Audio and Video File Transcription API: The Complete Guide

Not all content lives on social platforms. Podcasts, meeting recordings, lecture captures, and internal video libraries all require transcription—and APIs provide the scalable solution for processing these files programmatically.

This guide covers how to transcribe audio and video files using AI-powered APIs, from single files to enterprise-scale batch processing.

Supported File Formats

Audio Formats

• MP3: Most common format, excellent compression, widely supported
• WAV: Uncompressed audio, highest quality, larger file sizes
• M4A: Apple's format, common from iPhone recordings
• OGG: Open format, used by many applications
• FLAC: Lossless compression, used for archival
• WebM: Web-optimized format for browsers

Video Formats

• MP4: Universal standard, excellent compatibility
• MOV: Apple QuickTime format
• AVI: Legacy Windows format
• MKV: Flexible container supporting multiple tracks
• WebM: Web-optimized video
• FLV: Flash video, still used in some archives

Transcribing Audio Files

For audio files accessible via URL:

curl -X POST https://api.transcripthq.io/v1/transcribe-audio \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_API_KEY" \
  -d '{
    "audio_url": "https://example.com/podcast-episode-123.mp3",
    "noise_reduction": true,
    "word_timestamps": true
  }'

Response with Word-Level Timestamps

{
  "status": "completed",
  "transcript": "Welcome to the podcast. Today we're discussing...",
  "segments": [
    { "text": "Welcome to the podcast.", "start": 0.0, "end": 1.8 }
  ],
  "words": [
    { "word": "Welcome", "start": 0.0, "end": 0.4 },
    { "word": "to", "start": 0.4, "end": 0.5 },
    { "word": "the", "start": 0.5, "end": 0.6 },
    { "word": "podcast", "start": 0.6, "end": 1.2 }
  ],
  "duration_seconds": 3600,
  "credits_charged": 60
}

Transcribing Video Files

Video transcription extracts the audio track and processes it through the same Whisper pipeline:

curl -X POST https://api.transcripthq.io/v1/transcribe-video \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_API_KEY" \
  -d '{
    "video_url": "https://s3.amazonaws.com/bucket/training-video.mp4",
    "noise_reduction": true
  }'

File Size Limits

• Audio files: Up to 25MB per file
• Video files: Up to 1GB per file

For larger files, split them into segments or contact the API provider for enterprise limits.

Preprocessing Options

Noise Reduction

Removes background noise before transcription. Particularly valuable for:

• Phone call recordings with ambient noise
• Outdoor interviews
• Conference room recordings with HVAC noise
• Car recordings

Language Specification

While Whisper auto-detects languages, specifying the expected language improves accuracy:

{
  "audio_url": "https://example.com/spanish-interview.mp3",
  "source_language": "es"
}

Translation

Transcribe and translate in a single step:

{
  "audio_url": "https://example.com/german-lecture.mp3",
  "source_language": "de",
  "target_language": "en"
}

Enterprise Use Cases

Podcast Production

Podcast teams transcribe episodes for show notes, blog posts, social media clips, and SEO-optimized episode pages. Transcripts also enable keyword-based episode search.

Meeting Documentation

Organizations transcribe Zoom recordings, Teams meetings, and phone calls for searchable archives, compliance requirements, and AI-powered meeting summaries.

E-Learning Platforms

Educational content providers transcribe video courses for accessibility compliance, in-video search, and study material generation.

Media Archives

Libraries, news organizations, and media companies digitize and transcribe legacy audio/video archives to make historical content searchable.

Call Center Analytics

Customer service operations transcribe calls for quality assurance, compliance monitoring, training, and sentiment analysis.

Batch Processing

For large-scale transcription, use batch processing with webhooks:

// Submit batch job
POST /v1/transcribe-batch
{
  "files": [
    { "url": "https://s3.example.com/file1.mp3" },
    { "url": "https://s3.example.com/file2.mp3" },
    { "url": "https://s3.example.com/file3.mp3" }
  ],
  "webhook_url": "https://your-app.com/webhook/transcription-complete",
  "noise_reduction": true
}

Webhook Notification

// Webhook payload when complete
{
  "batch_id": "batch_abc123",
  "status": "completed",
  "results": [
    { "file": "file1.mp3", "transcript_url": "..." },
    { "file": "file2.mp3", "transcript_url": "..." },
    { "file": "file3.mp3", "transcript_url": "..." }
  ]
}

Best Practices

• Use presigned URLs: For private S3 files, generate presigned URLs with sufficient expiration time
• Enable noise reduction: Default to on unless you know the audio is clean
• Request word timestamps: If you might need them later, request them now—they're included at no extra cost
• Specify language when known: Improves accuracy and speed
• Monitor credit usage: Long files consume more credits; budget accordingly
• Use webhooks for production: Don't poll in production systems

Conclusion

Audio and video file transcription powers critical workflows across industries—from media production to enterprise documentation. Modern AI transcription APIs handle diverse formats, languages, and audio conditions while providing the reliability and scale that production systems require.

Whether you're transcribing a single podcast episode or processing millions of call center recordings, the API approach provides consistent, accurate results at any scale.