FastAPI Video Pipeline Optimization

I have a Python + FastAPI application that already automates script-to-video creation with FFmpeg under the hood. Users supply a script and voice-over; the tool then pulls footage from both online databases and my local library, aligns visuals to narration, lets the user preview, and finally exports the video.

Where I need help
• The biggest headache is footage-matching accuracy—clips sometimes feel random or arrive at the wrong timestamps.
• Secondary pain points include sluggish searching/downloading/rendering, occasional preview or export glitches, and API calls that retry when they shouldn’t.

Scope of this engagement
1. Inspect the current pipeline on my Apple Silicon M4 Pro workstation.
2. Pinpoint the exact bottlenecks (profiling, logs, or your preferred tools).
3. Implement lean, targeted fixes rather than a full rewrite—think refined FFmpeg filter graphs, smarter async calls in FastAPI, or improved asset-matching logic.
4. Deliver before/after benchmarks so improvements are measurable (e.g., clip-match precision, processing time, export reliability).

Acceptance criteria
– Footage-matching precision demonstrably higher on a representative test script.
– Overall processing time reduced and no redundant downloads.
– Preview and final export play through without timing hiccups.

When you respond, please include links or brief summaries of past Python/FFmpeg video-automation work and your estimated hours and cost for the first milestone (diagnosis + initial fixes). I’m ready to grant repo access and a set of sample scripts as soon as we agree on the plan.