ProjectIntent Over Keywords: From Stitched Embeddings to a Production Fashion Agent · Module 4: Speech: Whisper and MiniLM · part 6 of 26
Part 6 · Module 4: Speech: Whisper and MiniLM

Module 4: Speech: Whisper and MiniLM

22 min read·
About 45 minutes · Needs: Module 2 (the stitched pipeline), Module 3 (frames, CLIP and cosine similarity) · Code: part1-video-rag-stitched/src/embedders/local_models.py, part1-video-rag-stitched/src/text/chunking.py

Where this fits

WHERE THIS FITS Intent Over Keywords this module PART 1: STITCHED VIDEO RAG three models, two spaces, RRF PART 2: UNIFIED VIDEO RAG one model, one space PART 3: STYLEME FastAPI, LangGraph, React QUALITY AND OPERATIONS evals, Docker, Cloud Run Frames ffmpeg, one per 5 s CLIP ViT-B/32 frames + text, 512 d Whisper base speech to text MiniLM 64-word chunks, 384 d Two collections p1_visual, p1_text Rank fusion RRF, k = 60 Gemini answer from transcript text API + Streamlit upload, ask, sources Windows ffmpeg, video + audio Embedding 2 Gemini, one vector One collection p2_unified, no fusion Notes + answer Gemini, cited times React web chat, photo, chips nginx one URL, /api proxy FastAPI /api/chat, limits Sessions in memory, cap of 3 Planner intent before retrieval Implementer batched embed, search Generator stylist reply, ids Grounding check no LLM, drops invented Gemini flash-lite, embedding 2 Qdrant styleme_products Catalogue ingest image + title vectors Langfuse optional traces Test video 26 questions, 4 kinds Video eval Part 1 vs Part 2 Agent evals intent, grounding Guards uploads, limits, secrets Containers compose profiles Cloud Run StyleMe only CI/CD GitHub Actions, WIF Original code v1, v2, fashion-agent
This module: the speech path of Part 1. Whisper and MiniLM are amber because they are models; the text collection is our own code (blue).

Part 1 hears the video through two models. Whisper turns the soundtrack into text with times, and MiniLM turns pieces of that text into vectors that can be searched. This module follows the spoken half of the test video from the audio file to the 12 points in the p1_text collection. Module 3 did the same for the frames and CLIP; Module 5 shows how the two halves are searched and merged.

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