#Python

Building the Video Translator , the assumption going in was that OpenAI Whisper’s larger model would transcribe more accurately. In practice, on clips with loud background music, the turbo model did noticeably better than large. It seems to tolerate messy audio better, rather than just being a faster, lower-quality option. Separating vocals from background music first (with demucs) didn’t help as much as expected either; Whisper handled the mixed track better on its own. Worth trying turbo first rather than assuming bigger is more accurate.

A CLI that generates word-timed subtitles for any video and translates them into any language, using Whisper for transcription and a choice of GPT or Google Translate.

A local pipeline that dubs a video into another language in each speaker's own cloned voice: transcribe, translate, clone, fit the result to the original timing, and remux, all from one command.

Turned bugs found while hand-filing a real ITR-3 return into a reusable validator module and test suite for the project's e-filing JSON generator.

Added endpoints that were missing from WazirX's official Python connector (klines, trade history, coins, withdrawals, sub-account transfers) and fixed a mismapped system_status endpoint.

Relaxed the minimum-segment-length check in se_extractor.py so sub-1.5s lines can be cloned, and made the pipeline run on CPU (Apple M1).

Blockchain integrations and fault-tolerant wallet-sync workflows for a crypto portfolio and tax platform.

Designed the database architecture for an in-house Global Distribution System and a serverless bus-ticket booking engine.