- Built a Python-based transcription system using OpenAI’s Whisper model for privacy and cost efficiency.
- Detailed setup requirements including FFmpeg installation and Python environment setup.
- Comparison of different Whisper models based on size, RAM required, speed, and accuracy.
- Provided a clean, production-ready implementation of Whisper for audio transcription.
- Explained batch processing for multiple audio files and creating subtitle files (SRT format).
- Introduced an alternative method using the SpeechRecognition library for different recognition engines.
- Listed common issues and solutions such as FFmpeg not found, out of memory errors, and poor accuracy on noisy audio.
- Performance benchmarks for different Whisper models and hardware impacts.
- Created a simple command-line script for easy usage of the transcription tool.
- Highlighted key advantages of local audio transcription including zero ongoing costs, complete privacy, and high accuracy.