admin
06f0e7e651
Introduce Meeting Context V1 with YAML schema, validation and template. Support optional --meeting-context during chunk extraction. Inject authoritative Meeting Context into extraction prompts. Record Meeting Context provenance in extraction output. Activate todos.md in shared prompt assembly. Strengthen responsibility attribution and decision/todo boundaries. Add focused Gold scenarios and validation tests. Update architecture and pipeline documentation.
71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
# Project Process Meeting Real-Life Sample
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## Purpose
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This directory preserves the complete input data for the current real meeting
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experiment so the Meeting Lab pipeline can be reproduced on another computer.
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This is a deliberately selected reference sample, not ordinary generated
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runtime data.
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## Confidentiality
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This sample contains real meeting data. Keep the repository private. Do not
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copy, publish or redistribute these files outside the intended private
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development context.
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Avoid exposing unnecessary personal details in derived documentation, reports
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or screenshots.
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## Language
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The meeting language is German.
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## Contents
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Original input files:
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- `audio/meeting_speech.wav`: source meeting audio.
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- `transcript/meeting_speech.json`: raw Whisper transcript JSON.
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- `transcript/meeting_speech_cleaned.json`: cleaned Whisper transcript JSON.
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Reference material:
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- `meeting_context.yaml`: manually maintained Meeting Context V1 scaffold for
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reliable metadata. It can be validated and injected into chunk extraction
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with `--meeting-context`; later pipeline stages are not connected yet.
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- `reference/human_reference_protocol.md`: not currently included. No exact
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existing human-written protocol file was found in the repository workspace.
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Generated outputs are intentionally excluded. This sample does not include
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generated chunks, normalized chunks, segmentation outputs, extraction JSON
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files, consolidated outputs, generated protocols, raw model responses or
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temporary files.
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## Intended Pipeline Use
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This sample may be used to reproduce and test:
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- audio/transcription workflow setup
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- Whisper JSON cleanup behavior
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- transcript chunking
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- normalization
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- segmentation experiments
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- extraction experiments
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- canonicalization and future consolidation experiments
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Generated artifacts should be written to normal experiment or benchmark output
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locations, not back into this input sample directory.
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## Git LFS
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The WAV audio is intentionally versioned through Git LFS using the scoped
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repository rule for `samples/real_live/**/*.wav`.
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After cloning the repository, retrieve LFS files with:
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```text
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git lfs pull
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```
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