video-text-extractor 1.0.5

Reconstruct code, slides, and prose from YouTube videos as Markdown.

video-text-extractor (vte)

Reconstruct code, slides, and prose shown in YouTube videos as a single Markdown document plus a machine-readable JSON manifest.

v1.0.0 ships the full 8-stage pipeline with PySceneDetect, VLM-fallback classifier, VLM-primary OCR, LLM topic segmentation, typing-progression code reconstruction with tree-sitter validation, Whisper ASR (opt-in via [asr] extra), WhisperX alignment (opt-in via [align]), vte validate-manifest, vte models pull/list, Dockerfile, and CI/CD workflows.

Requirements

• Python 3.12+
• System binaries on PATH: ffmpeg, ffprobe, tesseract, yt-dlp
• Apple Silicon or NVIDIA GPU recommended for local VLM / Whisper (CPU fallback works)

Install

From PyPI (once published)

pip install video-text-extractor             # core
pip install "video-text-extractor[asr]"      # + Whisper ASR fallback
pip install "video-text-extractor[align]"    # + WhisperX word-level alignment

From Docker

docker run --rm -v "$PWD":/work ghcr.io/jon-chun/vte extract <url> --out /work/out

From source

git clone https://github.com/jon-chun/video-text-extractor
cd video-text-extractor
uv sync
uv run vte extract <url>

For local development with all optional extras:

uv sync --all-extras

Quickstart

uv run vte extract https://www.youtube.com/watch?v=<ID> --out ./out
# or with a local file:
uv run vte extract path/to/video.mp4 --out ./out

Outputs per video:

out/<video-id>/
  metadata.json
  video.mp4
  audio.m4a
  transcript/
    raw_captions.vtt        # if YouTube provided captions
    asr_segments.json       # if Whisper ASR ran
    final.json
  keyframes/
    raw/000001.png ...      # all uniform samples
    selected/000001.png ... # post-dedup working set
    index.json
    classifications.json
  ocr/
    000001.json ...
    _done.marker
  narrative/
    segments.json
  output/
    reconstructed.md        # main artifact
    manifest.json           # schema-validated
    README.md               # human-readable run summary
    elements.json
    assets/
      000001.png            # downscaled slide
      000001.thumb.png      # 200px thumbnail
  vte.log                   # structured JSON log

Subcommands

vte version                                              # print version
vte extract <url-or-path> [options]                      # main pipeline
vte providers ping --provider <name> --model <m>         # provider smoke test
vte models list                                          # list configured providers
vte models pull                                          # ollama pull for each ollama-configured model
vte validate-manifest <path>                             # validate manifest.json against schema

vte extract flags:

--out DIR                       output directory (default: ./out)
--config PATH                   per-job YAML override
--force                         re-run all stages even if outputs exist
--force-stage NAME              re-run a single stage
--log-level LEVEL               structlog level (default: info)
--respect-no-derivatives        abort (exit 5) on ND-licensed videos

Exit codes:

• 0 — success (possibly with downgrades)
• 1 — unexpected error
• 2 — config error (e.g. missing env var)
• 3 — pre-emit stage failed
• 4 — emit failed
• 5 — refused (--respect-no-derivatives and ND license)
• 6 — provider HTTP error (from vte providers ping)
• 7 — manifest schema validation failed (from vte validate-manifest)

Providers

Configure LLM/VLM backends via the ProviderSpec shape. Smoke-test connectivity:

# Local Ollama:
uv run vte providers ping --provider ollama --model qwen2.5:14b-instruct

# OpenAI (requires OPENAI_API_KEY in env):
uv run vte providers ping --provider openai --model gpt-4o-mini \
  --api-key-env-var OPENAI_API_KEY

# Anthropic, OpenRouter — same shape; substitute provider and env var.

Real Models (default still local)

When cfg.classifier.vlm_fallback.enabled: true and an Ollama (or hosted-API) provider is configured, ambiguous frames are reclassified by a VLM. When cfg.ocr.primary.provider != "tesseract", the configured VLM does primary OCR with Tesseract running in parallel for an agreement score. When cfg.narrative.segmentation.provider.provider != "fixed-window", the configured LLM generates real topic segments from the transcript.

Default config is Ollama-first — qwen2.5vl:7b (classifier VLM) and llama3.1:8b (narrative + code repair) drive the pipeline out of the box. Pull them once (uv run vte models pull) and you get the full v1.0 quality on the next extract. If Ollama isn't running or the models aren't pulled, every stage downgrades gracefully (recorded in manifest.json → pipeline.stages[].downgrades[]); the pipeline still exits 0 with M1-tier output. (v1.0.1 briefly defaulted to qwen3-vl, but its chain-of-thought reasoning mode produced empty/truncated structured output; v1.0.2 reverted to non-thinking models.)

To switch to a paid API instead of local Ollama, override the provider in your config:

ocr:
  primary:
    provider: "openai"
    model: "gpt-4o-mini"
    api_key_env_var: "OPENAI_API_KEY"
narrative:
  segmentation:
    provider:
      provider: "anthropic"
      model: "claude-sonnet-4-5"
      api_key_env_var: "ANTHROPIC_API_KEY"

To revert to the pre-v1.0 heuristic-only behaviour (no model calls), set classifier.vlm_fallback.enabled: false, narrative.segmentation.provider.provider: "fixed-window", and reassembly.code.repair_provider.provider: "none".

Code Reconstruction

Code-classified frames go through a four-stage reconstruction pipeline:

1. Typing-progression detection (reasm/code_diff.py) — consecutive code frames within a scene that look like incremental edits get collapsed; only the last frame's text is emitted.
2. Language detection (reasm/language_detect.py) — slide-hint lookup → Pygments guess_lexer → optional LLM tiebreaker.
3. OCR repair (reasm/code_repair.py) — LLM-driven fix of common OCR errors (O/0, l/1, broken indent), gated by tree-sitter validation: if the LLM rewrites too much, the repair is rejected.
4. Validation (reasm/tree_sitter_validate.py) — ast.parse for Python, tree-sitter for the other six v1 languages.

Default config: repair is OFF (reassembly.code.repair_provider.provider: "none"), but language detection + tree-sitter validation run on every code block. Each emitted code element carries language, language_source, repair_status, validation, and agreement.

To enable LLM repair against a local Ollama Qwen2.5-Coder:

reassembly:
  code:
    repair_provider:
      provider: "ollama"
      model: "qwen2.5-coder:14b"
      temperature: 0.0

Supported languages (v1): Python, JavaScript, TypeScript, Go, Rust, Java, C, C++, Bash.

Whisper ASR (opt-in)

vte falls back from captions to Whisper ASR when captions are missing or empty. Enable with the [asr] extra:

pip install "video-text-extractor[asr]"

Word-level alignment via WhisperX is a further opt-in:

pip install "video-text-extractor[align]"

Both extras are off by default to keep the core install lean.

Configuration

Precedence (highest → lowest):

1. CLI flags
2. --config <path.yaml> (per-job)
3. ~/.config/vte/config.yaml (user-wide)
4. Shipped src/vte/defaults.yaml

Secrets (for hosted-API providers) come from environment variables only — see .env.example. The config_snapshot in manifest.json redacts any key whose underscore-separated components include key, token, secret, or password.

See docs/configuration.md for the full YAML reference.

Legal / Fair Use

vte is for personal, fair-use, and educational reconstruction of video content. Users are responsible for confirming their use respects the source video's license and applicable local law. The tool does not bypass DRM. The --respect-no-derivatives flag aborts (exit code 5) on videos licensed under no-derivatives terms.

Development

uv sync --all-extras
uv run pytest -v                                # full suite
uv run pytest -m "not live and not e2e" -v      # default CI subset (189 tests)
uv run pytest -m integration -v                 # integration tests
uv run ruff check src tests
uv run ruff format src tests
uv run mypy src/vte/

Architecture

8-stage pipeline:

URL/path → acquire → transcript → keyframes → classify → ocr → narrative → reassembly → emit

Stage	Implementation
acquire	yt-dlp + local-file passthrough; ND-license check via --respect-no-derivatives
transcript	VTT captions → Whisper ASR fallback ([asr]) → WhisperX alignment ([align])
keyframes	PySceneDetect scene boundaries + uniform sampling within scenes + pHash dedup
classify	Heuristics (text coverage, hue counts, edge ratio) + VLM fallback for ambiguous frames
ocr	VLM primary + Tesseract sanity check (or Tesseract-only by config)
narrative	LLM topic segmentation (or fixed-window fallback)
reassembly	Diff-based code reconstruction + language detect + tree-sitter validation + segment interleave
emit	Manifest + Markdown + per-run README; schema-validated

For the full data flow, module map, and extension points see:

• Architecture — pipeline stages, module map, extension points.
• Configuration — full YAML reference + provider spec.
• FAQ — troubleshooting + common gotchas.
• Design plan — cumulative why behind every decision.
• Tech spec — static v1 specification.

License

MIT.