deep-read-summarize v0.3.6

Unified video pipeline with a built-in faster-whisper transcription bootstrap.

What's new

Video: unified pipeline (no more "tiers") — the goal is always a full transcript before deep-reading:
1. if the video has a platform subtitle (Bilibili AI subtitle / YouTube CC), use it directly (= full text, fastest, zero-dependency);
2. otherwise, transcribe via the built-in scripts/transcribe.ps1.
New self-bootstrapping transcription tool scripts/transcribe.ps1: uv creates a Python 3.12 venv, installs faster-whisper from a PyPI mirror, and pulls the model from hf-mirror with caching. faster-whisper bundles PyAV, so no separate ffmpeg is needed.
Workflow tuning: maxChunks default 6→4, maxRetries default 1→0, batched parallel (3 per batch) to avoid long hangs.
yt-dlp: never auto-download the exe (GitHub direct is slow/flaky); use winget/pip or degrade; add --socket-timeout 15 --retries 3.
Source used (textSource: subtitle|transcription|desc|manual) is surfaced in the run log ([source]) and result — not written into the note.

First transcription downloads the model (small ≈ 484MB, via hf-mirror); cached afterwards, so it happens only once. Only videos with no public subtitle trigger transcription.
For quality, no-subtitle videos take longer (CPU ≈ 0.5–2× video duration); subtitle videos are near-instant. This is intentional.
This is an assistive deep-reading tool, not a substitute for learning — verify against the source.

Feedback welcome: open an issue.