TranscriptAI
AI/ML SaaS ApplicationA full-featured AI-powered transcription platform built in partnership with Pima Web Design. TranscriptAI converts audio and video files into accurate, speaker-labeled transcripts using OpenAI Whisper and advanced speaker diarization technology.
Technologies Used
Application Screenshots
Drag & drop upload with enterprise security
AI model selection & speaker detection
Audio waveform with speaker-labeled transcript
Usage stats & account management
Project Goals
Build an enterprise-grade transcription solution that goes beyond basic speech-to-text. The platform needed to handle diverse audio formats, automatically identify different speakers, support GPU acceleration for fast processing, and export professional documents with timestamps and speaker attribution.
My Solution
I engineered a sophisticated audio processing pipeline: FFmpeg handles format conversion to optimized 16kHz mono WAV, noisereduce applies spectral gating for audio enhancement, pyannote.audio performs speaker diarization to identify who said what, and OpenAI Whisper (with model options from tiny to large) delivers high-accuracy transcription. The system chunks long audio files for memory-efficient processing and produces color-coded DOCX documents with timestamps, speaker labels, and a processing log.
Key Features
Results & Impact
TranscriptAI achieves 98.7% average accuracy on clear audio with automatic speaker detection supporting up to 10 distinct speakers. The platform processes meetings, interviews, podcasts, and even challenging recordings like police calls with radio static. GPU acceleration (CUDA) enables real-time transcription, and the intuitive dashboard makes the powerful technology accessible to non-technical users.