AUDIO TO TEXT

Audio to Text is a prototype transcription utility that converts audio input into text using an AI transcription API. It is for anyone who needs quick transcription for notes, clips, study material, or automation workflows.

Audio content is useful, but it is painful to search, skim, or reuse without text. Manual transcription wastes time, and many quick tools hide the actual backend flow. I wanted to build the pipeline myself so the moving parts were clear.

The backend flow is built with Python and Flask. It accepts an audio upload, validates the request, prepares the payload for the transcription provider, receives the model output, then normalizes the response before returning it to the frontend.

The system is designed as a direct utility first. Once the transcription is reliable, it can be extended into summaries, searchable transcripts, or automated note generation. Pehle text sahi nikalo, then fancy features.

The tool keeps the workflow focused on reliable transcription.

Audio workflows can fail in many small ways: unsupported formats, large files, slow responses, provider errors, and messy output formatting. The main challenge was keeping the pipeline predictable instead of assuming every upload behaves nicely.

I kept the backend as the control point for validation and provider interaction. That gives room to add file constraints, retries, and better response formatting later without pushing fragile logic into the client.

The prototype establishes a working speech-to-text flow with a clear backend boundary. It is ready to grow into a more complete transcription and study utility once storage, history, and post-processing are added.