500 --> 00:00:07.200 position:50% align:middle
Thanks for having me. I'm excited to
discuss the future of AI transcription.

3
00:00:07.500 --> 00:00:12.000
<v Speaker 1>Let's start with the basics. What makes
modern transcription different?</v>

Technical Specifications

When to Use VTT Format

• ✓ HTML5 video players: Native support in modern browsers (Chrome, Firefox, Safari, Edge)
• ✓ Website video content: Embedded videos requiring custom styling and positioning
• ✓ Interactive captions: Videos with chapters, navigation points, or clickable links
• ✓ Advanced styling needs: Custom fonts, colors, backgrounds, and positioning
• ✓ Multi-language support: Better handling of right-to-left languages (Arabic, Hebrew)

VTT Advanced Features

JSON Format

What is JSON Format?

JSON (JavaScript Object Notation) is a structured data format that stores transcription text along with detailed metadata including word-level timestamps, confidence scores, and speaker information. This format is ideal for programmatic access, AI analysis, and NLP processing workflows.

File Structure Example

{
  "segments": [
    {
      "id": 0,
      "start": 0.0,
      "end": 3.5,
      "text": "Welcome to today's podcast episode.",
      "speaker": "SPEAKER_00",
      "words": [
        {
          "word": "Welcome",
          "start": 0.0,
          "end": 0.5,
          "score": 0.98
        },
        {
          "word": "to",
          "start": 0.5,
          "end": 0.7,
          "score": 0.99
        }
      ]
    }
  ],
  "language": "en",
  "duration": 180.5
}

Technical Specifications

When to Use JSON Format

• ✓ AI and machine learning: Structured data for NLP analysis, sentiment detection, topic extraction
• ✓ Programmatic processing: Automated workflows, custom applications, data integration
• ✓ Word-level analysis: Precise timestamp mapping, speech pattern analysis, filler word detection
• ✓ Quality assurance: Confidence scores for identifying low-accuracy sections
• ✓ Speaker analytics: Track who spoke when, speaking time distribution, turn-taking analysis

JSON Advantages for AI Workflows

AI Prompts for Format Optimization

Prompt #1: Format Conversion & Optimization

Convert between transcript formats while optimizing for specific use cases and maintaining quality.

I have a transcript in [SOURCE FORMAT] that I need to convert to [TARGET FORMAT] for [SPECIFIC USE CASE].

Source Format: [TXT/SRT/VTT/JSON]
Target Format: [TXT/SRT/VTT/JSON]
Use Case: [YouTube upload / Website embedding / AI analysis / Documentation]

Please convert the transcript while:
1. Preserving all content accuracy
2. Optimizing timing for readability (if applicable)
3. Adding proper formatting for the target platform
4. Following best practices for [TARGET FORMAT]
5. Maintaining speaker identification if present

Additional Requirements:
- Subtitle duration: [2-3 seconds per caption / custom timing]
- Reading speed: [15-20 characters per second / custom]
- Styling needs: [Basic / Advanced CSS / None]
- Character encoding: [UTF-8 / other]

Here's the source transcript:
[PASTE YOUR TRANSCRIPT HERE]

Please provide the converted transcript ready for immediate use.

Prompt #2: Subtitle Timing Optimization

Improve subtitle readability by optimizing timing, line breaks, and reading speed for SRT/VTT formats.

Please optimize this SRT/VTT subtitle file for maximum readability and professional quality.

Optimization Goals:
1. Timing: Maintain 2-3 seconds per subtitle (minimum 1s, maximum 6s)
2. Reading Speed: 15-20 characters per second
3. Line Length: Maximum 42 characters per line
4. Line Breaks: Split at natural phrase boundaries
5. Gaps: Add 0.3-0.5 second gaps between subtitles
6. Format: Maximum 2 lines per subtitle

Target Platform: [YouTube / Website / DVD / Broadcast]
Language: [English / Other]
Content Type: [Interview / Lecture / Podcast / Meeting]

Please also:
- Fix any overlapping timestamps
- Ensure proper synchronization with speech
- Remove unnecessary line breaks
- Optimize for comfortable reading pace
- Follow professional subtitle formatting standards

Here's the subtitle file:
[PASTE YOUR SRT/VTT CONTENT HERE]

Return the optimized subtitle file ready for upload.

Prompt #3: JSON Metadata Analysis

Extract insights from JSON transcript metadata including speaker analytics, confidence scores, and timing patterns.

Analyze this JSON transcript and provide detailed insights about the conversation.

Analysis Requirements:

## Speaker Analytics
- Total number of speakers
- Speaking time per speaker (duration and percentage)
- Turn-taking patterns and interruptions
- Speech pace (words per minute per speaker)

## Quality Metrics
- Average confidence score by speaker
- Low-confidence sections (score < 0.85) requiring review
- Word count and vocabulary complexity
- Speech clarity indicators

## Content Insights
- Main topics discussed (extracted from high-confidence segments)
- Key moments (based on speaker transitions and timing)
- Engagement patterns (question-response dynamics)
- Summary of discussion flow

## Technical Details
- Total duration
- Language detected
- Words per segment statistics
- Timestamp accuracy verification

Please format the analysis as a comprehensive report with:
1. Executive summary
2. Detailed speaker breakdown
3. Quality assessment
4. Content highlights
5. Actionable recommendations

JSON Transcript:
[PASTE YOUR JSON TRANSCRIPT HERE]

Prompt #4: Accessible TXT Documentation

Transform any transcript format into WCAG-compliant accessible documentation with proper structure and formatting.

Create a WCAG-compliant accessible transcript document from this source material.

Accessibility Requirements:
1. Clear speaker identification
2. Logical paragraph structure
3. Proper headings and sections
4. Description of non-speech audio [when present]
5. UTF-8 encoding
6. Screen reader optimization
7. Remove filler words for clarity

Document Structure:
- Title: [Meeting/Interview/Lecture Title]
- Date: [Date if known]
- Participants: [List of speakers]
- Main Content: [Formatted transcript]
- Summary: [Key points and action items]

Formatting Guidelines:
- Use "Speaker Name:" for speaker labels
- Add blank lines between speaker turns
- Group related exchanges into paragraphs
- Include timestamps for key moments [optional]
- Add [DESCRIPTION] tags for non-speech audio

Content Optimization:
- Remove filler words (um, uh, like) for readability
- Fix obvious transcription errors
- Maintain natural speech patterns
- Preserve important pauses [indicated]

Source Transcript:
[PASTE YOUR TRANSCRIPT IN ANY FORMAT]

Please create a clean, accessible TXT document following W3C guidelines and optimized for screen readers.

How to Choose the Right Format

By Use Case

Quick Decision Flowchart

Converting Between Formats

SRT ↔ VTT Conversion

The simplest conversion since formats are nearly identical:

TXT → SRT/VTT Conversion

Requires timing information—best done with AI assistance:

• Use audio-to-text service to generate timed subtitles
• AI prompts can estimate timing based on word count and speech pace
• Manual timing using video editing software (Premiere, DaVinci Resolve)
• Use the "Format Conversion & Optimization" prompt above with timing estimates

JSON → Other Formats

JSON contains all necessary data for conversion to any format:

• JSON → TXT: Extract text from segments, format with speaker labels
• JSON → SRT/VTT: Use segment timestamps and text, add sequential numbering
• JSON → Analyzed Report: Use "JSON Metadata Analysis" prompt above
• Python/JavaScript scripts can automate batch conversions

Additional Resources

Related BrassTranscripts Guides

External References