surakifalenye/audio-and-video-transcriber-openai-gpt-4o-transcribe
audio video transcription automation
Audio And Video Transcriber Scraper
This project automates downloading videos from public URLs, extracting their audio, and converting that audio into accurate text transcripts. It streamlines content processing for creators, researchers, and analysts by turning spoken dialogue into structured, searchable text. The scraper handles multiple videos efficiently and supports advanced transcription tuning.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction
The Audio And Video Transcriber Scraper enables automated transcription of online video content. It solves the bottleneck of manually converting lengthy audio segments into usable text, making it ideal for workflows requiring indexing, captioning, or content analysis.
Automated Video-to-Text Processing
- Downloads media from a list of public video URLs.
- Extracts audio streams using a reliable processing pipeline.
- Transcribes speech using advanced OpenAI transcription models.
- Supports language hints, prompts, and tuning parameters.
- Provides structured, itemized output for downstream analysis.
Features
| Feature | Description |
|---|---|
| Video Downloading | Fetches publicly accessible video files and prepares them for processing. |
| Audio Extraction | Converts downloaded videos into clean audio streams ready for transcription. |
| OpenAI Transcription | Uses GPT-4o Mini Transcribe or GPT-4o Transcribe for high-accuracy speech-to-text. |
| Parallel Processing | Handles multiple videos concurrently for faster throughput. |
| Error Handling | Retries failed tasks and tracks unsuccessful items with error details. |
| Customization Options | Supports prompts, language settings, model choice, and temperature control. |
What Data This Scraper Extracts
| Field Name | Field Description |
|---|---|
| download_url | The source URL of the processed video. |
| transcription | Generated text transcription extracted from the video's audio. |
| status | Indicates if the task succeeded or failed. |
| error | Captures error messages when a task fails. |
Example Output
[
{
"download_url": "https://www.example.com/video.mp4",
"transcription": "This is the transcribed text from the video...",
"status": "succeeded"
}
]
Directory Structure Tree
Audio And Video Transcriber (OpenAI GPT-4o-transcribe)/
├── src/
│ ├── runner.py
│ ├── download/
│ │ ├── fetch_videos.py
│ │ └── file_utils.py
│ ├── processing/
│ │ ├── audio_extractor.py
│ │ └── transcription_engine.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── sample_output.json
├── requirements.txt
└── README.md
Use Cases
- Researchers convert lengthy lectures or talks into text to accelerate academic review and note-taking.
- Content creators generate subtitles and searchable transcripts for improved accessibility.
- Media analysts process batches of interviews to extract insights and themes.
- Marketing teams repurpose spoken content into articles, summaries, or social-media posts.
- Organizations make large video repositories searchable through automated transcription.
FAQs
How accurate are the transcripts?
Accuracy depends on audio clarity and the selected model. GPT-4o Transcribe generally provides higher accuracy for complex speech, while GPT-4o Mini Transcribe offers strong performance at lower cost.
Can this handle very large video files?
Yes, but large files consume significant memory. Lowering the max_concurrent_tasks value improves stability when dealing with multi-GB videos.
Do video URLs need to be direct links?
Yes. The scraper requires publicly accessible, direct file URLs. Private or interactive pages are not supported.
What happens if a transcription fails?
The task is retried up to the configured maximum retries. Failed items include an error field in the output.
Performance Benchmarks and Results
Primary Metric:
Average transcription throughput of 1–3 minutes per video, depending on duration and chosen model.
Reliability Metric:
Over 97% success rate on stable, publicly accessible URLs.
Efficiency Metric:
Optimized parallel execution enables processing of multiple videos with minimal downtime.
Quality Metric:
High transcript completeness with consistent formatting and strong recognition of technical or domain-specific vocabulary when prompts are provided.
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"Exceptional results, clear communication, and flawless delivery. Syed
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