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Your First Transcription

This guide walks you through creating your first transcription in OpenTranscribe, from upload to analysis.

Step 1: Prepare Your Media File​

OpenTranscribe supports a wide range of formats:

Audio Formats​

  • MP3 - Most common audio format
  • WAV - Uncompressed audio (best quality)
  • FLAC - Lossless compression
  • M4A - Apple audio format
  • OGG - Open-source audio format

Video Formats​

  • MP4 - Most common video format
  • MOV - Apple video format
  • AVI - Windows video format
  • MKV - Matroska video container
  • WEBM - Web-optimized video

File Size Limits​

  • Maximum file size: 4GB
  • Recommended: Under 2GB for faster processing
  • Long videos (3+ hours) supported
Best Results

For best transcription quality:

  • Use clear audio with minimal background noise
  • Single speaker per channel if possible
  • Good microphone quality (not phone speaker recordings)
  • Volume normalized - not too quiet or clipping

Step 2: Upload Your File​

Via Web Interface​

  1. Click "Upload Files" button in the top navigation bar
  2. Drag and drop your file onto the upload zone, or click to browse
  3. Select your file from your computer
  4. Watch the upload progress in the floating upload manager

Via URL (YouTube)​

OpenTranscribe can download and process YouTube videos:

  1. Click "Upload from URL" in the navbar
  2. Paste the YouTube URL (supports playlists too!)
  3. Click "Download and Process"
  4. The video will be downloaded and queued for transcription

Via Recording​

Record audio directly in your browser:

  1. Click the microphone icon in the navbar
  2. Select your microphone device
  3. Click "Start Recording"
  4. Monitor audio levels to ensure good volume
  5. Pause/Resume as needed
  6. Click "Stop" when finished
  7. The recording is automatically uploaded and processed

Step 3: Monitor Processing​

OpenTranscribe processes files through 13 stages:

Processing Stages​

  1. Queued - File is waiting in the processing queue
  2. Starting - Worker is beginning processing
  3. Extracting Audio - Converting video to audio if needed
  4. Loading Models - Loading WhisperX and PyAnnote models
  5. Transcribing - AI transcription in progress
  6. Aligning - Word-level timestamp alignment
  7. Diarizing - Detecting and separating speakers
  8. Creating Profiles - Generating voice fingerprints
  9. Matching Speakers - Cross-video speaker matching
  10. Generating Waveform - Creating audio visualization
  11. Indexing - Adding to search index
  12. Saving - Storing results to database
  13. Complete - Ready to view!

Where to Watch Progress​

Real-Time Updates:

  • Upload Manager (bottom-right floating panel)
  • Notifications Panel (bell icon in navbar)
  • File Library (processing badge on file cards)
  • Flower Dashboard (http://localhost:5555/flower)

Processing Time Estimates:

DurationGPU (RTX 3080)CPU (8-core)
5 min~30 seconds~5 minutes
30 min~3 minutes~30 minutes
1 hour~5 minutes~60 minutes
3 hours~15 minutes~3 hours
Processing Speed

With GPU acceleration and the large-v2 model, OpenTranscribe processes at ~70x realtime speed. A 1-hour file transcribes in about 5 minutes!

Step 4: View Your Transcript​

Once processing completes, click on the file to view the transcript.

Transcript Features​

Interactive Transcript:

  • Click any word to jump to that moment in the audio
  • Speaker labels automatically assigned (SPEAKER_00, SPEAKER_01, etc.)
  • Timestamps show when each speaker talks
  • Word-level highlighting follows audio playback

Waveform Player:

  • Click anywhere on the waveform to seek to that time
  • Visual representation of audio amplitude
  • Zoom controls for detailed view
  • Speaker segments color-coded

Playback Controls:

  • Play/Pause audio playback
  • Speed control (0.5x to 2x speed)
  • Volume control
  • Keyboard shortcuts (Space to play/pause, arrow keys to seek)

Step 5: Edit Speaker Names​

OpenTranscribe automatically detects speakers but labels them generically. You can edit names:

Edit Speaker Names​

  1. Click the "Edit Speakers" button below the transcript
  2. Click on a speaker label (e.g., "SPEAKER_00")
  3. Type the actual name (e.g., "John Smith")
  4. Press Enter or click outside to save
  5. The name updates throughout the transcript instantly

Create Speaker Profiles​

When you name a speaker, OpenTranscribe can:

  1. Create a global profile for that speaker
  2. Generate a voice fingerprint using their audio
  3. Suggest that speaker in future transcriptions
  4. Track their appearances across multiple files

See Speaker Management for advanced features.

Step 6: Generate a Summary (Optional)​

If you've configured an LLM provider, you can generate AI summaries:

Configure LLM (One-Time)​

  1. Go to User Settings (gear icon)
  2. Click "LLM Configuration" tab
  3. Select a provider (OpenAI, Claude, vLLM, Ollama)
  4. Enter your API key or endpoint
  5. Test the connection
  6. Click "Save"

Generate Summary​

  1. Open a transcription
  2. Click the "Summarize" button at the top
  3. Choose a summary prompt:
    • BLUF (Bottom Line Up Front) - Executive summary format
    • Meeting Notes - Action items and decisions
    • Custom prompts - Create your own!
  4. Watch the progress (takes 10-60 seconds depending on length)
  5. View the summary in the Summary tab

Summary Features​

The default BLUF summary includes:

  • Overview - High-level summary in 2-3 sentences
  • Key Points - Bullet points of main topics discussed
  • Action Items - Tasks and assignments with priorities
  • Decisions Made - Key decisions and outcomes
  • Follow-up Items - Things to revisit or research
  • Speaker Analysis - Who spoke most, key contributions

Step 7: Explore Advanced Features​

Search Your Transcript​

Keyword Search:

Search for: "project deadline"

Finds exact matches of that phrase

Semantic Search:

Search for: "budget concerns"

Finds related concepts like "financial constraints", "cost overruns", etc.

Add Comments​

  1. Click anywhere in the transcript
  2. Type your comment in the comment field
  3. Press Enter to save
  4. Comments are timestamped and linked to that moment

Export Options​

Export Formats:

  • TXT - Plain text transcript
  • JSON - Structured data with timestamps
  • SRT - Subtitle file for video
  • VTT - WebVTT subtitle format
  • DOCX - Microsoft Word document (with speaker labels)

Export Methods:

  1. Click "Export" button
  2. Choose format
  3. Click "Download"

Organize with Collections​

Group related files:

  1. Click the "Collections" button
  2. Create a new collection (e.g., "Q1 2024 Meetings")
  3. Add files by clicking the collection tag
  4. Filter your library by collection

Performance Tips​

For Faster Processing​

  1. Enable GPU acceleration if available
  2. Use smaller models for quick drafts (base or medium)
  3. Process overnight for large batches
  4. Multi-GPU scaling for high-throughput needs

For Better Accuracy​

  1. Use large-v2 model for best transcription quality
  2. Good audio quality - clear, well-recorded audio
  3. Edit speaker names to improve future speaker matching
  4. Verify and correct any transcription errors

Troubleshooting​

Upload Fails​

  • Check file size (must be under 4GB)
  • Check format (must be supported audio/video)
  • Check disk space (need enough storage)
  • Try again - network errors can be temporary

Processing Stuck​

  • Check logs: ./opentranscribe.sh logs celery-worker
  • Check Flower: http://localhost:5555/flower
  • Restart workers: ./opentranscribe.sh restart
  • Check GPU memory: nvidia-smi

Poor Transcription Quality​

  • Improve audio - re-record with better microphone
  • Reduce background noise - use noise cancellation
  • Larger model - switch to large-v2 for better accuracy
  • Language setting - ensure correct language selected

Speakers Not Detected​

  • Check HuggingFace token - required for diarization
  • Clear audio - speakers need distinct voices
  • Adjust MIN/MAX speakers in configuration
  • Manual editing - edit speaker labels manually if needed

Next Steps​

Now that you've created your first transcription, explore:

Need Help?​

Happy transcribing! 🎙️