AI/ML Case Study

Real-Time Audio Transcription

This transcription tool is designed for privacy and speed. By focusing on offline processing, it ensures that sensitive audio never leaves the user's machine, providing high-accuracy text conversion for long-form recordings.

Technology Stack

PythonSpeechRecognitionAudio Processing

System Architecture

Input

Audio stream capture using PyAudio.

Engine

SpeechRecognition library with custom model tuning.

Output

Formatted text export for documentation.

The Challenges

Optimizing for different accents and background noise levels.

Handling long audio files without crashing due to memory limits.

Maintaining accuracy in specialized domains (e.g., medical or legal).

The Solutions

Implemented a noise-gating filter to clean audio before processing.

Used stream-processing to transcribe audio in manageable chunks rather than loading entire files.

Added a custom dictionary feature to improve accuracy for technical terms.

Key Results & Metrics

01

Real-time processing

02

Custom recognition models

03

Offline capability