Speech-to-Text System - Annas Tri Widagdo

[COVER]Before and after subtitle output from the speech-to-text workflow

[01]

Overview

This Google Colab workflow converts audio or video into reusable transcript and subtitle outputs.

It normalizes source media, performs inference with the pretrained facebook/wav2vec2-base-960h model, and exports TXT, CSV, JSON, SRT, and burned-in subtitles.

[02]

My Contribution

Built the media-ingestion flow for audio and video sources.
Implemented mono 16 kHz conversion and chunk-based processing before inference.
Structured transcript output for plain text, tabular, JSON, and subtitle formats.
Added SRT generation and an FFmpeg step for burned-in subtitle output.

[03]

Key Technical Notes

Audio is converted to mono at a 16 kHz sample rate before model inference.
Long inputs are divided into chunks so the workflow can process them sequentially.
The implementation uses the pretrained Wav2Vec2 base 960-hour model as documented.
The available project record does not include a benchmark evaluation.

[04]

Visual Evidence

Speech-to-text subtitle workflow evidence — FIG.01Automatic speech recognition output showing generated video subtitles.

[05]

Tech Stack

Python
Wav2Vec2
Hugging Face Transformers
Librosa
FFmpeg
Pandas

[06]

Status & Link

Completed workflow

Source repository or production deployment is private.