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WhisperGPTTranscriber is a Python-based tool that efficiently converts audio files to text using OpenAI's Whisper model and enhances the transcription accuracy with GPT-4 post-processing. It supports large audio files, offers conditional GPT-4 enhancement, and allows customizable output locations.

License: MIT License

Python 100.00%

whispergpttranscriber's Introduction

Whisper GPT Transcriber

WhisperGPTTranscriber is a Python-based tool that efficiently converts audio files to text using OpenAI's Whisper model and enhances the transcription accuracy with GPT-4 post-processing. It supports large audio files, offers conditional GPT-4 enhancement, and allows customizable output locations.

Features

  • Audio Transcription: Utilizes the Whisper model for efficient audio-to-text conversion.
  • GPT-4 Post-Processing: Offers optional post-processing with GPT-4 for enhanced transcription accuracy or a direct summary of key facts.
  • Large File Support: Capable of processing audio files larger than the standard size limits of 25 MBs.
  • Customizable Output: Allows users to specify output file locations.

Installation

  • Install openai and pydub (mp3 handling): pip install openai pydub
  • Add the openai key to the .py file
  • Change the GPT Model if you want. I am using "gpt-4-1106-preview"

Usage

The script takes a while (minutes). Especially when the mp3 is long. As a progress Statement, it will print the transcripts of the "chopped" mp3 into the terminal.

Example: Download any Youtube Video as .mp3. There are a billion converters out there. Like https://notube.cc/de/youtube-app-v103 Then...

This will run the script and save the transcript in the same folder as the .py file:

python3 whisper.py path/to/audio.mp3

This will run the script, post-process it and then save the transcript in a designated folder:

python3 whisper.py path/to/audio.mp3 --gpt_post_process --output_file path/to/output.txt

GPT Post-Processing

The GPT post-processing feature in WhisperGPTTranscriber uses OpenAI's GPT-4 model to refine and enhance the transcriptions. After the initial transcription with Whisper, the text is passed through GPT-4, which corrects grammatical errors, clarifies ambiguous language, and ensures proper spelling, especially of specific terms or names.

Examples of GPT Post-Processing Usage

  1. Technical Meetings: Improves the accuracy of technical jargon in transcriptions from IT or scientific discussions.
  2. Educational Lectures: Enhances clarity in transcribed lectures, ensuring correct terminology and coherence.
  3. Business Conferences: Polishes transcripts of business conferences, focusing on correct names of companies, products, and industry-specific terms.
  4. Medical Dictations: Ensures medical terms and drug names are accurately transcribed from doctors’ audio notes.
  5. Legal Proceedings: Refines transcriptions of legal proceedings, where accuracy of names, laws, and legal terminology is crucial.

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