Marius Mutu
d22038d002
Un singur set de scripturi acum rulează pe orice curs configurat în courses.py. Master rămâne la rădăcina repo (backward-compat M1-M6); cursuri noi (ex. practitioner la shop.cursnlp.ro) primesc un root dedicat (nlp-practitioner/) cu propriile artefacte. - courses.py: config dict (master, practitioner) + course_paths() + validate_manifest_course() (manifest fără course_key = master). - download.py: --course + --modules; trei tipuri de lecții (audio HTTP, Vimeo iframe via yt-dlp audio-only, text-only cu captură HTML); merge cu manifest existent în loc de replace; strip [Audio] pentru backward-compat paths. - transcribe.py: --course + --modules; skip type==text; path-uri prin course_paths(); validare course_key. - summarize.py: --course + --compile; template prompt folosește course['name']; scrie SUPORT_CURS.md cu LF explicit (WSL2 baseline). - md_to_pdf.py: --course resolv-ă summaries_dir / pdf_dir per curs. - run.bat: detectează master|practitioner ca primul argument, propagă --course la sub-scripturi; backward-compat run.bat [modules]. - requirements.txt: + yt-dlp. - .gitignore: nlp-practitioner/audio/, audio_wav/, scratch_recon.py, tmp_recon/. - tests/test_regression.sh: 5 gate-uri read-only (import, schema, disk-coherence, SUPORT_CURS byte-identic, cross-course isolation). Regression curs master: PASS (manifest + SUPORT_CURS.md hash identic cu baseline /tmp/suport_before.md). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
49 lines
635 B
Plaintext
49 lines
635 B
Plaintext
# Audio files
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audio/
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*.mp3
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*.wav
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# Whisper models
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models/
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*.bin
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# Manifest (machine-specific state)
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manifest.json
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# Credentials
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.env
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# SRT subtitles (generated, can be re-derived from transcripts)
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*.srt
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# Binaries (downloaded by setup_whisper.py)
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whisper-bin/
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ffmpeg-bin/
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# Temp files
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.whisper_bin_path
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.ffmpeg_bin_path
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# WAV cache (converted from MP3)
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audio_wav/
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# Python
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__pycache__/
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*.pyc
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.venv/
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.venv_pdf/
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# Claude Code local state
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.claude/
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# Logs
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*.log
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# Second course (practitioner) — artifacts only, scripts partajate
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nlp-practitioner/audio/
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nlp-practitioner/audio_wav/
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# Recon scratch
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scratch_recon.py
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tmp_recon/
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