Using Amazon Transcribe to Generate Accessible Captions for Video Content

Introduction

Captions matter for more than compliance. They support deaf and hard-of-hearing users, non-native speakers, people watching in sound-restricted environments, and anyone who processes information better through text. In practice, captions improve clarity and reach for everyone.

Captioning a single video is straightforward with today’s tools. The challenge appears when captioning becomes part of a platform or an ongoing content pipeline. At that point, the question shifts from “How do I caption this video?” to “How do I build captioning into my workflow?”

Consider a small engineering, product, or DevRel team that regularly produces video content. At first, captions are handled manually using simple tools. This works until volume and expectations grow.

Over time, captioning shifts from an occasional task to a recurring workflow. Videos are published regularly, more contributors are involved, and accessibility expectations increase. At this point, captioning stops being a one-off task and becomes a workflow problem.

This is where Amazon Transcribe becomes relevant.

What Amazon Transcribe Is

Amazon Transcribe is AWS's speech-to-text service. It converts spoken audio into time-aligned text. For video workflows, it outputs subtitle files in formats like WebVTT (.vtt) and SubRip (.srt), which are widely supported by video players and streaming platforms.

The service abstracts away the complexity of speech recognition. There's no model training or ML pipeline to manage. You provide the media file, and the service returns captions. This matters when you need captioning to be a repeatable, automated part of your system rather than a manual task.

Amazon Transcribe is a managed service, which means AWS handles the infrastructure, scaling, and maintenance. You focus on integrating it into your workflow and ensuring the output meets your accessibility standards.

Prerequisites

If you're planning to work with Amazon Transcribe, you'll need:

• AWS account access - You'll need an account with permissions for Amazon Transcribe and Amazon S3
• AWS familiarity - Basic understanding of AWS services, particularly S3 for storing media files
• Media files - Video or audio files in supported formats (MP3, MP4, WAV, FLAC, and others)
• Development tools - AWS CLI or SDK for automation (the examples in this guide use Python and boto3)

You can also use Amazon Transcribe through the AWS Console without writing code, which is useful for exploring the service and understanding its capabilities before building automation.

When You'd Use Amazon Transcribe

Amazon Transcribe isn't trying to replace simple, one-click captioning tools. Those work perfectly well for occasional use. Instead, it solves a different problem: captioning at scale within an existing workflow.

Specific Scenarios

You might consider Amazon Transcribe if you are:

• Building a platform that hosts video content and needs built-in captioning capabilities
• Producing content regularly as part of training, documentation, or educational programs
• Working with accessibility requirements that are part of your product or organizational standards
• Automating media workflows using AWS services and need captioning to fit into that automation

Amazon Transcribe becomes valuable when you need automation, integration, consistency, and scalability. Whether it's five videos or five hundred, the workflow stays the same. It's less about convenience and more about control and integration.

If you're captioning a single video occasionally, simpler tools are probably faster. If captioning is part of your product or process, Amazon Transcribe fits naturally into your infrastructure.

How Amazon Transcribe Works

Architecture Overview

Thinking in workflows rather than individual tools helps clarify how this fits together. At a conceptual level, a captioning pipeline on AWS looks like this:

The flow is straightforward:

1. Amazon S3 stores uploaded files
2. Amazon Transcribe processes the audio and generates caption files
3. Captions are reviewed for accuracy
4. Captions are attached to the video player or publishing platform

Optional services like AWS Lambda can automate orchestration, and AWS Elemental MediaConvert can be used if captions need to be embedded directly into video outputs.

Basic Implementation

Here's an example on what starting a transcription job looks like using the AWS SDK for Python (boto3):

import boto3

transcribe = boto3.client('transcribe', region_name='us-east-1')

job_name = "video-caption-job-001"
job_uri = "s3://your-bucket-name/your-file.mp4"

transcribe.start_transcription_job(
    TranscriptionJobName=job_name,
    Media={'MediaFileUri': job_uri},
    MediaFormat='mp4',
    LanguageCode='en-US',
    OutputBucketName='your-output-bucket',
    Subtitles={
        'Formats': ['vtt', 'srt']
    }
)

The Subtitles parameter tells Transcribe to format the output specifically for video players. Without it, you'd get a JSON transcript that you'd need to convert to caption format yourself.

Transcription jobs run asynchronously. You start the job, then check its status until it completes:

import time

while True:
    status = transcribe.get_transcription_job(
        TranscriptionJobName=job_name
    )
    job_status = status['TranscriptionJob']['TranscriptionJobStatus']

    if job_status in ['COMPLETED', 'FAILED']:
        break

    print(f"Status: {job_status}. Checking again...")
    time.sleep(15)

if job_status == 'COMPLETED':
    transcript_uri = status['TranscriptionJob']['Transcript']['TranscriptFileUri']
    print(f"Caption files available in S3 output bucket")

When the job completes, Amazon Transcribe writes the caption files to your specified S3 bucket. You get both the caption files (VTT and SRT) and a JSON file containing the full transcript with detailed timing information.

Understanding the Output

A VTT caption file looks like this:

WEBVTT

00:00:00.000 --> 00:00:03.450
Welcome to this guide on Amazon Transcribe.

00:00:03.450 --> 00:00:07.890
Today we'll explore how to generate captions for your video content.

Each caption block has a timestamp range and the corresponding text. Video players that support VTT will display this text at the right moments during playback.

The JSON output includes confidence scores for each word, which helps you identify sections that might need manual review:

{
  "results": {
    "items": [{
      "start_time": "0.000",
      "end_time": "0.450",
      "alternatives": [{
        "confidence": "0.9987",
        "content": "Welcome"
      }]
    }]
  }
}

Additional Features

Amazon Transcribe supports several features that improve accuracy for specific use cases:

• Custom vocabularies help with domain-specific terms, product names, and technical terms:

transcribe.start_transcription_job(
    TranscriptionJobName=job_name,
    Media={'MediaFileUri': job_uri},
    MediaFormat='mp4',
    LanguageCode='en-US',
    Settings={
        'VocabularyName': 'tech-terms-vocabulary'
    },
    Subtitles={
        'Formats': ['vtt', 'srt']
    }
)

• Speaker identification distinguishes between different speakers in interviews or panel discussions:

Settings={
    'ShowSpeakerLabels': True,
    'MaxSpeakerLabels': 2
}

• Multiple languages are supported, allowing you to caption content in dozens of languages using the same workflow.

Important Considerations

Human Review Is Required

Like all automated captioning systems, Amazon Transcribe has limitations. Common issues could include missing punctuation, incorrect terminology, poor speaker identification, misheard words, etc.

While automation is valuable for scaling, it doesn't eliminate the need for quality control. Someone needs to review the generated captions for accuracy and readability before publishing. The captions represent your content, and errors can confuse viewers or change meaning.

Audio Quality Matters

Caption quality is closely tied to audio quality. Clear audio and minimal background noise tend to produce more accurate results, which is worth keeping in mind when recording video intended for transcription.

Cost and Scale Considerations

Amazon Transcribe charges per second of audio transcribed. For occasional use, costs are minimal. For large-scale operations processing hours of content daily, it's important to monitor usage and costs carefully.

Integration with Your Platform

Most video platforms support VTT and SRT caption files. For custom video players using HTML5, you add captions with a track element:

<video controls>
  <source src="your-file.mp4" type="video/mp4">
  <track label="English" kind="captions" srclang="en" 
         src="captions.vtt" default>
</video>

For YouTube, you can upload caption files directly. For streaming platforms like AWS Elemental MediaConvert or AWS Elemental MediaPackage, you can include caption files as part of your video processing workflow.

Consider where caption files will be stored in your storage system, how they'll be linked to their corresponding videos, and how your application will retrieve and display them.

Getting Started

Start small. Try transcribing a few videos using the AWS Console or a simple script. See how the accuracy matches your specific content. Identify common error patterns. Build confidence in the process before automating and scaling up.

Next Steps

Ready to explore further? Here are resources to help you move forward:

Official Documentation:

• Amazon Transcribe Developer Guide - Comprehensive documentation covering all features and API references
• Getting Started with Amazon Transcribe - Step-by-step tutorials for your first transcription jobs
• Subtitles Documentation - Detailed guide on generating subtitle files

Code Examples:

• AWS Samples on GitHub - Search for "transcribe" to find complete code examples and reference architectures

Related AWS Services:

• AWS Lambda Documentation - For automating your transcription workflow
• Amazon S3 Documentation - For managing your media files and outputs
• AWS Elemental MediaConvert - For embedding captions directly into video files

Pricing and Costs:

• Amazon Transcribe Pricing - Current pricing information to estimate your costs

The AWS documentation includes complete tutorials, API references, and best practices that go beyond what this guide covers. Start with the Getting Started guide, experiment with a few videos, and build from there.

Conclusion

Amazon Transcribe provides a practical way for teams to generate captions as part of their workflow, not as an afterthought. When captions are treated as part of the content lifecycle and reviewed with care, Amazon Transcribe helps shift the work from manual transcription to quality review, making consistent captioning achievable at any scale.

If captioning is becoming a recurring need in your work, it's worth exploring how this kind of automation can support your accessibility goals while maintaining the quality your audience deserves.