📅 2025-08-04 — Session: Enhanced Whisper Model for Spanish Transcription
🕒 13:30–14:20
🏷️ Labels: Whisper, Transcription, Diarization, Spanish, Automation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the transcription capabilities of the Whisper model, particularly focusing on improving lexical accuracy in Spanish transcriptions and integrating diarization for better ASR results.
Key Activities
- Updated Whisper Transcription Cell: Modified the transcription cell to avoid internal timestamp restoration errors by disabling timestamp post-processing.
- Lexical Accuracy Analysis: Identified specific lexical inaccuracies in Spanish transcriptions, including terminology errors and name misinterpretations, recommending a switch to a multilingual model.
- Diarization Integration: Leveraged diarization outputs to enhance ASR transcription by aligning clip timestamps with diarization segments.
- Quality Assessment: Compared output models based on accuracy, segmentation, and completeness, favoring diarization-driven outputs for better speaker attribution.
- Architectural Planning: Outlined an end-to-end architecture for processing audio/video into AI-curated Markdown, addressing potential challenges.
- Pipeline Design: Designed an automated content ingestion pipeline for efficient content tracking and processing.
- YouTube Metadata Extraction: Explored methods for extracting metadata from YouTube channels using various tools and APIs.
Achievements
- Successfully updated the Whisper model to improve transcription accuracy and integration with diarization.
- Developed a comprehensive plan for an end-to-end audio/video processing architecture.
Pending Tasks
- Implement the recommended switch to a multilingual model for better Spanish transcription accuracy.
- Finalize and test the automated content ingestion pipeline.
- Evaluate the effectiveness of YouTube metadata extraction methods for integration.