intermediate1-2 hours
Build Audio Transcription Pipeline with OpenAI Whisper API
Learn how to transcribe audio files and parallelize API calls efficiently with Pixeltable and OpenAI Whisper.
Challenge
Transcribing large volumes of audio files requires complex orchestration of file handling, API rate limiting, error retry logic, and parallel processing.
Solution
Pixeltable provides automated audio transcription workflows with built-in Whisper API integration and intelligent parallelization.
Implementation Steps
Step 1 of 1Create table for audio files and metadata management
import pixeltable as pxtfrom pixeltable.functions import openai# Create table for audio processingaudio_files = pxt.create_table('audio_transcription', {'audio_file': pxt.Audio,'filename': pxt.String,'duration_seconds': pxt.Float,'source': pxt.String})# Add Whisper transcriptionaudio_files.add_computed_column(transcript=openai.transcribe(audio=audio_files.audio_file,model='whisper-1'))
đĄ Sets up audio file management with automatic Whisper transcription.
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Key Benefits
90% reduction in transcription pipeline development time
Built-in rate limiting eliminates API management complexity
Seamless scaling from single files to thousands
Real Applications
â˘Podcast transcription at scale
â˘Meeting transcription workflows
â˘Customer service call analysis
Prerequisites
â˘Basic Python programming knowledge
â˘OpenAI API account and key
Technical Needs
â˘Python 3.8+
â˘OpenAI API key with Whisper access
Performance
Processing Speed
Concurrent processing with optimization
100+ files/hour
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Ready to Get Started?
Install Pixeltable and build your own build audio transcription pipeline with openai whisper api in minutes.