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.

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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 1

Create table for audio files and metadata management

import pixeltable as pxt
from pixeltable.functions import openai
# Create table for audio processing
audio_files = pxt.create_table('audio_transcription', {
'audio_file': pxt.Audio,
'filename': pxt.String,
'duration_seconds': pxt.Float,
'source': pxt.String
})
# Add Whisper transcription
audio_files.add_computed_column(
transcript=openai.transcribe(
audio=audio_files.audio_file,
model='whisper-1'
)
)

💡 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

•Podcast transcription at scale
•Meeting transcription workflows
•Customer service call analysis

Prerequisites

•Basic Python programming knowledge
•OpenAI API account and key

Technical Needs

•Python 3.8+
•OpenAI API key with Whisper access

Performance

Processing Speed
Concurrent processing with optimization
100+ files/hour

Ready to Get Started?

Install Pixeltable and build your own build audio transcription pipeline with openai whisper api in minutes.