Improve speaker-review excerpts

.env.example

@@ -0,0 +1,18 @@
+MKA_WHISPER_MODEL=/path/to/ggml-large-v3-turbo.bin
+MKA_WHISPER_EXECUTABLE=whisper-cli
+MKA_FFMPEG_EXECUTABLE=ffmpeg
+MKA_PROTOCOL_MODEL=qwen3.8:27b
+MKA_OLLAMA_ENDPOINT=http://127.0.0.1:11434
+MKA_DATA_ROOT=data/meetings
+MKA_GLOSSARY_DATABASE=data/database/glossary.sqlite3
+MKA_WHISPER_THREADS=auto
+MKA_DIARIZATION_MODE=auto
+MKA_DIARIZATION_RUNTIME=native
+# MKA_DIARIZATION_CONTAINER_IMAGE=
+# MKA_DIARIZATION_CONTAINER_ARGS=[]
+
+# Example AMD ROCm container configuration for a compatible workstation:
+# MKA_DIARIZATION_MODE=gpu
+# MKA_DIARIZATION_RUNTIME=container
+# MKA_DIARIZATION_CONTAINER_IMAGE=rocm/pytorch:rocm7.2.1_ubuntu24.04_py3.12_pytorch_release_2.9.1
+# MKA_DIARIZATION_CONTAINER_ARGS='["--device=/dev/kfd","--device=/dev/dri","--group-add","video"]'

.gitignore

@@ -34,6 +34,7 @@ dist/
 *.sqlite3
 
 # Meeting data
+data/meetings/*
 data/recordings/*
 data/transcripts/*
 data/database/*
@@ -42,3 +43,4 @@ data/database/*
 !data/recordings/.gitkeep
 !data/transcripts/.gitkeep
 !data/database/.gitkeep
+!data/meetings/.gitkeep

CHANGELOG.md

@@ -0,0 +1,45 @@
+# Changelog
+
+## [Unreleased]
+
+### Added
+
+- Retained live stage and total timings for processing and protocol regeneration.
+- Immutable diagnostic generation history with atomic latest-result publication.
+
+- Auto/Fast/Efficient/Powersave protocol profiles with backend-selected threads
+  by default, also applied during mapped-speaker regeneration.
+
+- Versioned UTF-8 YAML import/export for the SQLite terminology glossary, with
+  stable entry IDs, complete validation, and atomic replacement semantics.
+- First Streamlit MVP for audio upload, Meeting Context entry, participant
+  management, processing progress and protocol editing.
+- Application service and Meeting Lab adapter with environment-based runtime
+  configuration.
+- Per-meeting upload and edited-protocol persistence.
+- Unit tests for context construction, configuration translation, progress,
+  failure and result handling.
+- ADR 0011 for the Meeting Lab MVP backend architecture.
+
+### Changed
+
+- Record active glossary configuration without rewriting protocol transcript input.
+
+- Reconciled the documented MVP with the validated Meeting Lab backend.
+- Selected `whisper.cpp` for transcription and made diarization optional.
+- Defined the Python API, progress-event and Meeting Context integration
+  boundaries.
+- Set the user-facing Meeting Assistant GUI as the next milestone.
+
+## [0.1.0] - 2026-07-14
+
+### Added
+
+- Initial project structure
+- Architecture documentation
+- ADR framework
+- Domain model foundation
+- Python project bootstrap
+- Pydantic integration
+- Ruff integration
+- Pytest integration

CONTRIBUTING.md

@@ -0,0 +1,38 @@
+# Contributing
+
+1. Branch Strategy
+2. Development Workflow
+3. Commit Messages
+4. Definition of Done
+5. Coding Standards
+6. Documentation Requirements
+7. Testing
+8. Creating ADRs
+
+## Development Workflow
+
+See ADR 0010 for the overall development workflow.
+
+---
+
+## Commit Messages
+
+Commit messages should be:
+
+- short
+- descriptive
+- written in the imperative mood
+
+Examples:
+
+- Add meeting import
+- Implement transcript persistence
+- Refactor storage layer
+- Fix timestamp serialization
+
+Avoid messages such as:
+
+- Update
+- Changes
+- Fix
+- Miscellaneous

PROJECT_KNOWLEDGE.md

@@ -1,163 +1,186 @@
 # Project Knowledge
 
+## Meeting language
+
+The GUI's `meeting_language` is passed through `MeetingDetails.language` to both
+Meeting Context `meeting.language` and `MvpMeetingConfig.language` (Whisper).
+Meeting Lab derives explicit protocol output language from the persisted context,
+also during regeneration, and records derived `output_language` provenance in
+protocol runtime metadata. Missing context language defaults to German. There is
+no separate protocol-language setting or automatic context/transcript translation.
+
 ## Vision
 
-The project creates a complete pipeline that transforms spoken meetings into structured organizational knowledge.
+Meeting Assistant turns recorded meetings into reviewed, user-facing protocols
+and, over time, searchable organizational knowledge. It is the application
+layer over the reusable Meeting Lab processing backend.
 
-The transcript is the canonical source.
+The source audio and canonical transcript are preserved. Generated protocols
+are derived, reproducible artifacts and require human review.
 
-AI-generated summaries are reproducible artefacts.
+## System Boundary
 
----
+### Meeting Lab
+
+Meeting Lab owns reusable and experimental processing:
+
+- FFmpeg audio preparation, with optional normalization enabled by default
+- `whisper.cpp` transcription
+- optional `pyannote.audio` diarization
+- protocol generation
+- pipeline orchestration and progress events
+
+Its reusable integration surface is the Python API:
+
+- `MvpMeetingConfig`
+- `MvpRunResult`
+- `run_mvp_meeting(...)`
+
+The CLI is a thin adapter, not the Meeting Assistant integration boundary.
+
+### Meeting Assistant
+
+Meeting Assistant owns user interaction and product workflow:
+
+- audio-file selection
+- structured Meeting Context editing
+- participant management
+- versioned YAML import/export for reusable People lists, using replace semantics
+- versioned JSON import/export for the complete user-configurable run-input
+  form; source media is represented only by optional filename metadata and must
+  be selected again after import
+- optional explicit speaker mapping
+- post-diarization speaker review and protocol-only regeneration from existing
+  run artifacts
+- pipeline launch and progress display
+- protocol review and editing
+- a global SQLite terminology glossary whose active canonical core terms and
+  recognition aliases are rendered through Meeting Context into direct
+  protocol prompts
+- versioned UTF-8 YAML glossary interchange (`version: 1`) that preserves entry
+  IDs and atomically replaces SQLite state only after complete validation
+- export and presentation of protocol versions
+
+Processing logic must not be duplicated in the application.
+
+Diarization runtime, container image and ordered container arguments are
+machine configuration. `MKA_DIARIZATION_CONTAINER_ARGS` is a JSON array of
+strings forwarded unchanged to Meeting Lab; these details are not normal UI
+controls.
+
+People-list YAML is a Meeting Assistant application concern and contains only
+stable IDs, display names, roles, organizations and attendance states. It does
+not contain meeting metadata or processing settings. Named team or meeting
+templates may build on this later, but are not part of the current mechanism.
+
+## Validated MVP Pipeline
+
+```text
+Imported audio
+    -> always prepare as mono, 16 kHz PCM WAV with FFmpeg
+       (optionally normalize loudness; default on)
+    -> transcribe with whisper.cpp and large-v3-turbo
+    -> optionally diarize with pyannote.audio Community-1
+    -> when diarized, wait for optional speaker review before explicit protocol generation
+    -> otherwise generate a protocol directly from the full transcript
+    -> review and edit by a human
+```
+
+Mandatory fixed five-minute chunking is not part of the productive MVP path.
+Chunking or segmentation used internally by an engine does not shape the
+Meeting Assistant storage or domain model.
+
+On the North reference machine, `whisper.cpp` with Vulkan on an AMD RX 9070
+transcribed approximately 93.5 minutes in about 100 seconds. Pyannote through
+PyTorch/ROCm processed the same duration in approximately 98.6 seconds. These
+are validation observations, not performance guarantees or hardware
+requirements. CPU execution remains supported and may be substantially slower.
+
+Protocol-generation performance is selected through intentionally abstract
+profiles rather than hardware controls in the GUI. `auto` is the default and
+delegates thread selection to the inference backend; with Ollama this means
+omitting `num_thread` entirely. The explicit resource profiles currently map
+`fast` to 16 Ollama CPU threads, `efficient` to 10, and `powersave` to 4. These
+concrete mappings belong to the backend/configuration boundary and may evolve
+independently of the user-facing profile semantics.
+
+## Meeting Context and Speakers
+
+`MeetingContext` is a structured domain object containing meeting metadata,
+present participants, and people who were mentioned without attending. The GUI
+records exactly `present` or `mentioned_only`; missing legacy participant status
+defaults to `present` in Meeting Lab. It can also contain optional explicit mappings from
+`SPEAKER_XX` labels to `participant_id` values.
+
+A speaker mapping is authoritative only when a user explicitly confirms it and
+may reference only a present participant.
+Speakers otherwise remain anonymous. Automatic speaker-name inference is not
+allowed. The GUI must create and edit Meeting Context; hand-written YAML is not
+a product requirement.
+
+Speaker selectors filter participants using a snapshot of all current widget
+selections, falling back to saved mappings before first interaction. Filtering
+retains existing assignments; clearing a selection releases the participant.
+Completeness counts and warnings cover detected speakers only and do not gate
+protocol generation or change mapping persistence. A diarized run persists the
+`awaiting_speaker_review` checkpoint after diarization; an empty, partial or
+complete mapping can then explicitly generate a protocol from the existing
+artifacts. Failed generation preserves that checkpoint and saved mappings for retry.
+
+## Progress Contract
+
+Meeting Lab emits stage-based progress events for:
+
+- `preparing`
+- `transcription`
+- `diarization`
+- `protocol_generation`
+- `completed`
+- `failed`
+
+The GUI consumes this event interface. Percentages are shown only when real
+measurable progress is available.
+
+## Protocol Policy
+
+Direct full-transcript protocol generation is the practical MVP direction.
+`qwen3.8:27b` has shown strong readability and contextual synthesis;
+`qwen3.6:35B-A3B` has shown somewhat more conservative behavior in some areas.
+Experimental dual-model and diarization-assisted hard-fact extraction has not
+demonstrated reliably better strict attribution accuracy and is not mandatory.
+
+The product should ultimately offer both a detailed contextual protocol and a
+shorter participant/distribution version. The short version remains follow-up
+work if it is not available for the first GUI milestone.
+
+Confirmed meeting-specific corrections should ultimately be reusable by both
+protocol views. The detailed correction and selective-regeneration decision is
+recorded in [ADR 0012](docs/adr/0012-post-run-corrections.md); it is later
+product work, not a current MVP requirement.
 
 ## Design Principles
 
-Offline-first where practical.
+- offline-first where practical
-
+- explicit application/backend boundaries
-Replaceable AI components.
+- replaceable AI components
-
+- CPU-compatible operation with optional acceleration
-Vendor independence.
+- immutable source artifacts and traceable derived artifacts
-
+- no automatic identity claims
-Modular architecture.
+- human review of generated protocols
-
+- small modules and simple interfaces
-Small focused modules.
+
-
+Active glossary aliases are forwarded to Meeting Lab as provenance metadata.
-Simple interfaces.
+Canonical terminology remains Meeting Context guidance; the exact protocol
-
+transcript input and the raw Whisper/diarization artifacts are not rewritten.
----
+`glossary_replacements` remains empty.
-
+
-## Core Pipeline
+Processing and protocol regeneration show measured monotonic stage/total timings,
-
+including frozen failure durations. The latest timing display survives ordinary
-Recorder
+Streamlit reruns. Initial-run durations are saved in `run_metadata.json`;
-
+regeneration display timings remain session-local. Worker callbacks capture UI
-↓
+configuration before dispatch and never read Streamlit session state.
-
+
-Transcription
+Meeting Lab retains immutable generation records including prompt, transcript
-
+input, model response/metadata, glossary configuration, mappings and Meeting
-↓
+Context. Latest protocol/diagnostic/context paths resolve through an atomic
-
+`protocol/current` link. Copy whole runs with relative symlinks preserved.
-Speaker Identification
-
-↓
-
-LLM Analysis
-
-↓
-
-Knowledge Extraction
-
-↓
-
-Storage
-
-↓
-
-Export
-
----
-
-## AI Components
-
-Recorder
-
-Responsible only for recording.
-
-No AI.
-
----
-
-Transcription
-
-Responsible only for speech-to-text.
-
-No summarization.
-
----
-
-Speaker Identification
-
-Responsible only for identifying speakers.
-
-Must not modify transcript text.
-
----
-
-LLM Analysis
-
-Responsible for:
-
-- Summary
-
-- Decisions
-
-- Action Items
-
-- Risks
-
-- Questions
-
-- Knowledge extraction
-
----
-
-Storage
-
-Stores:
-
-Audio
-
-Transcript
-
-Metadata
-
-AI Results
-
-Knowledge Objects
-
----
-
-## Architectural Rule
-
-Every module has exactly one responsibility.
-
----
-
-## Transcript Policy
-
-Never modify the original transcript.
-
-Corrections must create a new derived version.
-
----
-
-## AI Output Policy
-
-Every AI output should be reproducible.
-
-Prompt version should be stored.
-
-Model should be stored.
-
-Timestamp should be stored.
-
----
-
-## Long-Term Goal
-
-Every meeting becomes searchable organizational knowledge.
-
-No information should be lost after the meeting.
-
----
-
-## Engineering Principles
-
-The project follows an architecture-first development approach.
-
-Before implementing a feature:
-
-- define the domain model
-- define module boundaries
-- document architectural decisions
-
-Implementation is intentionally delayed until the architecture is considered sufficiently stable.

README.md

@@ -1,126 +1,264 @@
-# Meeting Knowledge Assistant
+# Meeting Assistant
 
-## Vision
+Meeting Assistant is the user-facing application for turning recorded meetings
+into reviewed, distributable protocols. It uses Meeting Lab as its reusable
+local processing backend.
 
-The Meeting Knowledge Assistant (MKA) is an AI-assisted desktop application that records meetings, creates high-quality transcripts and transforms them into structured knowledge.
+## Current MVP Direction
 
-Unlike traditional meeting assistants, MKA does not aim to replace human interaction during meetings.
+The first product milestone is a desktop GUI that lets a user:
-Its goal is to create an accurate, searchable and long-term knowledge base from spoken communication.
 
-The project follows an "AI-first" architecture:
+- select an existing WAV, FLAC, or M4A recording
+- create and edit structured meeting metadata and relevant people, including whether
+  they were present or only mentioned
+- import and export the reusable People list as versioned YAML
+- optionally map anonymous `SPEAKER_XX` labels to known participants
+- start the Meeting Lab processing pipeline
+- follow stage-based progress
+- review and edit the generated protocol
+- export the reviewed result
 
-Audio
+The MVP does not include integrated recording. OBS Studio remains a recommended
-→ Transcription
+reference recorder, but imported audio is not tied to OBS-specific behavior.
-→ Speaker Identification
-→ AI Analysis
-→ Structured Knowledge
-→ Search
 
----
+## Processing Pipeline
 
-## Core Features
+The existing **Meeting language** selector controls both transcription and protocol
-
+output: `de` means German for both, and `en` means English for both. The selection
-- Import of existing meeting recordings
+is saved in Meeting Context (`meeting.language`) and reused for protocol-only
-- OBS Studio as the recommended reference recorder
+regeneration without retranscription. Older contexts without a language retain
-- High-quality local transcription
+German protocol output. Names, speaker mappings and authored Meeting Context are
-- Speaker diarization
+preserved; no transcript translation stage is added.
-- Editable working transcripts
-- AI-generated meeting minutes
-- Executive summaries
-- Action items
-- Decision tracking
-- Knowledge extraction
-- Local file-based storage
-- Export to Markdown, PDF and DOCX
-
----
-
-## Recording Workflow
-
-The MVP does not include its own audio recorder.
-
-OBS Studio is the recommended reference recorder for online meetings. It can
-
-capture system audio and microphone audio without joining the meeting as a bot.
-
-Initial workflow:
 
 ```text
+Audio file
+    -> FFmpeg preparation (mono, 16 kHz PCM WAV; normalization optional)
+    -> whisper.cpp transcription (large-v3-turbo)
+    -> optional pyannote.audio Community-1 diarization
+    -> optional speaker review and explicit protocol generation (when diarized)
+       or direct full-transcript protocol generation (without diarization)
+    -> human review and export
+```
 
-Online Meeting
+Meeting Assistant calls Meeting Lab's reusable Python API
+(`MvpMeetingConfig`, `MvpRunResult` and `run_mvp_meeting(...)`). It does not run
+the Meeting Lab CLI as a subprocess. Fixed five-minute audio chunking is not a
+required part of this workflow.
 
-    ↓
+GPU acceleration is supported where available, but CPU execution remains a
+compatibility path. Diarization is optional and produces anonymous speaker
+labels. A label identifies a participant only when the user explicitly
+confirms the mapping; automatic speaker-name inference is not allowed.
 
-OBS Studio
+Speaker selectors hide participants already assigned to other speakers while keeping
+the current assignment available. The UI shows detected, assigned and unassigned
+counts, marks unassigned speakers, and warns when mappings remain incomplete.
+Anonymous-speaker protocol generation remains available.
 
-    ↓
+After a diarized run, the result view lists detected `SPEAKER_XX` labels with
+short transcript excerpts. Confirmed mappings regenerate only the protocol
+from the existing diarized transcript; audio preparation, Whisper and Pyannote
+are not rerun. `Unmapped / Unknown` remains valid, and the original anonymous
+diarized transcript is preserved.
 
-WAV Recording
+## Product Outputs
 
-    ↓
+The product direction includes:
 
-Meeting Assistant Import
+- a detailed, contextual protocol for review and continued work
+- a shorter version suitable for participants and distribution
 
-    ↓
+The detailed direct-protocol flow is the practical MVP direction. The shorter
+version may be implemented after the initial GUI. Human review remains part of
+the workflow for all generated protocols.
 
-Transcription
+## Project Boundary
 
-    ↓
+Meeting Lab owns reusable audio preparation, transcription, diarization,
+orchestration and protocol-generation logic. Meeting Assistant owns the GUI,
+context and participant editing, explicit speaker mapping, progress display,
+protocol editing and export.
 
-Speaker Diarization
+Audio normalization is enabled by default and can be disabled in the processing
+options. This controls loudness normalization only: Meeting Lab still prepares
+every WAV, FLAC or M4A source as canonical audio before transcription.
 
-    ↓
+The **Performance profile** selector controls protocol-generation runtime using
+the abstract Auto (default), Fast, Efficient, and Powersave profiles. Auto lets
+the inference backend select its own thread configuration; for Ollama, Meeting
+Assistant intentionally sends no `num_thread` option. Fast, Efficient, and
+Powersave are explicit resource profiles currently mapped to 16, 10, and 4
+Ollama CPU threads. These concrete mappings may evolve independently of the UI
+semantics.
 
-Analysis and Export
+The People section can export its current entries to a UTF-8 `people.yaml` file
+and replace them from a previous `.yaml` or `.yml` export. Stable person IDs,
+names, roles, organizations and attendance states are retained. This is a small
+reuse mechanism, not a server-side participant library or named meeting-template
+system.
 
----
+## Run the Streamlit MVP
 
-## Design Goals
+The development setup expects `meeting-assistant` and `meeting-lab` to be
+sibling repositories. Meeting Lab currently imports its API through the
+`src.meeting_lab` package path, so its repository root must be supplied on
+`PYTHONPATH`. Meeting Assistant does not modify `sys.path` at runtime.
 
-- High transcription quality
+Create a virtual environment and install Meeting Assistant, including
-- Modular architecture
+Streamlit:
-- Offline-first whenever practical
-- Replaceable AI components
-- Vendor independence
-- Long-term maintainability
 
----
+```bash
+python3 -m venv .venv
+.venv/bin/pip install -e '.[dev]'
+.venv/bin/pip install -e ../meeting-lab
+```
 
-## Philosophy
+Configure at least the local whisper.cpp model. All supported values are shown
+in `.env.example`; export them in the shell because the application does not
+load `.env` files implicitly:
 
-The transcript is the primary asset.
+```bash
+export MKA_WHISPER_MODEL=/path/to/ggml-large-v3-turbo.bin
+export MKA_WHISPER_EXECUTABLE=whisper-cli
+export MKA_FFMPEG_EXECUTABLE=ffmpeg
+export MKA_PROTOCOL_MODEL=qwen3.8:27b
+```
 
-Everything else (summaries, reports, action items, knowledge extraction)
+Optional machine-specific settings include:
-can always be regenerated using better AI models in the future.
 
-Therefore:
+- `MKA_DATA_ROOT` (default: `data/meetings`)
+- `MKA_OLLAMA_ENDPOINT` (default: `http://127.0.0.1:11434`)
+- `MKA_WHISPER_THREADS` (default: `auto`)
+- `MKA_FFMPEG_EXECUTABLE` (default: `ffmpeg` found on `PATH`)
+- `MKA_DIARIZATION_MODE` (`auto`, `cpu`, or `gpu`; default: `auto`)
+- `MKA_DIARIZATION_RUNTIME` (`native` or `container`; default: `native`)
+- `MKA_DIARIZATION_CONTAINER_IMAGE` (required for container diarization)
+- `MKA_DIARIZATION_CONTAINER_ARGS` (default: no extra arguments), encoded as a
+  JSON array of strings so ordering and leading dashes are preserved exactly
+- `MKA_GLOSSARY_DATABASE` (default: `data/database/glossary.sqlite3`), the local
+  SQLite file used by the global terminology glossary
 
-Audio
+## Terminology glossary
-→ Transcript
-is considered immutable.
 
-AI output is considered reproducible.
+The Streamlit **Terminology glossary** section manages recurring product,
+material, organization, acronym, and technical names. Store canonical core
+terms such as `Secugrid HS`, not every compound such as `Secugrid HS Düse`.
+Aliases help the protocol model recognize transcript variants while retaining
+the surrounding wording. Only active entries are added to Meeting Context as
+authoritative terminology for direct protocol generation; inactive entries
+remain stored but are omitted. The production database starts empty.
 
----
+The database defaults to `data/database/glossary.sqlite3`. It is created and
+bootstrapped automatically and can be moved with `MKA_GLOSSARY_DATABASE`.
+Glossary integration does not rewrite raw or diarized transcript artifacts.
 
-## Planned Architecture
+Use **Export glossary** to download the complete SQLite-backed glossary as
+UTF-8 YAML, and **Import glossary** to replace it from a previously exported
+file. Imports are fully validated before a single SQLite transaction replaces
+the current glossary, so malformed or conflicting files leave existing data
+unchanged. Schema version 1 is:
 
-Recorder
+```yaml
-↓
+version: 1
-Transcription
+glossary:
-↓
+  - id: 1
-Speaker Identification
+    canonical_term: Secugrid HS
-↓
+    aliases:
-LLM Processing
+      - Sikirgut
-↓
+    category: product
-Knowledge Database
+    description: Canonical product spelling
-↓
+    active: true
-Export
+```
 
----
+`id` is the stable SQLite glossary-entry identifier. Categories are `product`,
+`material`, `organization`, `technical_term`, `acronym`, or `other`.
 
-## Current Status
+## Input configuration import and export
 
-Project planning.
+Use **Export inputs** to save the current Meeting Assistant run-input form as a
+small, versioned JSON file and **Import inputs** to restore it later. The file
+contains meeting metadata, participant records, language, date, audio
+normalization, and diarization choices. It contains form configuration only:
+generated prompts, protocols, run artifacts, and source-media contents are not
+included.
 
-No implementation has started yet.
+The original source filename may be retained as a reminder, but importing does
+not restore an upload. Select the audio file explicitly before starting the new
+run.
+
+Protocol generation with `qwen3.8:27b` explicitly requests a 32,768-token
+Ollama context with thinking disabled; Ollama's machine default may otherwise
+be only 4,096. Keep normal prompt input at approximately 29,000 tokens or less.
+Although a 31,038-token synthetic prompt passed, larger input is not assumed
+safe merely because the model advertises a 262,144-token native context.
+
+For example, a compatible AMD ROCm workstation can configure validated
+container access without adding controls to the Streamlit UI:
+
+```bash
+export MKA_DIARIZATION_MODE=gpu
+export MKA_DIARIZATION_RUNTIME=container
+export MKA_DIARIZATION_CONTAINER_IMAGE=rocm/pytorch:rocm7.2.1_ubuntu24.04_py3.12_pytorch_release_2.9.1
+export MKA_DIARIZATION_CONTAINER_ARGS='["--device=/dev/kfd","--device=/dev/dri","--group-add","video"]'
+```
+
+The JSON elements are forwarded as four separate, ordered Meeting Lab
+container arguments. Runtime and hardware details remain machine-specific
+environment configuration; the UI continues to expose only the diarization
+on/off choice.
+
+From the Meeting Assistant repository, start the UI with:
+
+```bash
+PYTHONPATH=src:../meeting-lab .venv/bin/streamlit run src/mka/ui/streamlit_app.py
+```
+
+### North launcher
+
+In the post-diarization Assistant worktree on North, use the checked-in
+launcher instead of creating another virtual environment:
+
+```bash
+./run_north.sh
+```
+
+It reuses North's validated Assistant virtual environment and external
+Whisper/Ollama/Docker-ROCm runtime while loading Meeting Assistant from this
+worktree and Meeting Lab from
+`/opt/git-projekts/meeting-lab`. `HF_TOKEN`, when present, is
+passed through to the disposable diarization container without being stored by
+the launcher.
+
+Streamlit opens `http://localhost:8501` by default. Installing the sibling
+Meeting Lab project supplies its runtime requirements such as PyYAML and
+Requests. Optional diarization dependencies are needed only when diarization
+is enabled.
+
+Uploaded source files are stored under
+`data/meetings/<meeting-id>/uploads/`. Meeting Lab run artifacts are stored
+under `data/meetings/<meeting-id>/runs/`. The reviewed protocol is saved as
+`protocol_edited.md` inside its run directory; the generated `protocol.md`
+remains unchanged.
+
+The upload remains in its original format and is passed unchanged to Meeting Lab.
+Meeting Lab creates the canonical mono 16 kHz signed PCM16 WAV run artifact used by
+transcription; Meeting Assistant does not duplicate audio conversion.
+
+See [Architecture](docs/architecture.md), [Project Knowledge](PROJECT_KNOWLEDGE.md),
+[Roadmap](ROADMAP.md) and [ADR 0011](docs/adr/0011-use-meeting-lab-mvp-backend.md).
+
+Active glossary aliases are forwarded to Meeting Lab as provenance metadata.
+Canonical terminology remains Meeting Context guidance; the exact protocol
+transcript input and the raw Whisper/diarization artifacts are not rewritten.
+`glossary_replacements` remains empty.
+
+Processing and protocol regeneration show measured monotonic stage/total timings,
+including frozen failure durations. The latest timing display survives ordinary
+Streamlit reruns. Initial-run durations are saved in `run_metadata.json`;
+regeneration display timings remain session-local. Worker callbacks capture UI
+configuration before dispatch and never read Streamlit session state.
+
+Meeting Lab retains immutable generation records including prompt, transcript
+input, model response/metadata, glossary configuration, mappings and Meeting
+Context. Latest protocol/diagnostic/context paths resolve through an atomic
+`protocol/current` link. Copy whole runs with relative symlinks preserved.

ROADMAP.md

@@ -1,112 +1,62 @@
 # Roadmap
 
-## Phase 1: Minimum Viable Processing Pipeline
+## Next Milestone: Meeting Assistant MVP GUI
 
-- Import existing WAV recordings
+Build the user-facing application over the validated Meeting Lab Python API.
-- Validate recording metadata
-- Create meeting directory
-- Create canonical transcript
-- Save transcript as JSON
-- Export transcript as Markdown
-- Archive confirmed recordings as FLAC
 
----
+- select an existing audio file
+- reliably import WAV, FLAC and M4A through canonical FFmpeg preparation
+- offer optional audio normalization, enabled by default
+- create and edit Meeting Context through structured fields
+- add and manage present and `mentioned_only` people
+- optionally map `SPEAKER_XX` labels to participants with explicit confirmation
+- configure and start `run_mvp_meeting(...)`
+- display stage-based status and real progress when available
+- handle completed and failed runs clearly
+- display and edit the detailed generated protocol
+- export the reviewed protocol
 
-## Phase 2
+The milestone must keep diarization, GPU acceleration and fixed-duration audio
+chunking optional. It must not launch the Meeting Lab CLI as a subprocess or
+duplicate Meeting Lab processing logic.
 
-Speaker diarization
+Complete real-meeting validation of this vertical slice before expanding the
+post-run workflow.
 
-- Speaker detection
+## Following Milestone: Distribution Protocol
-- Speaker naming
-- Timeline view
 
----
+- derive or generate a shorter participant-facing version
+- allow review and editing before distribution
+- export both detailed and short versions
+- retain provenance linking each version to its transcript, configuration,
+  prompt and model where practical
 
-## Phase 3
+## Later Product Work
 
-Meeting intelligence
+- speaker/name review with explicit human confirmation
+- deterministic correction of suitable derived artifacts, beginning with a
+  simple search-and-replace path
+- protocol-only regeneration from existing transcription and diarization plus
+  corrected mappings and Meeting Context
+- transcript viewing and broader correction workflows
+- recording and artifact lifecycle management
+- search, tags, projects and meeting history
+- richer Markdown, PDF and DOCX export
+- optional SQLite indexing and full-text search
+- integrated recording controls
+- calendar and conferencing integrations
+- semantic search and organizational knowledge features
 
-- Executive Summary
+## Research and Optional Extensions
-- Action Items
-- Decisions
-- Risks
-- Open Questions
-
----
-
-## Phase 4
-
-Knowledge system
-
-- SQLite database
-- Full text search
-- Semantic search
-- Tags
-- Projects
-- Participants
-
----
-
-## Phase 5
-
-Desktop application
-
-- Recording UI
-- Transcript viewer
-- Search
-- Export
-
----
-
-## Phase 6
-
-Integrations
-
-- Outlook
-- Teams
-- Google Calendar
-- Local LLM
-- OpenAI
-- Ollama
-
----
-
-## Future Phase: Integrated Recording
-
-- Capture microphone audio
-
-- Capture system audio
-
-- Support separate audio channels
-
-- Provide recording controls in the desktop application
-
-- Replace or complement the external OBS workflow
-
-- Live transcription
-
-- Real-time summaries
-
-- Company glossary
-
-- Custom vocabulary
-
-- Automatic project detection
-
-- Meeting templates
-
-- Voice identification
-
-- Audio cleanup
 
+- live transcription and real-time summaries
+- company glossary and custom vocabulary
+- voice identification with explicit consent and confirmation
+- automatic name and speaker suggestions only as non-authoritative candidates
+  for later human review
+- audio cleanup
 - OCR for shared screens
-
+- RAG and knowledge-graph integration
-- Timeline with bookmarks
+- multi-language meetings and translation
-
+- dual-model or evidence pipelines only if later validation demonstrates a
-- RAG knowledge integration
+  material reliability benefit
-
-- Multi-language meetings
-
-- Translation
-
-- Automatic follow-up generation

docs/adr/0001-project-vision.md

@@ -2,7 +2,7 @@
 
 ## Status
 
-Accepted
+Accepted, amended by [ADR 0011](0011-use-meeting-lab-mvp-backend.md)
 
 ## Context
 
@@ -30,3 +30,8 @@ The transcript is treated as the canonical source. AI-generated summaries and kn
 - AI outputs must be reproducible where practical.
 - Modules should remain replaceable.
 - The project should avoid unnecessary vendor lock-in.
+
+ADR 0011 narrows the practical MVP to a user-facing application over the
+reusable Meeting Lab backend. Direct protocol generation is the validated MVP
+path; a separate knowledge-extraction stage remains a possible future
+extension rather than an MVP requirement.

docs/adr/0002-use-faster-whisper.md

@@ -2,7 +2,7 @@
 
 ## Status
 
-Accepted
+Superseded by [ADR 0011](0011-use-meeting-lab-mvp-backend.md)
 
 ## Context
 

docs/adr/0003-use-pyannote.md

@@ -2,7 +2,7 @@
 
 ## Status
 
-Accepted
+Accepted, amended by [ADR 0011](0011-use-meeting-lab-mvp-backend.md)
 
 ## Context
 
@@ -23,3 +23,11 @@ Speaker diarization will be treated as a separate pipeline step after transcript
 - Human correction of speaker names should be supported later.
 - The diarization module must hide the concrete engine behind an internal interface.
 - Model name, engine version and timestamp should be stored with every diarization result.
+
+## Amendment
+
+ADR 0011 makes diarization optional for the MVP and selects `pyannote.audio`
+Community-1 as the current preferred backend. CPU execution remains supported,
+with GPU acceleration used when available. Speaker labels remain anonymous
+unless a user explicitly confirms a `SPEAKER_XX` to participant mapping in the
+Meeting Context. Automatic speaker-name inference is not allowed.

docs/adr/0007-recording-archive-policy.md

@@ -2,7 +2,7 @@
 
 ## Status
 
-Accepted
+Accepted, amended by [ADR 0011](0011-use-meeting-lab-mvp-backend.md)
 
 ## Context
 
@@ -18,8 +18,8 @@ removing temporary source files without risking data loss.
 Recordings shall initially be captured in a processing-friendly lossless
 format, normally WAV.
 
-After transcription and diarization have completed, the recording may be
+After transcription and, when enabled, diarization have completed, the
-converted to an archive format.
+recording may be converted to an archive format.
 
 The preferred archive formats are:
 

docs/adr/0009-use-external-recording-for-mvp.md

@@ -2,7 +2,7 @@
 
 ## Status
 
-Accepted
+Accepted, amended by [ADR 0011](0011-use-meeting-lab-mvp-backend.md)
 
 ## Context
 
@@ -13,8 +13,9 @@ OBS Studio already provides stable, configurable and cross-platform audio
 capture. Reimplementing audio capture during the initial project phase would
 add substantial complexity without directly improving transcription quality.
 
-The primary value of the Meeting Assistant lies in transcription, speaker
+The primary value of the Meeting Assistant lies in transcription, optional
-diarization, analysis, structured knowledge extraction and export.
+speaker diarization, protocol generation, review and export. ADR 0011 defines
+the validated processing boundary and current MVP protocol path.
 
 ## Decision
 

docs/adr/0010-development-workflow.md

@@ -0,0 +1,55 @@
+# ADR 0010: Development Workflow
+
+## Status
+
+Accepted
+
+## Context
+
+The project is expected to evolve over an extended period and will be developed with the assistance of AI coding tools.
+
+To maintain code quality, architectural consistency and a comprehensible project history, a common development workflow is required.
+
+## Decision
+
+The project follows a feature-branch based development workflow.
+
+The `main` branch shall always represent a stable and working state.
+
+Development of new functionality shall take place on dedicated feature branches.
+
+Examples:
+
+- feature/domain-model
+- feature/storage
+- feature/import
+- feature/transcription
+- feature/diarization
+- feature/export
+- feature/ui
+
+Feature branches shall be merged into `main` only after:
+
+- implementation is complete
+- documentation has been updated where required
+- all tests pass
+- static analysis completes without errors
+
+Architectural changes shall be documented using Architecture Decision Records (ADRs).
+
+Project versioning follows Semantic Versioning.
+
+Version numbers shall be maintained in:
+
+- `pyproject.toml`
+- `CHANGELOG.md`
+
+Each released version shall be tagged in Git.
+
+
+## Consequences
+
+- Development history remains easy to understand.
+- Experimental work is isolated from the stable branch.
+- Architectural decisions remain documented.
+- Releases are reproducible through Git tags.

docs/adr/0011-use-meeting-lab-mvp-backend.md

@@ -0,0 +1,94 @@
+# ADR 0011: Use Meeting Lab as the MVP Processing Backend
+
+## Status
+
+Accepted
+
+Supersedes ADR 0002 and amends ADRs 0001 and 0003.
+
+## Context
+
+Meeting Lab experiments have validated a practical local pipeline for turning
+a recorded meeting into an editable protocol. Earlier Meeting Assistant plans
+selected `faster-whisper`, treated diarization as required and described a
+separate knowledge-extraction stage as part of the primary pipeline. Work on an
+old feature branch also proposed mandatory fixed five-minute audio chunks and
+chunk-oriented storage.
+
+The validated backend now has a reusable Python API, optional diarization and a
+direct full-transcript protocol path. The product boundary must keep backend
+processing in Meeting Lab and user interaction in Meeting Assistant.
+
+## Decision
+
+Meeting Lab is the reusable processing backend and experimental engine.
+Meeting Assistant is the user-facing application and calls Meeting Lab through
+its Python API rather than launching its CLI as a subprocess.
+
+The MVP processing flow is:
+
+```text
+Source audio
+    -> FFmpeg preparation (mono, 16 kHz PCM WAV)
+    -> whisper.cpp transcription (large-v3-turbo)
+    -> optional pyannote.audio Community-1 diarization
+    -> direct full-transcript protocol generation
+    -> human review and editing
+```
+
+The productive path does not require fixed five-minute audio chunks. Internal
+streaming or segmentation remains an implementation detail of Meeting Lab and
+must not define Meeting Assistant's domain or storage model.
+
+Meeting Assistant integrates with these public Meeting Lab interfaces:
+
+- `MvpMeetingConfig`
+- `MvpRunResult`
+- `run_mvp_meeting(...)`
+- stage-based progress events: `preparing`, `transcription`, `diarization`,
+  `protocol_generation`, `completed` and `failed`
+
+Progress percentages are displayed only when the backend reports real
+measurable progress.
+
+`MeetingContext` is the structured input containing meeting metadata and
+participants. It may contain explicitly confirmed `SPEAKER_XX -> participant_id`
+mappings. Such mappings are authoritative only when confirmed by the user.
+The system must not infer speaker names automatically.
+
+The preferred local engines are:
+
+- `whisper.cpp` with `large-v3-turbo` for transcription
+- `pyannote.audio` Community-1 for optional diarization
+
+GPU acceleration is optional. Vulkan transcription and PyTorch/ROCm
+diarization are validated acceleration paths on North's AMD RX 9070. CPU
+execution remains the compatibility fallback.
+
+The direct full-transcript protocol path is the practical MVP direction.
+`qwen3.8:27B` has produced strong readability and contextual synthesis, while
+`qwen3.6:35B-A3B` has sometimes behaved more conservatively. Dual-model and
+diarization-assisted hard-fact experiments have not established sufficiently
+reliable attribution gains to justify mandatory MVP complexity. Model choice
+remains backend configuration, and human review is required.
+
+Meeting Assistant owns the GUI, structured context editor, participant and
+speaker-mapping controls, progress presentation, protocol editor and export.
+Meeting Lab owns audio preparation, transcription, diarization, orchestration
+and protocol-generation logic.
+
+The product should ultimately provide a detailed contextual protocol and a
+shorter participant/distribution version. The shorter version may follow the
+first GUI milestone.
+
+## Consequences
+
+- ADR 0002's `faster-whisper` selection is no longer current.
+- Fixed-duration chunking and chunk-centric storage are not MVP requirements.
+- Diarization and GPU acceleration remain optional.
+- The Meeting Lab CLI remains useful for command-line operation but is not the
+  application integration boundary.
+- Meeting Context is created through the future GUI; users are not expected to
+  hand-write YAML.
+- Backend experiments can evolve without moving processing logic into the GUI.
+- Protocols remain AI-assisted outputs that require human review.

docs/adr/0012-post-run-corrections.md

@@ -0,0 +1,67 @@
+# ADR 0012: Preserve Corrections as Post-Run Knowledge
+
+## Status
+
+Accepted as a future product direction; not required for the current MVP.
+
+## Context
+
+Real-meeting review exposes corrections that should not require repeating
+expensive processing. These include misspelled or recurring name variants,
+people mentioned but omitted from the initial Meeting Context, and confirmed
+`SPEAKER_XX -> participant_id` mappings. A destructive edit would lose useful
+provenance, while rerunning Whisper or diarization would add cost without
+improving a deterministic correction.
+
+The product is also expected to provide both a detailed contextual protocol and
+a short participant/distribution protocol. Both should eventually use the same
+confirmed context and corrections.
+
+## Decision
+
+Original machine-generated artifacts remain immutable. Corrected or reviewed
+artifacts are separate derivatives. Human corrections should evolve into
+structured, meeting-specific knowledge rather than opaque destructive edits;
+the correction schema is deliberately not defined by this ADR.
+
+An early correction tool may be simple deterministic search and replace, for
+example `Grossman` to `Herr Grossmann`. Applying such a confirmed correction to
+a suitable editable artifact requires neither Whisper, diarization nor an LLM
+call.
+
+A later review stage may run after diarization or the complete initial run. It
+may propose likely person/name matches, allow addition of previously omitted
+mentioned people, and present anonymous speaker mappings for review. Every
+suggestion is non-authoritative: anonymous labels remain anonymous until the
+user explicitly confirms or corrects them, and `mentioned_only` people cannot
+be mapped as speakers.
+
+After confirmation, the product should offer two paths:
+
+1. A fast path applies confirmed speaker, name or text corrections
+   deterministically where that is semantically safe.
+2. A quality path regenerates protocol output using the existing transcription,
+   existing diarization, corrected Meeting Context and confirmed mappings or
+   corrections. It reruns protocol generation only. Whisper and diarization run
+   again only when separately requested or technically necessary.
+
+The likely later workflow is therefore:
+
+```text
+Audio -> transcription -> optional diarization -> initial protocol
+      -> name/person/speaker review -> human confirmation
+      -> deterministic correction or protocol-only regeneration
+      -> final reviewed detailed and/or distribution protocol
+```
+
+The exact ordering may evolve, and this review stage is not mandatory for the
+current Streamlit MVP.
+
+## Consequences
+
+- Human knowledge can improve outputs without unnecessary upstream work.
+- Provenance is retained because originals and corrected derivatives coexist.
+- Correction data can later be reused consistently across detailed and short
+  protocol views.
+- Search/replace, correction storage, review UI, identity suggestions and
+  protocol-only rerun controls remain future implementation work.

docs/alpha-validation.md

@@ -0,0 +1,40 @@
+# Alpha paired validation
+
+Run the complete Assistant suite with `PYTHONPATH=src:<Lab candidate root>`.
+`tests/test_alpha_pair.py` exercises the real adapter, context, orchestration,
+transcript selection and generation persistence with mocked audio preparation,
+Whisper, diarization and model responses. It covers de/en, all performance
+profiles, anonymous generation, mapped regeneration, glossary provenance and
+immutable history. The test skips when Lab is unavailable; a release validation
+must run it with Lab available and report no skips.
+
+Run Lab's full `python -m unittest discover -s tests` at its candidate revision.
+History tests inject write/publication failures and a process interruption.
+UI tests use the real executor and reject worker access to session state.
+
+These tests do not measure recognition quality or model language compliance.
+Before tagging, perform the final local GUI/model smoke with the exact paired
+revisions and configured model/runtime. Reuse copied historical transcripts
+where possible; do not rewrite original regression evidence. Preserve the known
+GTM-Hub human-reference whitespace. Record both candidate SHAs in release notes.
+
+## Historical Lab fixture dependency
+
+Four older experiment tests require six ignored files, absent from a fresh Git
+worktree. Do not add them to the alpha source boundary or silently skip the tests:
+
+- `artifacts/experiments/evidence_observations_v3/20260819_v3_single_run/h_resulting_action/parsed_observations.json`
+- `artifacts/experiments/negative_act_form_v0/20260820_qwen35_9b_single_run/na-01/v3_style_input_observations.json`
+- The same negative-act filename under `na-03`, `na-04`, `na-05`, and `na-06`.
+
+For the alpha audit, these files were copied from the original Lab worktree to a
+separate temporary test directory. That directory exposed the candidate's
+`tests`, `prompts`, `samples`, `src`, and `scripts` as resource links. The full
+suite ran there with the candidate Lab root on `PYTHONPATH` and the candidate's
+absolute tests directory supplied to unittest discovery. All 413 tests passed.
+The bare candidate worktree run instead reports four missing-fixture errors.
+This is a historical test portability limitation, not a runtime dependency.
+
+The paired Assistant suite passed all 107 tests with the candidate Lab backend;
+no Whisper, diarization or Ollama inference was executed. These counts describe
+the preparation validation and do not replace the final smoke-test record.

docs/architecture.md

@@ -2,254 +2,159 @@
 
 ## Purpose
 
-The Meeting Knowledge Assistant transforms recorded meetings into structured organizational knowledge through a modular processing pipeline.
+Meeting Assistant is the user-facing application for preparing meeting
+context, running the Meeting Lab pipeline and reviewing its results. Meeting
+Lab is the reusable processing backend and experimental engine.
 
----
+## System Boundary
-
-# Design Principles
-
-- Architecture first
-- Offline first where practical
-- Immutable source data
-- Replaceable AI components
-- Small, focused modules
-- Explicit interfaces
-- Reproducible AI outputs
-- External recording and internal processing are separate responsibilities.
-- The processing pipeline must not depend on OBS-specific metadata or behavior.
-- Imported recordings must be treated like recordings from any other supported source.
-
----
-
-## High-Level Pipeline
-
-External Recorder
-    ↓
-Audio Import
-    ↓
-Recording Validation
-    ↓
-Meeting Storage
-    ↓
-Transcription
-    ↓
-Speaker Diarization
-    ↓
-Working Transcript
-    ↓
-LLM Analysis
-    ↓
-Knowledge Extraction
-    ↓
-Export
-
----
-
-## Core Pipeline
-
-External Recording
-
-↓
-
-Audio Import and Validation
-
-↓
-
-Transcription
-
-↓
-
-Speaker Diarization
-
-↓
-
-Working Transcript
-
-↓
-
-LLM Analysis
-
-↓
-
-Knowledge Extraction
-
-↓
-
-Storage and Export
-
-# Domain Model
-
-Meeting
-│
-├── Recording
-├── Transcript
-│     ├── Canonical
-│     └── Working
-├── Speakers
-├── Participants
-├── AI Artifacts
-├── Knowledge Objects
-├── Attachments
-└── Exports
-
----
-
-# Module Responsibilities
-
-## Input and Ingest
-
-Responsible for importing existing recordings into the application.
-
-Initial reference source:
-
-- OBS Studio
-
-Initial supported formats:
-
-- WAV
-
-- FLAC
-
-Responsibilities:
-
-- validate the input file
-
-- collect technical metadata
-
-- calculate checksums
-
-- copy or move the recording into the meeting directory
-
-- create the initial Recording domain object
-
-The ingest component must not perform transcription or modify the audio content.
-
-## Recorder
-
-The recorder module is reserved for a future integrated recording implementation.
-
-It is not required for the MVP.
-
-The initial application workflow uses externally created recordings, with OBS
-Studio as the recommended reference recorder.
-
----
-
-## Transcription
-
-Responsible only for speech-to-text conversion.
-
-Input:
-Recording
-
-Output:
-Canonical Transcript
-
----
-
-## Diarization
-
-Responsible only for speaker identification.
-
-Input:
-Recording + Canonical Transcript
-
-Output:
-Working Transcript
-
----
-
-## LLM
-
-Responsible for semantic analysis.
-
-Input:
-Transcript
-
-Output:
-AI Artifacts
-
----
-
-## Knowledge Extraction
-
-Responsible for creating structured knowledge.
-
-Input:
-AI Artifacts
-
-Output:
-Knowledge Objects
-
----
-
-## Export
-
-Responsible for creating user-facing documents.
-
-Input:
-Knowledge Objects
-
-Output:
-Markdown
-PDF
-DOCX
-
----
-
-## Artifact Lifecycle
 
 ```text
-Imported Recording
+Meeting Assistant                         Meeting Lab
-    ↓
+-----------------                         -----------
-Validated Source Recording
+Audio selection                  ------>  Audio preparation
-    ↓
+Meeting Context editor           ------>  Transcription
-Canonical Transcript
+Participant management           ------>  Optional diarization
-    ↓
+Explicit speaker mapping         ------>  Protocol generation
-Working Transcript
+Progress presentation            <------  Progress events
-    ↓
+Protocol editor and export       <------  Run result and artifacts
-AI Artifacts
+```
-    ↓
-Knowledge Objects
-    ↓
-Exports
 
----
+Meeting Assistant calls the Meeting Lab Python API directly. The Meeting Lab
+CLI is a thin adapter over that API and must not be launched as an application
+subprocess.
 
-## Initial Recording Strategy
+## MVP Processing Flow
 
-The MVP does not implement platform-specific audio capture.
+```text
+Source audio
+    -> FFmpeg preparation (normalization optional, default on)
+    -> mono, 16 kHz PCM WAV
+    -> whisper.cpp transcription with large-v3-turbo
+    -> optional pyannote.audio Community-1 diarization
+    -> direct full-transcript protocol generation
+    -> human review and editing
+    -> export
+```
 
-OBS Studio is the recommended reference recorder for online meetings.
+The MVP does not require fixed five-minute audio chunks, semantic chunking, a
+separate evidence-extraction pipeline or multiple LLMs. Any internal
+segmentation remains a Meeting Lab implementation detail.
 
-The application initially processes existing WAV or FLAC recordings. Integrated
+## Backend Integration
 
-recording remains a future extension and must not be required by transcription,
+The reusable Meeting Lab interface consists of:
 
-diarization or analysis modules.
+- `MvpMeetingConfig`, the run configuration
+- `run_mvp_meeting(...)`, the orchestration entry point
+- `MvpRunResult`, the completed run result
+- stage-based progress events
 
----
+The known stages are:
 
-# Storage Strategy
+```text
+preparing
+transcription
+diarization
+protocol_generation
+completed
+failed
+```
 
-The domain model is independent of the storage backend.
+The GUI shows the current stage. It shows a percentage only when the event
+contains real measurable progress; stage changes must not be presented as
+invented percentages.
 
-Possible implementations:
+## Meeting Context
 
-- File System
+`MeetingContext` is structured domain input rather than an informal prompt or a
-- SQLite
+file users must author manually. It contains meeting metadata and participants
-- PostgreSQL
+and can include optional mappings:
-- Cloud Storage
 
----
+```text
+SPEAKER_XX -> participant_id
+```
 
-# Future Extensions
+Mappings are authoritative only after explicit user confirmation. Diarization
+labels otherwise remain anonymous, and the application must not infer speaker
+names automatically. The Meeting Assistant GUI owns creation and editing of
+this context and its mappings.
 
-- Live transcription
+## Processing Components
-- Video processing
+
-- OCR
+### Audio Preparation
-- Semantic search
+
-- Knowledge graph
+Meeting Lab uses FFmpeg to prepare a consistent local-processing input. The
-- Company glossary
+current practical target is mono, 16 kHz PCM WAV. The imported source remains a
-- Multi-language meetings
+separate source artifact. Preparation always runs for WAV, FLAC and M4A,
-- Local LLM support
+regardless of the normalization switch. When enabled, Meeting Lab currently
+uses `loudnorm=I=-16:LRA=11:TP=-1.5`, an isolated conservative default for
+speech recordings that may be revisited after empirical comparison. Meeting
+Assistant passes only an on/off choice and does not own filter parameters.
+
+### Transcription
+
+`whisper.cpp` with `large-v3-turbo` is the preferred local backend. Vulkan is a
+validated acceleration path on North's AMD RX 9070, while CPU execution remains
+a compatibility fallback. Engine and model details should be retained with the
+result for traceability.
+
+### Diarization
+
+`pyannote.audio` Community-1 is preferred when diarization is enabled. It can
+run on CPU and may use GPU acceleration such as PyTorch/ROCm where available.
+Diarization is optional and its anonymous labels do not modify the canonical
+transcript or assert participant identity.
+
+### Protocol Generation
+
+The direct full-transcript path is the practical MVP. Current model experiments
+favor `qwen3.8:27B` for readability and contextual synthesis, while
+`qwen3.6:35B-A3B` has shown more conservative behavior in some areas. Neither a
+specific dual-model arrangement nor an evidence pipeline is an application
+requirement. Generated protocols require human review.
+
+## Application Responsibilities
+
+The first GUI milestone provides:
+
+- audio-file selection
+- structured Meeting Context and participant editing
+- optional explicit speaker mapping
+- pipeline start and configuration
+- progress and failure presentation
+- detailed protocol display and editing
+- export of the reviewed result
+
+The product direction also includes a shorter participant/distribution
+protocol. It may be delivered after the first GUI milestone.
+
+## Artifact and Storage Principles
+
+The existing Meeting domain remains the application-level container for source
+recordings, canonical and working transcripts, context, generated artifacts and
+exports. Storage remains independent of processing segmentation. In particular,
+chunks are not primary Meeting Assistant domain objects or the required unit of
+MVP persistence.
+
+Source audio and the canonical transcription output are preserved. Corrections
+and reviewed protocols are derived versions. Generated artifacts should retain
+their input version, backend/model configuration, prompt version and timestamp
+where practical.
+
+## Later Post-Run Correction Flow
+
+Post-run corrections are planned as a separate, non-mandatory workflow after
+initial protocol generation. As detailed in [ADR 0012](adr/0012-post-run-corrections.md),
+confirmed name, person and anonymous-speaker corrections should become
+meeting-specific knowledge. They may then be applied deterministically to safe
+derived artifacts or used for protocol-only regeneration without needlessly
+rerunning transcription or diarization.
+
+## Future Extensions
+
+- shorter distribution protocols
+- integrated recording
+- searchable meeting history and optional database indexes
+- live transcription
+- OCR and video processing
+- semantic search and organizational knowledge features

docs/glossary.md

@@ -2,7 +2,23 @@ Transcript
 Original speech-to-text output.
 
 Diarization
-Assignment of transcript segments to speakers.
+Assignment of transcript segments to anonymous speaker labels. It does not by
+itself identify participants.
+
+Meeting Context
+Structured meeting metadata, participants and optional explicitly confirmed
+speaker-to-participant mappings supplied to the processing pipeline.
+
+Speaker Mapping
+An explicitly confirmed association from a diarization label such as
+`SPEAKER_00` to a participant ID. It must not be inferred automatically.
+
+Detailed Protocol
+Contextual protocol generated from the full transcript for human review and
+continued work.
+
+Distribution Protocol
+Shorter, participant-facing version of a reviewed meeting protocol.
 
 Knowledge Object
 Structured information extracted from meetings.

docs/releases/v0.1.0-alpha.1.md

@@ -0,0 +1,39 @@
+# Meeting Assistant — First functional alpha
+
+Version: `0.1.0a1`
+Tag: `v0.1.0-alpha.1`
+
+This is the first functional end-to-end alpha of the local Meeting Assistant.
+It provides local meeting configuration and audio workflow, FFmpeg preparation
+and normalization, Whisper transcription, optional diarization, explicit
+speaker mapping, anonymous-speaker protocol generation, mapped-speaker
+regeneration, German and English meeting-language handling, glossary handling
+with YAML interchange, glossary provenance without transcript mutation,
+configurable protocol performance profiles, processing timings, generation
+history and provenance, and atomic publication of complete successful protocol
+generations. The GTM-Hub qualitative regression case is included.
+
+The validated candidate revisions for this release were:
+
+- Assistant: `b50b87c32b3941310c719f459fd45faa87658b68`
+- Meeting Lab: `d2e3636b949280402728308022c99bdee6ea1066`
+
+The immutable release revisions are the commits targeted by the paired
+`v0.1.0-alpha.1` tags in the two repositories.
+
+## Accepted alpha limitations
+
+- A local runtime, model, and container setup is required.
+- Generated protocols require human review, especially responsibility and
+  action attribution; speaker identity requires explicit confirmation.
+- Diarization can be imperfect. The final native smoke environment did not
+  contain `pyannote.audio==4.0.7` and PyTorch, so real diarization and mapped
+  speaker regeneration were not rerun there; automated tests cover these paths.
+- Real German and English `qwen3.8:27b` protocol generation was validated.
+- Context limits can cause attribution fallback or oversized-input rejection.
+- Historical ignored Lab fixtures are required by some old experiment tests and
+  are not automatically present in a completely fresh Lab worktree.
+- Generation publication assumes a local POSIX filesystem; power-loss
+  durability is not guaranteed.
+- Visual redesign is post-alpha, and concise distribution-protocol rendering
+  remains future work.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "meeting-assistant"
-version = "0.1.0"
+version = "0.1.0a1"
 description = "Local-first meeting transcription and knowledge capture assistant"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -12,7 +12,9 @@ authors = [
     { name = "Martin Tazl" }
 ]
 dependencies = [
-    "pydantic>=2,<3"
+    "pydantic>=2,<3",
+    "PyYAML>=6,<7",
+    "streamlit>=1.40,<2",
 ]
 
 [project.optional-dependencies]

run_north.sh

@@ -0,0 +1,57 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+assistant_root="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd -P)"
+assistant_src="$assistant_root/src"
+lab_root=/opt/git-projekts/meeting-lab
+north_venv=/opt/git-projekts/meeting-assistant/.venv
+whisper_executable=/home/martin/whisper.cpp/whisper.cpp/build/bin/whisper-cli
+whisper_model=/home/martin/whisper.cpp/whisper.cpp/models/ggml-large-v3-turbo.bin
+
+require_directory() {
+    if [[ ! -d "$1" ]]; then
+        printf 'Required directory is missing: %s\n' "$1" >&2
+        exit 1
+    fi
+}
+
+require_file() {
+    if [[ ! -f "$1" ]]; then
+        printf 'Required file is missing: %s\n' "$1" >&2
+        exit 1
+    fi
+}
+
+require_executable() {
+    if [[ ! -x "$1" ]]; then
+        printf 'Required executable is missing or not executable: %s\n' "$1" >&2
+        exit 1
+    fi
+}
+
+require_directory "$assistant_src"
+require_file "$assistant_src/mka/ui/streamlit_app.py"
+require_directory "$lab_root"
+require_file "$lab_root/src/meeting_lab/__init__.py"
+require_directory "$north_venv"
+require_executable "$north_venv/bin/streamlit"
+require_executable "$whisper_executable"
+require_file "$whisper_model"
+if ! command -v docker >/dev/null 2>&1; then
+    printf 'Required executable is not on PATH: docker\n' >&2
+    exit 1
+fi
+
+export MKA_WHISPER_EXECUTABLE="$whisper_executable"
+export MKA_WHISPER_MODEL="$whisper_model"
+export MKA_PROTOCOL_MODEL=qwen3.8:27b
+export MKA_OLLAMA_ENDPOINT=http://127.0.0.1:11434
+export MKA_DIARIZATION_MODE=auto
+export MKA_DIARIZATION_RUNTIME=container
+export MKA_DIARIZATION_CONTAINER_IMAGE=rocm/pytorch:rocm7.2.1_ubuntu24.04_py3.12_pytorch_release_2.9.1
+export MKA_DIARIZATION_CONTAINER_ARGS='["--device=/dev/kfd","--device=/dev/dri","--group-add","video"]'
+
+# Keep feature sources ahead of the reused venv's editable installations.
+export PYTHONPATH="$assistant_src:$lab_root"
+
+exec "$north_venv/bin/streamlit" run "$assistant_src/mka/ui/streamlit_app.py"

src/mka/application/__init__.py

@@ -0,0 +1,17 @@
+"""Application services for Meeting Assistant workflows."""
+
+from mka.application.meeting_service import (
+    MeetingDetails,
+    MeetingProcessingService,
+    ParticipantInput,
+    ProcessingOptions,
+    ProcessingOutcome,
+)
+
+__all__ = [
+    "MeetingDetails",
+    "MeetingProcessingService",
+    "ParticipantInput",
+    "ProcessingOptions",
+    "ProcessingOutcome",
+]

src/mka/application/config.py

@@ -0,0 +1,106 @@
+"""Environment-backed configuration for the Meeting Assistant application."""
+
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+
+class ConfigurationError(ValueError):
+    """Raised when required processing configuration is unavailable."""
+
+
+@dataclass(frozen=True)
+class AppSettings:
+    """Machine-specific Meeting Lab settings kept outside the UI."""
+
+    data_root: Path
+    whisper_model: Path | None
+    glossary_database: Path = Path("data/database/glossary.sqlite3")
+    whisper_executable: str = "whisper-cli"
+    ffmpeg_executable: str = "ffmpeg"
+    protocol_model: str = "qwen3.8:27b"
+    ollama_endpoint: str = "http://127.0.0.1:11434"
+    protocol_num_ctx: int = 32_768
+    protocol_safe_input_token_budget: int = 29_000
+    language: str = "de"
+    threads: str | int = "auto"
+    diarization_mode: str = "auto"
+    diarization_runtime: str = "native"
+    diarization_container_image: str | None = None
+    diarization_container_args: tuple[str, ...] = ()
+
+    @classmethod
+    def from_environment(cls) -> AppSettings:
+        """Load settings from MKA_* environment variables."""
+        whisper_model = os.getenv("MKA_WHISPER_MODEL")
+        threads = os.getenv("MKA_WHISPER_THREADS", "auto")
+        parsed_threads: str | int = int(threads) if threads.isdigit() else threads
+        return cls(
+            data_root=Path(os.getenv("MKA_DATA_ROOT", "data/meetings")),
+            whisper_model=Path(whisper_model) if whisper_model else None,
+            glossary_database=Path(
+                os.getenv("MKA_GLOSSARY_DATABASE", "data/database/glossary.sqlite3")
+            ),
+            whisper_executable=os.getenv("MKA_WHISPER_EXECUTABLE", "whisper-cli"),
+            ffmpeg_executable=os.getenv("MKA_FFMPEG_EXECUTABLE", "ffmpeg"),
+            protocol_model=os.getenv("MKA_PROTOCOL_MODEL", "qwen3.8:27b"),
+            ollama_endpoint=os.getenv("MKA_OLLAMA_ENDPOINT", "http://127.0.0.1:11434"),
+            language=os.getenv("MKA_LANGUAGE", "de"),
+            threads=parsed_threads,
+            diarization_mode=os.getenv("MKA_DIARIZATION_MODE", "auto"),
+            diarization_runtime=os.getenv("MKA_DIARIZATION_RUNTIME", "native"),
+            diarization_container_image=os.getenv("MKA_DIARIZATION_CONTAINER_IMAGE"),
+            diarization_container_args=_parse_container_args(
+                os.getenv("MKA_DIARIZATION_CONTAINER_ARGS")
+            ),
+        )
+
+    def validate_for_processing(self) -> None:
+        """Validate settings needed before a Meeting Lab run starts."""
+        if self.whisper_model is None:
+            raise ConfigurationError("MKA_WHISPER_MODEL is not configured.")
+        if not self.whisper_model.is_file():
+            raise ConfigurationError(
+                f"Configured Whisper model does not exist: {self.whisper_model}"
+            )
+        if not self.whisper_executable.strip():
+            raise ConfigurationError("MKA_WHISPER_EXECUTABLE must not be empty.")
+        if not self.ffmpeg_executable.strip():
+            raise ConfigurationError("MKA_FFMPEG_EXECUTABLE must not be empty.")
+        if self.diarization_mode not in {"auto", "cpu", "gpu"}:
+            raise ConfigurationError("MKA_DIARIZATION_MODE must be one of: auto, cpu, gpu.")
+        if self.diarization_runtime not in {"native", "container"}:
+            raise ConfigurationError("MKA_DIARIZATION_RUNTIME must be native or container.")
+        if self.diarization_runtime == "container" and not self.diarization_container_image:
+            raise ConfigurationError(
+                "MKA_DIARIZATION_CONTAINER_IMAGE is required for container runtime."
+            )
+        if any(
+            not isinstance(argument, str) or not argument
+            for argument in self.diarization_container_args
+        ):
+            raise ConfigurationError(
+                "MKA_DIARIZATION_CONTAINER_ARGS must contain only non-empty strings."
+            )
+
+
+def _parse_container_args(value: str | None) -> tuple[str, ...]:
+    """Parse an ordered JSON array of opaque container command arguments."""
+    if value is None or not value.strip():
+        return ()
+    try:
+        parsed = json.loads(value)
+    except json.JSONDecodeError as exc:
+        raise ConfigurationError(
+            "MKA_DIARIZATION_CONTAINER_ARGS must be a JSON array of strings."
+        ) from exc
+    if not isinstance(parsed, list) or any(
+        not isinstance(argument, str) or not argument for argument in parsed
+    ):
+        raise ConfigurationError(
+            "MKA_DIARIZATION_CONTAINER_ARGS must be a JSON array of non-empty strings."
+        )
+    return tuple(parsed)

src/mka/application/glossary.py

@@ -0,0 +1,369 @@
+"""SQLite-backed global terminology glossary."""
+
+from __future__ import annotations
+
+import sqlite3
+from collections.abc import Iterable, Iterator
+from contextlib import contextmanager
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+GLOSSARY_CATEGORIES = (
+    "product",
+    "material",
+    "organization",
+    "technical_term",
+    "acronym",
+    "other",
+)
+
+
+class GlossaryConflictError(ValueError):
+    """Raised when a canonical term or alias conflicts with existing terminology."""
+
+
+@dataclass(frozen=True)
+class GlossaryEntry:
+    id: int
+    canonical_term: str
+    category: str
+    description: str | None
+    is_active: bool
+    aliases: tuple[str, ...]
+    created_at: str
+    updated_at: str
+
+
+@dataclass(frozen=True)
+class GlossaryReplacementEntry:
+    """Validated values used to atomically replace the persisted glossary."""
+
+    id: int
+    canonical_term: str
+    category: str
+    description: str | None
+    is_active: bool
+    aliases: tuple[str, ...]
+
+
+class GlossaryRepository:
+    """Small data-access boundary for the local glossary database."""
+
+    def __init__(self, database_path: Path) -> None:
+        self.database_path = Path(database_path)
+
+    def initialize(self) -> None:
+        """Create the database and current schema when absent."""
+        self.database_path.parent.mkdir(parents=True, exist_ok=True)
+        with self._connect() as connection:
+            connection.executescript(
+                """
+                CREATE TABLE IF NOT EXISTS glossary_entries (
+                    id INTEGER PRIMARY KEY,
+                    canonical_term TEXT NOT NULL COLLATE NOCASE UNIQUE,
+                    category TEXT NOT NULL CHECK (category IN (
+                        'product', 'material', 'organization',
+                        'technical_term', 'acronym', 'other'
+                    )),
+                    description TEXT,
+                    is_active INTEGER NOT NULL DEFAULT 1 CHECK (is_active IN (0, 1)),
+                    created_at TEXT NOT NULL,
+                    updated_at TEXT NOT NULL
+                );
+                CREATE TABLE IF NOT EXISTS glossary_aliases (
+                    id INTEGER PRIMARY KEY,
+                    entry_id INTEGER NOT NULL REFERENCES glossary_entries(id)
+                        ON DELETE CASCADE,
+                    alias TEXT NOT NULL COLLATE NOCASE UNIQUE,
+                    created_at TEXT NOT NULL
+                );
+                CREATE INDEX IF NOT EXISTS idx_glossary_aliases_entry_id
+                    ON glossary_aliases(entry_id);
+                PRAGMA user_version = 1;
+                """
+            )
+
+    def create(
+        self,
+        canonical_term: str,
+        category: str,
+        *,
+        aliases: Iterable[str] = (),
+        description: str | None = None,
+        is_active: bool = True,
+    ) -> GlossaryEntry:
+        canonical, normalized_aliases = self._validate_values(canonical_term, category, aliases)
+        now = _timestamp()
+        try:
+            with self._connect() as connection:
+                self._ensure_terms_available(connection, canonical, normalized_aliases)
+                cursor = connection.execute(
+                    """INSERT INTO glossary_entries
+                    (canonical_term, category, description, is_active, created_at, updated_at)
+                    VALUES (?, ?, ?, ?, ?, ?)""",
+                    (canonical, category, _optional_text(description), is_active, now, now),
+                )
+                entry_id = int(cursor.lastrowid)
+                connection.executemany(
+                    "INSERT INTO glossary_aliases (entry_id, alias, created_at) VALUES (?, ?, ?)",
+                    ((entry_id, alias, now) for alias in normalized_aliases),
+                )
+        except sqlite3.IntegrityError as exc:
+            raise GlossaryConflictError("Canonical term or alias already exists.") from exc
+        return self.get(entry_id)
+
+    def get(self, entry_id: int) -> GlossaryEntry:
+        with self._connect() as connection:
+            row = connection.execute(
+                "SELECT * FROM glossary_entries WHERE id = ?", (entry_id,)
+            ).fetchone()
+            if row is None:
+                raise KeyError(f"Unknown glossary entry: {entry_id}")
+            return self._to_entry(connection, row)
+
+    def list(self, search: str = "", *, active_only: bool = False) -> list[GlossaryEntry]:
+        clauses: list[str] = []
+        parameters: list[object] = []
+        if active_only:
+            clauses.append("entry.is_active = 1")
+        if search.strip():
+            clauses.append(
+                "(entry.canonical_term LIKE ? COLLATE NOCASE "
+                "OR entry.category LIKE ? COLLATE NOCASE "
+                "OR entry.description LIKE ? COLLATE NOCASE "
+                "OR alias.alias LIKE ? COLLATE NOCASE)"
+            )
+            pattern = f"%{search.strip()}%"
+            parameters.extend([pattern] * 4)
+        where = f"WHERE {' AND '.join(clauses)}" if clauses else ""
+        with self._connect() as connection:
+            rows = connection.execute(
+                f"""SELECT DISTINCT entry.* FROM glossary_entries AS entry
+                LEFT JOIN glossary_aliases AS alias ON alias.entry_id = entry.id
+                {where} ORDER BY entry.canonical_term COLLATE NOCASE""",  # noqa: S608
+                parameters,
+            ).fetchall()
+            return [self._to_entry(connection, row) for row in rows]
+
+    def update(
+        self,
+        entry_id: int,
+        canonical_term: str,
+        category: str,
+        *,
+        aliases: Iterable[str] = (),
+        description: str | None = None,
+        is_active: bool = True,
+    ) -> GlossaryEntry:
+        canonical, normalized_aliases = self._validate_values(canonical_term, category, aliases)
+        try:
+            with self._connect() as connection:
+                exists = connection.execute(
+                    "SELECT 1 FROM glossary_entries WHERE id = ?", (entry_id,)
+                ).fetchone()
+                if exists is None:
+                    raise KeyError(f"Unknown glossary entry: {entry_id}")
+                self._ensure_terms_available(
+                    connection, canonical, normalized_aliases, excluding_entry_id=entry_id
+                )
+                now = _timestamp()
+                connection.execute(
+                    """UPDATE glossary_entries SET canonical_term = ?, category = ?,
+                    description = ?, is_active = ?, updated_at = ? WHERE id = ?""",
+                    (
+                        canonical,
+                        category,
+                        _optional_text(description),
+                        is_active,
+                        now,
+                        entry_id,
+                    ),
+                )
+                connection.execute("DELETE FROM glossary_aliases WHERE entry_id = ?", (entry_id,))
+                connection.executemany(
+                    "INSERT INTO glossary_aliases (entry_id, alias, created_at) VALUES (?, ?, ?)",
+                    ((entry_id, alias, now) for alias in normalized_aliases),
+                )
+        except sqlite3.IntegrityError as exc:
+            raise GlossaryConflictError("Canonical term or alias already exists.") from exc
+        return self.get(entry_id)
+
+    def set_active(self, entry_id: int, is_active: bool) -> GlossaryEntry:
+        entry = self.get(entry_id)
+        return self.update(
+            entry.id,
+            entry.canonical_term,
+            entry.category,
+            aliases=entry.aliases,
+            description=entry.description,
+            is_active=is_active,
+        )
+
+    def delete(self, entry_id: int) -> None:
+        with self._connect() as connection:
+            cursor = connection.execute("DELETE FROM glossary_entries WHERE id = ?", (entry_id,))
+            if cursor.rowcount == 0:
+                raise KeyError(f"Unknown glossary entry: {entry_id}")
+
+    def replace_all(self, entries: Iterable[GlossaryReplacementEntry]) -> None:
+        """Atomically replace every glossary entry, preserving supplied stable IDs."""
+        replacements = tuple(entries)
+        validated: list[GlossaryReplacementEntry] = []
+        seen_ids: set[int] = set()
+        seen_terms: set[str] = set()
+        for entry in replacements:
+            if type(entry.id) is not int or entry.id <= 0:
+                raise ValueError("Glossary entry IDs must be positive integers.")
+            if entry.id in seen_ids:
+                raise GlossaryConflictError(f"Duplicate glossary entry ID: {entry.id}.")
+            canonical, aliases = self._validate_values(
+                entry.canonical_term, entry.category, entry.aliases
+            )
+            folded_terms = {canonical.casefold(), *(alias.casefold() for alias in aliases)}
+            if seen_terms.intersection(folded_terms):
+                raise GlossaryConflictError("Canonical term or alias already exists.")
+            seen_ids.add(entry.id)
+            seen_terms.update(folded_terms)
+            validated.append(
+                GlossaryReplacementEntry(
+                    id=entry.id,
+                    canonical_term=canonical,
+                    category=entry.category,
+                    description=_optional_text(entry.description),
+                    is_active=entry.is_active,
+                    aliases=aliases,
+                )
+            )
+
+        now = _timestamp()
+        try:
+            with self._connect() as connection:
+                connection.execute("DELETE FROM glossary_entries")
+                connection.executemany(
+                    """INSERT INTO glossary_entries
+                    (id, canonical_term, category, description, is_active, created_at, updated_at)
+                    VALUES (?, ?, ?, ?, ?, ?, ?)""",
+                    (
+                        (
+                            entry.id,
+                            entry.canonical_term,
+                            entry.category,
+                            entry.description,
+                            entry.is_active,
+                            now,
+                            now,
+                        )
+                        for entry in validated
+                    ),
+                )
+                connection.executemany(
+                    "INSERT INTO glossary_aliases (entry_id, alias, created_at) VALUES (?, ?, ?)",
+                    ((entry.id, alias, now) for entry in validated for alias in entry.aliases),
+                )
+        except sqlite3.IntegrityError as exc:
+            raise GlossaryConflictError("Canonical term or alias already exists.") from exc
+
+    @contextmanager
+    def _connect(self) -> Iterator[sqlite3.Connection]:
+        connection = sqlite3.connect(self.database_path, timeout=5)
+        try:
+            connection.row_factory = sqlite3.Row
+            connection.execute("PRAGMA foreign_keys = ON")
+            connection.execute("PRAGMA journal_mode = WAL")
+            connection.execute("PRAGMA busy_timeout = 5000")
+            with connection:
+                yield connection
+        finally:
+            connection.close()
+
+    @staticmethod
+    def _validate_values(
+        canonical_term: str, category: str, aliases: Iterable[str]
+    ) -> tuple[str, tuple[str, ...]]:
+        canonical = canonical_term.strip()
+        if not canonical:
+            raise ValueError("Canonical term is required.")
+        if category not in GLOSSARY_CATEGORIES:
+            raise ValueError(f"Unsupported glossary category: {category}")
+        normalized_aliases = tuple(
+            dict.fromkeys(alias.strip() for alias in aliases if alias.strip())
+        )
+        folded = [alias.casefold() for alias in normalized_aliases]
+        if len(folded) != len(set(folded)) or canonical.casefold() in folded:
+            raise GlossaryConflictError(
+                "Aliases must be unique and differ from the canonical term."
+            )
+        return canonical, normalized_aliases
+
+    @staticmethod
+    def _ensure_terms_available(
+        connection: sqlite3.Connection,
+        canonical: str,
+        aliases: tuple[str, ...],
+        *,
+        excluding_entry_id: int | None = None,
+    ) -> None:
+        terms = (canonical, *aliases)
+        placeholders = ", ".join("?" for _ in terms)
+        exclusion = "AND entry_id != ?" if excluding_entry_id is not None else ""
+        alias_parameters: list[object] = [*terms]
+        if excluding_entry_id is not None:
+            alias_parameters.append(excluding_entry_id)
+        alias_conflict = connection.execute(
+            f"SELECT 1 FROM glossary_aliases WHERE alias IN ({placeholders}) {exclusion} LIMIT 1",  # noqa: S608
+            alias_parameters,
+        ).fetchone()
+        entry_exclusion = "AND id != ?" if excluding_entry_id is not None else ""
+        entry_parameters: list[object] = [*terms]
+        if excluding_entry_id is not None:
+            entry_parameters.append(excluding_entry_id)
+        canonical_conflict = connection.execute(
+            f"SELECT 1 FROM glossary_entries WHERE canonical_term IN ({placeholders}) "  # noqa: S608
+            f"{entry_exclusion} LIMIT 1",
+            entry_parameters,
+        ).fetchone()
+        if alias_conflict or canonical_conflict:
+            raise GlossaryConflictError("Canonical term or alias already exists.")
+
+    @staticmethod
+    def _to_entry(connection: sqlite3.Connection, row: sqlite3.Row) -> GlossaryEntry:
+        aliases = connection.execute(
+            "SELECT alias FROM glossary_aliases WHERE entry_id = ? ORDER BY alias COLLATE NOCASE",
+            (row["id"],),
+        ).fetchall()
+        return GlossaryEntry(
+            id=row["id"],
+            canonical_term=row["canonical_term"],
+            category=row["category"],
+            description=row["description"],
+            is_active=bool(row["is_active"]),
+            aliases=tuple(alias["alias"] for alias in aliases),
+            created_at=row["created_at"],
+            updated_at=row["updated_at"],
+        )
+
+
+def render_glossary_terms(entries: Iterable[GlossaryEntry]) -> list[str]:
+    """Render concise canonical terms with recognition aliases for Meeting Context."""
+    rendered = []
+    for entry in entries:
+        item = entry.canonical_term
+        if entry.aliases:
+            item += f" (aliases: {', '.join(entry.aliases)})"
+        rendered.append(item)
+    return rendered
+
+
+def glossary_alias_mapping(entries: Iterable[GlossaryEntry]) -> dict[str, str]:
+    """Return explicit alias configuration for provenance, not substitution."""
+    return {alias: entry.canonical_term for entry in entries for alias in entry.aliases}
+
+
+def _timestamp() -> str:
+    return datetime.now(UTC).isoformat(timespec="seconds")
+
+
+def _optional_text(value: str | None) -> str | None:
+    stripped = value.strip() if value else ""
+    return stripped or None

src/mka/application/glossary_yaml.py

@@ -0,0 +1,122 @@
+"""Versioned UTF-8 YAML import and export for the terminology glossary."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+import yaml
+
+from mka.application.glossary import (
+    GLOSSARY_CATEGORIES,
+    GlossaryEntry,
+    GlossaryReplacementEntry,
+)
+
+GLOSSARY_YAML_VERSION = 1
+
+
+class GlossaryYamlError(ValueError):
+    """Raised when a glossary YAML document is malformed or invalid."""
+
+
+def export_glossary_yaml(entries: Sequence[GlossaryEntry]) -> str:
+    """Serialize all glossary state in a deterministic, versioned document."""
+    document = {
+        "version": GLOSSARY_YAML_VERSION,
+        "glossary": [
+            {
+                "id": entry.id,
+                "canonical_term": entry.canonical_term,
+                "aliases": list(entry.aliases),
+                "category": entry.category,
+                "description": entry.description,
+                "active": entry.is_active,
+            }
+            for entry in entries
+        ],
+    }
+    return yaml.safe_dump(document, allow_unicode=True, sort_keys=False, default_flow_style=False)
+
+
+def import_glossary_yaml(content: str | bytes) -> list[GlossaryReplacementEntry]:
+    """Parse and completely validate a glossary replacement document."""
+    try:
+        if isinstance(content, bytes):
+            content = content.decode("utf-8")
+        document = yaml.safe_load(content)
+    except (yaml.YAMLError, UnicodeDecodeError) as exc:
+        raise GlossaryYamlError(f"Malformed glossary YAML: {exc}") from exc
+
+    if not isinstance(document, dict):
+        raise GlossaryYamlError("Glossary YAML must contain a top-level mapping.")
+    version = document.get("version")
+    if type(version) is not int or version != GLOSSARY_YAML_VERSION:
+        raise GlossaryYamlError(
+            f"Unsupported glossary YAML version {version!r}; expected {GLOSSARY_YAML_VERSION}."
+        )
+    raw_entries = document.get("glossary")
+    if not isinstance(raw_entries, list):
+        raise GlossaryYamlError("Glossary YAML must contain a top-level 'glossary' list.")
+
+    result: list[GlossaryReplacementEntry] = []
+    seen_ids: set[int] = set()
+    seen_terms: set[str] = set()
+    for index, raw in enumerate(raw_entries, start=1):
+        if not isinstance(raw, dict):
+            raise GlossaryYamlError(f"Glossary entry {index} must be a mapping.")
+        entry_id = raw.get("id")
+        if type(entry_id) is not int or entry_id <= 0:
+            raise GlossaryYamlError(f"Glossary entry {index} requires a positive integer id.")
+        if entry_id in seen_ids:
+            raise GlossaryYamlError(f"Duplicate glossary entry id: {entry_id}.")
+        seen_ids.add(entry_id)
+
+        canonical = _required_text(raw, "canonical_term", index).strip()
+        category = raw.get("category")
+        if category not in GLOSSARY_CATEGORIES:
+            allowed = ", ".join(GLOSSARY_CATEGORIES)
+            raise GlossaryYamlError(
+                f"Glossary entry {index} has invalid category; expected one of: {allowed}."
+            )
+        aliases_value = raw.get("aliases")
+        if not isinstance(aliases_value, list) or any(
+            not isinstance(alias, str) or not alias.strip() for alias in aliases_value
+        ):
+            raise GlossaryYamlError(f"Glossary entry {index} aliases must be a list of text.")
+        aliases = tuple(alias.strip() for alias in aliases_value)
+        folded = [canonical.casefold(), *(alias.casefold() for alias in aliases)]
+        if len(folded) != len(set(folded)):
+            raise GlossaryYamlError(
+                f"Glossary entry {index} aliases must be unique and differ from its canonical term."
+            )
+        duplicate = next((term for term in folded if term in seen_terms), None)
+        if duplicate is not None:
+            raise GlossaryYamlError(
+                f"Glossary entry {index} contains a duplicate canonical term or alias."
+            )
+        seen_terms.update(folded)
+
+        description = raw.get("description")
+        if description is not None and not isinstance(description, str):
+            raise GlossaryYamlError(f"Glossary entry {index} description must be text or null.")
+        active = raw.get("active")
+        if type(active) is not bool:
+            raise GlossaryYamlError(f"Glossary entry {index} active must be true or false.")
+        result.append(
+            GlossaryReplacementEntry(
+                id=entry_id,
+                canonical_term=canonical,
+                aliases=aliases,
+                category=category,
+                description=description,
+                is_active=active,
+            )
+        )
+    return result
+
+
+def _required_text(entry: dict[object, object], field: str, index: int) -> str:
+    value = entry.get(field)
+    if not isinstance(value, str) or not value.strip():
+        raise GlossaryYamlError(f"Glossary entry {index} requires a non-empty {field}.")
+    return value

src/mka/application/meeting_service.py

@@ -0,0 +1,752 @@
+"""Use-case service for processing one uploaded meeting recording."""
+
+from __future__ import annotations
+
+import json
+import re
+import shutil
+from collections.abc import Callable
+from dataclasses import dataclass
+from datetime import date
+from pathlib import Path
+from typing import Any, Protocol
+from uuid import uuid4
+
+import yaml
+
+from mka.application.config import AppSettings
+from mka.application.glossary import (
+    GlossaryRepository,
+    glossary_alias_mapping,
+    render_glossary_terms,
+)
+from mka.application.performance import DEFAULT_PERFORMANCE_PROFILE, resolve_ollama_num_thread
+from mka.application.progress_timing import ProcessingTimer
+
+STAGES = ("preparing", "transcription", "diarization", "protocol_generation")
+_EXCERPT_TOKEN_PATTERN = re.compile(r"[^\W\d_]+|\d+", re.UNICODE)
+_EXCERPT_STOP_WORDS = frozenset(
+    {
+        "a", "an", "and", "are", "das", "der", "die", "ein", "eine", "for", "i",
+        "ich", "im", "in", "is", "it", "ja", "mit", "of", "or", "the", "to", "und",
+        "we", "wir", "with",
+    }
+)
+_ACKNOWLEDGEMENT_WORDS = frozenset(
+    {
+        "absolutely", "alles", "danke", "genau", "gut", "ja", "klar", "okay", "ok",
+        "perfekt", "right", "sure", "thanks", "verstanden", "yes",
+    }
+)
+_FIRST_PERSON_WORDS = frozenset(
+    {"i", "ich", "me", "mein", "meine", "my", "unser", "unsere", "we", "wir"}
+)
+_ACTION_WORDS = frozenset(
+    {
+        "agree", "approve", "believe", "can", "decide", "habe", "kann", "möchte", "plan",
+        "recommend", "think", "übernehme", "werde", "will",
+    }
+)
+_DATE_WORDS = frozenset(
+    {
+        "april", "august", "december", "dienstag", "donnerstag", "february", "freitag",
+        "friday", "january", "juli", "june", "mai", "march", "monday", "mittwoch", "montag",
+        "november", "october", "saturday", "september", "sonntag", "sunday", "thursday",
+        "tuesday", "wednesday",
+    }
+)
+
+
+class MeetingLabPort(Protocol):
+    """Operations the application requires from Meeting Lab."""
+
+    def create_context(self, data: dict[str, Any]) -> Any: ...
+
+    def create_config(self, values: dict[str, Any]) -> Any: ...
+
+    def run(
+        self,
+        config: Any,
+        meeting_context: Any,
+        progress_sink: Callable[[Any], None],
+    ) -> Any: ...
+
+    def regenerate_protocol(
+        self,
+        run_dir: Path,
+        meeting_context: Any,
+        progress_sink: Callable[[Any], None],
+        **options: Any,
+    ) -> Any: ...
+
+
+@dataclass(frozen=True)
+class MeetingDetails:
+    title: str
+    language: str = "de"
+    meeting_date: date | None = None
+    description: str = ""
+    meeting_id: str | None = None
+
+
+@dataclass(frozen=True)
+class ParticipantInput:
+    participant_id: str
+    display_name: str
+    role: str = ""
+    organization: str = ""
+    attendance_status: str = "present"
+
+
+@dataclass(frozen=True)
+class ProcessingOptions:
+    diarization_enabled: bool = False
+    audio_normalization: bool = True
+    performance_profile: str = DEFAULT_PERFORMANCE_PROFILE
+
+
+@dataclass(frozen=True)
+class AppProgressEvent:
+    stage: str
+    status: str
+    elapsed_seconds: float
+    progress: float | None = None
+    message: str | None = None
+
+
+@dataclass(frozen=True)
+class ProcessingOutcome:
+    succeeded: bool
+    run_dir: Path | None
+    original_protocol: str | None
+    protocol_path: Path | None
+    failed_stage: str | None = None
+    error_message: str | None = None
+    speaker_attribution_available: bool | None = None
+    awaiting_speaker_review: bool = False
+    detected_speaker_count: int | None = None
+
+
+@dataclass(frozen=True)
+class SpeakerReview:
+    speaker_label: str
+    excerpts: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class SpeakerMappingReview:
+    speakers: tuple[SpeakerReview, ...]
+    participants: tuple[tuple[str, str], ...]
+    current_mappings: dict[str, str]
+
+
+@dataclass(frozen=True)
+class _ExcerptCandidate:
+    speaker_label: str
+    text: str
+    segment_index: int
+    tokens: frozenset[str]
+    lexical_tokens: frozenset[str]
+    previous_other_speaker_tokens: frozenset[str]
+
+
+def _excerpt_tokens(text: str) -> tuple[str, ...]:
+    return tuple(token.lower() for token in _EXCERPT_TOKEN_PATTERN.findall(text))
+
+
+def _excerpt_score(candidate: _ExcerptCandidate, distinctive_token_count: int) -> float:
+    """Score an excerpt using deterministic lexical signals only."""
+    word_count = len(candidate.tokens)
+    lexical_count = len(candidate.lexical_tokens)
+    score = min(word_count, 24) * 0.3 + min(lexical_count, 16) * 0.6
+    if word_count < 5:
+        score -= 8.0
+    elif word_count < 9:
+        score -= 3.0
+    if lexical_count <= 2:
+        score -= 4.0
+    if candidate.tokens and candidate.tokens <= _ACKNOWLEDGEMENT_WORDS:
+        score -= 14.0
+    if candidate.text.rstrip().endswith("?"):
+        score -= 5.0
+    if any(token.isdigit() for token in candidate.tokens):
+        score += 2.5
+    if candidate.tokens & _DATE_WORDS:
+        score += 2.0
+    if candidate.tokens & _FIRST_PERSON_WORDS:
+        score += 1.5
+        if candidate.tokens & _ACTION_WORDS:
+            score += 2.0
+    proper_name_count = len(re.findall(r"\b[A-ZÄÖÜ][a-zäöüß]{2,}\b", candidate.text))
+    score += min(proper_name_count, 3) * 0.75
+    score += min(sum(len(token) >= 10 for token in candidate.lexical_tokens), 4) * 0.4
+    score += min(distinctive_token_count, 6) * 0.65
+    if candidate.previous_other_speaker_tokens and candidate.lexical_tokens:
+        overlap = len(candidate.lexical_tokens & candidate.previous_other_speaker_tokens)
+        if overlap / len(candidate.lexical_tokens) >= 0.7:
+            score -= 5.0
+    return score
+
+
+def _segment_bucket(segment_index: int, segment_count: int) -> int:
+    return min(5, segment_index * 6 // max(segment_count, 1))
+
+
+def _selection_score(
+    candidate: _ExcerptCandidate,
+    base_score: float,
+    selected: list[_ExcerptCandidate],
+    selected_buckets: set[int],
+    segment_count: int,
+) -> float:
+    """Favor excerpts from new meeting positions and avoid near duplicates."""
+    score = base_score
+    bucket = _segment_bucket(candidate.segment_index, segment_count)
+    if bucket not in selected_buckets:
+        score += 2.5
+    if not selected or not candidate.lexical_tokens:
+        return score
+    similarities = [
+        len(candidate.lexical_tokens & item.lexical_tokens)
+        / len(candidate.lexical_tokens | item.lexical_tokens)
+        for item in selected
+        if item.lexical_tokens
+    ]
+    maximum_similarity = max(similarities, default=0.0)
+    if maximum_similarity >= 0.72:
+        score -= 18.0
+    elif maximum_similarity >= 0.55:
+        score -= 6.0
+    nearest_position = min(abs(candidate.segment_index - item.segment_index) for item in selected)
+    if nearest_position / max(segment_count - 1, 1) < 0.12:
+        score -= 1.5
+    return score
+
+
+def stable_id(value: str) -> str:
+    """Return a schema-safe ID based on user text, with a random fallback."""
+    normalized = re.sub(r"[^a-z0-9]+", "-", value.lower()).strip("-")
+    return normalized or f"participant-{uuid4().hex[:8]}"
+
+
+class MeetingProcessingService:
+    """Translate UI input into Meeting Lab calls and persisted artifacts."""
+
+    def __init__(
+        self,
+        settings: AppSettings,
+        meeting_lab: MeetingLabPort,
+        glossary: GlossaryRepository | None = None,
+        timer: ProcessingTimer | None = None,
+    ) -> None:
+        self.settings = settings
+        self.meeting_lab = meeting_lab
+        self.glossary = glossary or GlossaryRepository(settings.glossary_database)
+        self.glossary.initialize()
+        self.timer = timer or ProcessingTimer()
+
+    def build_context(
+        self,
+        meeting: MeetingDetails,
+        participants: list[ParticipantInput],
+        speaker_mappings: dict[str, str] | None = None,
+    ) -> Any:
+        """Build and validate Meeting Context V1 through Meeting Lab."""
+        meeting_id = meeting.meeting_id or stable_id(meeting.title)
+        organizations = sorted(
+            {item.organization.strip() for item in participants if item.organization.strip()}
+        )
+        departments = [
+            {"id": stable_id(name), "name": name, "aliases": []} for name in organizations
+        ]
+        department_ids = {item["name"]: item["id"] for item in departments}
+        data = {
+            "schema_version": "1",
+            "meeting": {
+                "meeting_id": meeting_id,
+                "title": meeting.title.strip(),
+                "language": meeting.language,
+                "date": meeting.meeting_date.isoformat() if meeting.meeting_date else None,
+                "objective": "",
+                "notes": meeting.description.strip(),
+            },
+            "participants": [
+                {
+                    "participant_id": item.participant_id.strip(),
+                    "display_name": item.display_name.strip(),
+                    "aliases": [],
+                    "role": item.role.strip() or None,
+                    "department": department_ids.get(item.organization.strip()),
+                    "attendance_status": "present",
+                    "notes": None,
+                }
+                for item in participants
+                if item.attendance_status == "present"
+            ],
+            "speaker_mappings": dict(speaker_mappings or {}),
+            "mentioned_people": [
+                {
+                    "person_id": item.participant_id.strip(),
+                    "display_name": item.display_name.strip(),
+                    "aliases": [],
+                    "role": item.role.strip() or None,
+                    "department": department_ids.get(item.organization.strip()),
+                    "attendance_status": "mentioned_only",
+                    "notes": None,
+                }
+                for item in participants
+                if item.attendance_status == "mentioned_only"
+            ],
+            "organization": {
+                "name": None,
+                "departments": departments,
+                "abbreviations": {},
+            },
+            "known_entities": {},
+            "context_rules": {
+                "participant_list_is_authoritative": True,
+                "do_not_infer_roles": True,
+                "do_not_infer_departments": True,
+                "do_not_infer_responsibilities": True,
+                "mentioned_people_are_not_participants": True,
+            },
+        }
+        self._apply_glossary(data)
+        return self.meeting_lab.create_context(data)
+
+    def preserve_upload(self, meeting_id: str, filename: str, source: Any) -> Path:
+        """Persist an uploaded source before processing starts."""
+        safe_filename = Path(filename).name
+        upload_dir = self.settings.data_root / stable_id(meeting_id) / "uploads"
+        upload_dir.mkdir(parents=True, exist_ok=True)
+        destination = upload_dir / f"{uuid4().hex[:8]}_{safe_filename}"
+        with destination.open("wb") as target:
+            if hasattr(source, "getbuffer"):
+                target.write(source.getbuffer())
+            else:
+                shutil.copyfileobj(source, target)
+        return destination
+
+    def process(
+        self,
+        audio_file: Path,
+        meeting: MeetingDetails,
+        participants: list[ParticipantInput],
+        options: ProcessingOptions,
+        progress_sink: Callable[[AppProgressEvent], None] | None = None,
+        speaker_mappings: dict[str, str] | None = None,
+    ) -> ProcessingOutcome:
+        """Validate input, run Meeting Lab and expose a UI-oriented result."""
+        self.settings.validate_for_processing()
+        context = self.build_context(meeting, participants, speaker_mappings)
+        meeting_id = context.meeting_id
+        output_root = self.settings.data_root / stable_id(meeting_id) / "runs"
+        config = self.meeting_lab.create_config(
+            {
+                "audio_file": audio_file,
+                "whisper_model": self.settings.whisper_model,
+                "whisper_executable": self.settings.whisper_executable,
+                "ffmpeg_executable": self.settings.ffmpeg_executable,
+                "audio_normalization": options.audio_normalization,
+                "output_root": output_root,
+                "language": meeting.language or self.settings.language,
+                "threads": self.settings.threads,
+                "model": self.settings.protocol_model,
+                "ollama_endpoint": self.settings.ollama_endpoint,
+                "protocol_num_ctx": self.settings.protocol_num_ctx,
+                "protocol_safe_input_token_budget": (
+                    self.settings.protocol_safe_input_token_budget
+                ),
+                "protocol_num_thread": resolve_ollama_num_thread(options.performance_profile),
+                "glossary_aliases": glossary_alias_mapping(self.glossary.list(active_only=True)),
+                "diarization": (
+                    self.settings.diarization_mode if options.diarization_enabled else "off"
+                ),
+                "diarization_runtime": self.settings.diarization_runtime,
+                "diarization_container_image": (self.settings.diarization_container_image),
+                "diarization_container_args": self.settings.diarization_container_args,
+                "stop_after_diarization": options.diarization_enabled,
+            }
+        )
+        current_stage: str | None = None
+        self.timer.start_run()
+
+        def relay(event: Any) -> None:
+            nonlocal current_stage
+            if event.stage in STAGES and event.status == "started":
+                current_stage = event.stage
+            self._update_timer(event)
+            app_event = AppProgressEvent(
+                stage=event.stage,
+                status=event.status,
+                elapsed_seconds=event.elapsed_seconds,
+                progress=event.progress,
+                message=event.message,
+            )
+            if progress_sink is not None:
+                progress_sink(app_event)
+
+        try:
+            result = self.meeting_lab.run(config, context, relay)
+        except Exception:
+            self.timer.finish_run()
+            raise
+        self.timer.finish_run()
+        self._persist_timing(result.run_dir)
+        if result.exit_code != 0:
+            failure = self._read_failure(result.run_dir)
+            backend_stage = failure.get("stage")
+            failed_stage = {
+                "validation": "preparing",
+                "setup": "preparing",
+                "audio_preparation": "preparing",
+                "whisper": "transcription",
+                "transcript_validation": "transcription",
+                "protocol": "protocol_generation",
+            }.get(backend_stage, backend_stage)
+            failure_message = failure.get("message") or "Meeting Lab processing failed."
+            failure_type = failure.get("type")
+            if failure_type and not failure_message.startswith(f"{failure_type}:"):
+                failure_message = f"{failure_type}: {failure_message}"
+            return ProcessingOutcome(
+                succeeded=False,
+                run_dir=result.run_dir,
+                original_protocol=None,
+                protocol_path=None,
+                failed_stage=failed_stage or current_stage or "preparing",
+                error_message=failure_message,
+            )
+        if result.protocol_path is None:
+            return ProcessingOutcome(
+                succeeded=True,
+                run_dir=result.run_dir,
+                original_protocol=None,
+                protocol_path=None,
+                awaiting_speaker_review=True,
+                detected_speaker_count=self._detected_speaker_count(result.run_dir),
+            )
+        protocol_path = Path(result.protocol_path)
+        return ProcessingOutcome(
+            succeeded=True,
+            run_dir=result.run_dir,
+            original_protocol=protocol_path.read_text(encoding="utf-8"),
+            protocol_path=protocol_path,
+            speaker_attribution_available=self._speaker_attribution_available(result.run_dir),
+        )
+
+    def load_speaker_mapping_review(
+        self,
+        run_dir: Path,
+        *,
+        excerpts_per_speaker: int = 6,
+        minimum_excerpt_characters: int = 20,
+    ) -> SpeakerMappingReview | None:
+        """Load detected labels and small deterministic identification excerpts."""
+        run_dir = Path(run_dir)
+        transcript_path = run_dir / "diarization" / "transcript_diarized.json"
+        context_path = run_dir / "context" / "meeting_context.yaml"
+        if not transcript_path.is_file() or not context_path.is_file():
+            return None
+        transcript = json.loads(transcript_path.read_text(encoding="utf-8-sig"))
+        context_data = yaml.safe_load(context_path.read_text(encoding="utf-8"))
+        if not isinstance(transcript, dict) or not isinstance(context_data, dict):
+            raise ValueError("Existing run contains malformed speaker review artifacts.")
+        segments = transcript.get("segments")
+        if not isinstance(segments, list):
+            raise ValueError("Diarized transcript has no segments list.")
+
+        candidates_by_speaker: dict[str, list[_ExcerptCandidate]] = {}
+        short_candidates_by_speaker: dict[str, list[_ExcerptCandidate]] = {}
+        previous_label: str | None = None
+        previous_tokens: frozenset[str] = frozenset()
+        for segment_index, segment in enumerate(segments):
+            if not isinstance(segment, dict):
+                continue
+            label = segment.get("speaker_id")
+            text = segment.get("text")
+            normalized = " ".join(text.split()) if isinstance(text, str) else ""
+            tokens = frozenset(_excerpt_tokens(normalized))
+            if (
+                not isinstance(label, str)
+                or not label.startswith("SPEAKER_")
+                or label == "SPEAKER_UNASSIGNED"
+                or not normalized
+            ):
+                if normalized:
+                    previous_label = label if isinstance(label, str) else None
+                    previous_tokens = tokens
+                continue
+
+            candidate = _ExcerptCandidate(
+                speaker_label=label,
+                text=normalized,
+                segment_index=segment_index,
+                tokens=tokens,
+                lexical_tokens=tokens - _EXCERPT_STOP_WORDS,
+                previous_other_speaker_tokens=(
+                    previous_tokens
+                    if previous_label is not None and previous_label != label
+                    else frozenset()
+                ),
+            )
+            target = (
+                candidates_by_speaker
+                if len(normalized) >= minimum_excerpt_characters
+                else short_candidates_by_speaker
+            )
+            target.setdefault(label, []).append(candidate)
+            previous_label = label
+            previous_tokens = tokens
+
+        labels = sorted(set(candidates_by_speaker) | set(short_candidates_by_speaker))
+        token_speakers: dict[str, set[str]] = {}
+        for candidate_group in (*candidates_by_speaker.values(), *short_candidates_by_speaker.values()):
+            for candidate in candidate_group:
+                for token in candidate.lexical_tokens:
+                    token_speakers.setdefault(token, set()).add(candidate.speaker_label)
+        speakers = []
+        for label in labels:
+            candidates = candidates_by_speaker.get(label) or short_candidates_by_speaker.get(
+                label, []
+            )
+            excerpts = self._select_identifying_excerpts(
+                candidates,
+                excerpts_per_speaker=excerpts_per_speaker,
+                segment_count=len(segments),
+                token_speakers=token_speakers,
+            )
+            speakers.append(SpeakerReview(label, excerpts))
+        participants = tuple(
+            (participant["participant_id"], participant["display_name"])
+            for participant in context_data.get("participants", [])
+            if isinstance(participant, dict)
+            and isinstance(participant.get("participant_id"), str)
+            and isinstance(participant.get("display_name"), str)
+        )
+        mappings = context_data.get("speaker_mappings")
+        return SpeakerMappingReview(
+            speakers=tuple(speakers),
+            participants=participants,
+            current_mappings=dict(mappings) if isinstance(mappings, dict) else {},
+        )
+
+    @staticmethod
+    def _select_identifying_excerpts(
+        candidates: list[_ExcerptCandidate],
+        *,
+        excerpts_per_speaker: int,
+        segment_count: int,
+        token_speakers: dict[str, set[str]],
+    ) -> tuple[str, ...]:
+        """Choose informative, distinct excerpts while retaining transcript order."""
+        if len(candidates) <= excerpts_per_speaker:
+            return tuple(candidate.text for candidate in candidates)
+
+        base_scores = {
+            candidate: _excerpt_score(
+                candidate,
+                sum(
+                    len(token_speakers.get(token, set())) == 1
+                    for token in candidate.lexical_tokens
+                ),
+            )
+            for candidate in candidates
+        }
+        selected: list[_ExcerptCandidate] = []
+        selected_buckets: set[int] = set()
+        while len(selected) < excerpts_per_speaker:
+            choice = max(
+                (candidate for candidate in candidates if candidate not in selected),
+                key=lambda candidate: (
+                    _selection_score(
+                        candidate,
+                        base_scores[candidate],
+                        selected,
+                        selected_buckets,
+                        segment_count,
+                    ),
+                    -candidate.segment_index,
+                ),
+            )
+            selected.append(choice)
+            selected_buckets.add(_segment_bucket(choice.segment_index, segment_count))
+        return tuple(
+            candidate.text for candidate in sorted(selected, key=lambda item: item.segment_index)
+        )
+
+    def regenerate_protocol(
+        self,
+        run_dir: Path,
+        speaker_mappings: dict[str, str],
+        progress_sink: Callable[[AppProgressEvent], None] | None = None,
+        performance_profile: str = DEFAULT_PERFORMANCE_PROFILE,
+    ) -> ProcessingOutcome:
+        """Persist confirmed mappings and regenerate only the direct protocol."""
+        review = self.load_speaker_mapping_review(run_dir)
+        if review is None:
+            raise ValueError("This run has no diarization artifacts to map.")
+        detected = {speaker.speaker_label for speaker in review.speakers}
+        participant_ids = {participant_id for participant_id, _ in review.participants}
+        unknown_labels = sorted(set(speaker_mappings) - detected)
+        if unknown_labels:
+            raise ValueError(f"Unknown diarization speaker label: {unknown_labels[0]}")
+        unknown_participants = sorted(set(speaker_mappings.values()) - participant_ids)
+        if unknown_participants:
+            raise ValueError(f"Unknown participant ID: {unknown_participants[0]}")
+        assigned_participants = list(speaker_mappings.values())
+        if len(assigned_participants) != len(set(assigned_participants)):
+            raise ValueError("A participant may be assigned to only one speaker label.")
+
+        context_path = Path(run_dir) / "context" / "meeting_context.yaml"
+        context_data = yaml.safe_load(context_path.read_text(encoding="utf-8"))
+        if not isinstance(context_data, dict):
+            raise ValueError("Existing run contains malformed Meeting Context.")
+        context_data["speaker_mappings"] = dict(sorted(speaker_mappings.items()))
+        self._apply_glossary(context_data)
+        context = self.meeting_lab.create_context(context_data)
+        # A checkpoint mapping is user input, not a derived protocol artifact.  Save it
+        # before inference so a failed generation remains retryable with the same review.
+        if context_path.is_symlink():
+            context_path.unlink()
+        context_path.write_text(
+            yaml.safe_dump(context.data, allow_unicode=True, sort_keys=False), encoding="utf-8"
+        )
+
+        def relay(event: Any) -> None:
+            self._update_timer(event)
+            if progress_sink is not None:
+                progress_sink(
+                    AppProgressEvent(
+                        stage=event.stage,
+                        status=event.status,
+                        elapsed_seconds=event.elapsed_seconds,
+                        progress=event.progress,
+                        message=event.message,
+                    )
+                )
+
+        self.timer.start_run()
+        try:
+            result = self.meeting_lab.regenerate_protocol(
+                Path(run_dir),
+                context,
+                relay,
+                model=self.settings.protocol_model,
+                ollama_endpoint=self.settings.ollama_endpoint,
+                protocol_num_ctx=self.settings.protocol_num_ctx,
+                protocol_safe_input_token_budget=(self.settings.protocol_safe_input_token_budget),
+                protocol_num_thread=resolve_ollama_num_thread(performance_profile),
+                glossary_aliases=glossary_alias_mapping(self.glossary.list(active_only=True)),
+            )
+        except Exception:
+            self.timer.finish_run()
+            raise
+        self.timer.finish_run()
+        self._persist_timing(Path(result.run_dir))
+        protocol_path = Path(result.protocol_path)
+        return ProcessingOutcome(
+            succeeded=True,
+            run_dir=Path(result.run_dir),
+            original_protocol=protocol_path.read_text(encoding="utf-8"),
+            protocol_path=protocol_path,
+            speaker_attribution_available=self._speaker_attribution_available(result.run_dir),
+        )
+
+    def _apply_glossary(self, context_data: dict[str, Any]) -> None:
+        """Merge active terminology into context without discarding other entities."""
+        glossary_terms = render_glossary_terms(self.glossary.list(active_only=True))
+        known_entities = context_data.setdefault("known_entities", {})
+        if glossary_terms:
+            known_entities["Authoritative terminology"] = glossary_terms
+        else:
+            known_entities.pop("Authoritative terminology", None)
+        rules = context_data.setdefault("context_rules", {})
+        rules["glossary_canonical_spelling"] = (
+            "Use canonical glossary spellings only when the meeting clearly refers to "
+            "those terms; do not invent matches or replace unrelated words."
+        )
+        rules["glossary_core_terms"] = (
+            "Preserve surrounding context and use canonical core terms inside compounds "
+            "where appropriate."
+        )
+
+    @staticmethod
+    def _speaker_attribution_available(run_dir: Path | None) -> bool | None:
+        if run_dir is None:
+            return None
+        metadata_path = Path(run_dir) / "protocol" / "runtime_metadata.json"
+        if not metadata_path.is_file():
+            return None
+        try:
+            metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
+        except (OSError, json.JSONDecodeError):
+            return None
+        value = metadata.get("speaker_attribution_available")
+        return value if isinstance(value, bool) else None
+
+    @staticmethod
+    def _detected_speaker_count(run_dir: Path | None) -> int | None:
+        if run_dir is None:
+            return None
+        metadata_path = Path(run_dir) / "run_metadata.json"
+        try:
+            metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
+        except (OSError, json.JSONDecodeError):
+            return None
+        value = metadata.get("diarization", {}).get("speaker_count")
+        return value if isinstance(value, int) else None
+
+    @staticmethod
+    def _read_failure(run_dir: Path | None) -> dict[str, str]:
+        if run_dir is None:
+            return {}
+        metadata_path = Path(run_dir) / "run_metadata.json"
+        if not metadata_path.is_file():
+            return {}
+        try:
+            failure = json.loads(metadata_path.read_text(encoding="utf-8")).get("failure")
+        except (OSError, json.JSONDecodeError):
+            return {}
+        return failure if isinstance(failure, dict) else {}
+
+    @staticmethod
+    def save_edited_protocol(run_dir: Path, text: str) -> Path:
+        """Persist user edits beside, never over, the generated protocol."""
+        destination = Path(run_dir) / "protocol_edited.md"
+        destination.write_text(text, encoding="utf-8")
+        return destination
+
+    def _update_timer(self, event: Any) -> None:
+        """Apply a backend progress event to the shared per-run timer."""
+        if event.stage in STAGES and event.status == "started":
+            self.timer.start_stage(event.stage)
+        elif event.stage in STAGES and event.status in {"completed", "skipped"}:
+            self.timer.finish_stage(event.stage)
+        if event.stage in {"completed", "failed"}:
+            self.timer.finish_run()
+
+    def _persist_timing(self, run_dir: Path | None) -> None:
+        """Merge final monotonic durations into Meeting Lab run metadata."""
+        if run_dir is None:
+            return
+        metadata_path = Path(run_dir) / "run_metadata.json"
+        metadata: dict[str, Any] = {}
+        if metadata_path.is_file():
+            try:
+                existing = json.loads(metadata_path.read_text(encoding="utf-8"))
+                if isinstance(existing, dict):
+                    metadata = existing
+            except (OSError, json.JSONDecodeError):
+                return
+        snapshot = self.timer.snapshot()
+        metadata["timing"] = {
+            "stages_seconds": snapshot.stage_durations,
+            "total_seconds": snapshot.total_duration,
+        }
+        metadata_path.parent.mkdir(parents=True, exist_ok=True)
+        try:
+            metadata_path.write_text(
+                json.dumps(metadata, indent=2, sort_keys=True) + "\n", encoding="utf-8"
+            )
+        except OSError:
+            return

src/mka/application/people_yaml.py

@@ -0,0 +1,108 @@
+"""Versioned YAML import and export for reusable People lists."""
+
+from __future__ import annotations
+
+import re
+from collections.abc import Sequence
+
+import yaml
+
+from mka.application.meeting_service import ParticipantInput
+
+PEOPLE_YAML_VERSION = 1
+ATTENDANCE_VALUES = frozenset({"present", "mentioned_only"})
+PARTICIPANT_ID_PATTERN = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_.:-]*$")
+
+
+class PeopleYamlError(ValueError):
+    """Raised when a reusable People-list document is invalid."""
+
+
+def export_people_yaml(people: Sequence[ParticipantInput]) -> str:
+    """Serialize people deterministically without meeting-specific data."""
+    document = {
+        "version": PEOPLE_YAML_VERSION,
+        "people": [
+            {
+                "participant_id": person.participant_id,
+                "display_name": person.display_name,
+                "role": person.role,
+                "organization": person.organization,
+                "attendance_status": person.attendance_status,
+            }
+            for person in people
+        ],
+    }
+    return yaml.safe_dump(
+        document,
+        allow_unicode=True,
+        sort_keys=False,
+        default_flow_style=False,
+    )
+
+
+def import_people_yaml(content: str | bytes) -> list[ParticipantInput]:
+    """Parse and validate a complete replacement People list."""
+    try:
+        if isinstance(content, bytes):
+            content = content.decode("utf-8")
+        document = yaml.safe_load(content)
+    except (yaml.YAMLError, UnicodeDecodeError) as exc:
+        raise PeopleYamlError(f"Malformed People YAML: {exc}") from exc
+
+    if not isinstance(document, dict):
+        raise PeopleYamlError("People YAML must contain a top-level mapping.")
+    version = document.get("version")
+    if type(version) is not int or version != PEOPLE_YAML_VERSION:
+        raise PeopleYamlError(
+            f"Unsupported People YAML version {version!r}; expected {PEOPLE_YAML_VERSION}."
+        )
+    entries = document.get("people")
+    if not isinstance(entries, list):
+        raise PeopleYamlError("People YAML must contain a top-level 'people' list.")
+
+    people: list[ParticipantInput] = []
+    seen_ids: set[str] = set()
+    for index, entry in enumerate(entries, start=1):
+        if not isinstance(entry, dict):
+            raise PeopleYamlError(f"Person {index} must be a mapping.")
+        participant_id = _required_text(entry, "participant_id", index)
+        if PARTICIPANT_ID_PATTERN.fullmatch(participant_id) is None:
+            raise PeopleYamlError(f"Person {index} has invalid participant_id {participant_id!r}.")
+        if participant_id in seen_ids:
+            raise PeopleYamlError(f"Duplicate participant_id: {participant_id!r}.")
+        seen_ids.add(participant_id)
+
+        display_name = _required_text(entry, "display_name", index)
+        role = _optional_text(entry, "role", index)
+        organization = _optional_text(entry, "organization", index)
+        attendance_status = entry.get("attendance_status")
+        if attendance_status not in ATTENDANCE_VALUES:
+            allowed = ", ".join(sorted(ATTENDANCE_VALUES))
+            raise PeopleYamlError(
+                f"Person {index} has invalid attendance_status; expected one of: {allowed}."
+            )
+        people.append(
+            ParticipantInput(
+                participant_id=participant_id,
+                display_name=display_name,
+                role=role,
+                organization=organization,
+                attendance_status=attendance_status,
+            )
+        )
+    return people
+
+
+def _required_text(entry: dict[object, object], field: str, index: int) -> str:
+    value = entry.get(field)
+    if not isinstance(value, str) or not value.strip():
+        raise PeopleYamlError(f"Person {index} requires a non-empty {field}.")
+    return value
+
+
+def _optional_text(entry: dict[object, object], field: str, index: int) -> str:
+    value = entry.get(field, "")
+    if not isinstance(value, str):
+        raise PeopleYamlError(f"Person {index} field {field} must be text.")
+    return value

src/mka/application/performance.py

@@ -0,0 +1,23 @@
+"""Abstract performance profiles resolved to current backend runtime options."""
+
+from __future__ import annotations
+
+DEFAULT_PERFORMANCE_PROFILE = "auto"
+PERFORMANCE_PROFILES = ("auto", "fast", "efficient", "powersave")
+
+_OLLAMA_THREADS_BY_PROFILE = {
+    "fast": 16,
+    "efficient": 10,
+    "powersave": 4,
+}
+
+
+def resolve_ollama_num_thread(profile: str) -> int | None:
+    """Resolve a profile, leaving automatic thread selection to Ollama for Auto."""
+    if profile == "auto":
+        return None
+    try:
+        return _OLLAMA_THREADS_BY_PROFILE[profile]
+    except KeyError as exc:
+        allowed = ", ".join(PERFORMANCE_PROFILES)
+        raise ValueError(f"Unknown performance profile {profile!r}; expected: {allowed}.") from exc

src/mka/application/progress_timing.py

@@ -0,0 +1,80 @@
+"""Thread-safe monotonic timing state for meeting-processing progress."""
+
+from __future__ import annotations
+
+import time
+from collections.abc import Callable
+from dataclasses import dataclass
+from threading import Lock
+
+
+@dataclass(frozen=True)
+class TimingSnapshot:
+    """Presentation-neutral runtime values measured in seconds."""
+
+    stage_durations: dict[str, float]
+    active_stage: str | None
+    total_duration: float
+    running: bool
+
+
+class ProcessingTimer:
+    """Track pipeline stages independently from pipeline business logic."""
+
+    def __init__(self, clock: Callable[[], float] = time.perf_counter) -> None:
+        self._clock = clock
+        self._lock = Lock()
+        self._run_started: float | None = None
+        self._run_finished: float | None = None
+        self._stage_started: dict[str, float] = {}
+        self._stage_finished: dict[str, float] = {}
+        self._active_stage: str | None = None
+
+    def start_run(self) -> None:
+        with self._lock:
+            self._run_started = self._clock()
+            self._run_finished = None
+            self._stage_started.clear()
+            self._stage_finished.clear()
+            self._active_stage = None
+
+    def start_stage(self, stage: str) -> None:
+        with self._lock:
+            now = self._clock()
+            if self._active_stage is not None and self._active_stage != stage:
+                self._stage_finished.setdefault(self._active_stage, now)
+            self._stage_started.setdefault(stage, now)
+            self._active_stage = stage
+
+    def finish_stage(self, stage: str) -> None:
+        with self._lock:
+            if stage in self._stage_started:
+                self._stage_finished.setdefault(stage, self._clock())
+            if self._active_stage == stage:
+                self._active_stage = None
+
+    def finish_run(self) -> None:
+        with self._lock:
+            if self._run_finished is not None:
+                return
+            now = self._clock()
+            if self._active_stage is not None:
+                self._stage_finished.setdefault(self._active_stage, now)
+                self._active_stage = None
+            if self._run_started is not None:
+                self._run_finished = now
+
+    def snapshot(self) -> TimingSnapshot:
+        with self._lock:
+            now = self._run_finished if self._run_finished is not None else self._clock()
+            durations = {
+                stage: max(0.0, self._stage_finished.get(stage, now) - started)
+                for stage, started in self._stage_started.items()
+            }
+            total = max(0.0, now - self._run_started) if self._run_started is not None else 0.0
+            return TimingSnapshot(
+                stage_durations=durations,
+                active_stage=self._active_stage,
+                total_duration=total,
+                running=self._run_started is not None and self._run_finished is None,
+            )

src/mka/application/run_inputs.py

@@ -0,0 +1,218 @@
+"""Versioned JSON import/export for the user-facing run input form."""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Sequence
+from dataclasses import dataclass
+from datetime import date
+from pathlib import Path
+from typing import Any
+
+from mka.application.meeting_service import ParticipantInput, stable_id
+from mka.application.people_yaml import ATTENDANCE_VALUES, PARTICIPANT_ID_PATTERN
+
+RUN_INPUT_SCHEMA_VERSION = 1
+SUPPORTED_LANGUAGES = frozenset({"de", "en"})
+
+
+class RunInputJsonError(ValueError):
+    """Raised when a run-input JSON document is malformed or unsupported."""
+
+
+@dataclass(frozen=True)
+class RunInputState:
+    """All user-configurable values needed before starting a processing run."""
+
+    title: str
+    description: str
+    language: str
+    meeting_date: date | None
+    participants: tuple[ParticipantInput, ...]
+    audio_normalization: bool
+    diarization_enabled: bool
+    source_file_name: str | None = None
+
+    @classmethod
+    def defaults(cls) -> RunInputState:
+        return cls(
+            title="",
+            description="",
+            language="de",
+            meeting_date=date.today(),
+            participants=(ParticipantInput(participant_id="", display_name=""),),
+            audio_normalization=True,
+            diarization_enabled=False,
+        )
+
+
+def export_run_inputs(state: RunInputState) -> str:
+    """Serialize form state as deterministic, human-readable JSON."""
+    source_file_name = _source_file_name(state.source_file_name)
+    document = {
+        "schema_version": RUN_INPUT_SCHEMA_VERSION,
+        "meeting": {
+            "title": state.title,
+            "description": state.description,
+            "language": state.language,
+            "date": state.meeting_date.isoformat() if state.meeting_date else None,
+            "participants": [
+                {
+                    "participant_id": person.participant_id,
+                    "display_name": person.display_name,
+                    "role": person.role,
+                    "organization": person.organization,
+                    "attendance_status": person.attendance_status,
+                }
+                for person in state.participants
+            ],
+        },
+        "processing": {
+            "audio_normalization": state.audio_normalization,
+            "diarization_enabled": state.diarization_enabled,
+        },
+        "source_file_name": source_file_name,
+    }
+    return json.dumps(document, ensure_ascii=False, indent=2) + "\n"
+
+
+def import_run_inputs(content: str | bytes) -> RunInputState:
+    """Parse supported fields, applying current defaults to omitted optional fields."""
+    try:
+        if isinstance(content, bytes):
+            content = content.decode("utf-8")
+        document = json.loads(content)
+    except (UnicodeDecodeError, json.JSONDecodeError) as exc:
+        raise RunInputJsonError(f"Malformed input JSON: {exc}") from exc
+    if not isinstance(document, dict):
+        raise RunInputJsonError("Input JSON must contain a top-level object.")
+    version = document.get("schema_version")
+    if type(version) is not int or version != RUN_INPUT_SCHEMA_VERSION:
+        raise RunInputJsonError(
+            f"Unsupported input schema_version {version!r}; expected {RUN_INPUT_SCHEMA_VERSION}."
+        )
+
+    defaults = RunInputState.defaults()
+    meeting = _optional_mapping(document, "meeting")
+    processing = _optional_mapping(document, "processing")
+    title = _optional_text(meeting, "title", defaults.title)
+    description = _optional_text(meeting, "description", defaults.description)
+    language = _optional_text(meeting, "language", defaults.language)
+    if language not in SUPPORTED_LANGUAGES:
+        raise RunInputJsonError(f"Unsupported meeting language: {language!r}.")
+    meeting_date = _meeting_date(meeting, defaults.meeting_date)
+    participants = _participants(meeting, defaults.participants)
+    audio_normalization = _optional_bool(
+        processing, "audio_normalization", defaults.audio_normalization
+    )
+    diarization_enabled = _optional_bool(
+        processing, "diarization_enabled", defaults.diarization_enabled
+    )
+    source_file_name = _source_file_name(document.get("source_file_name"))
+    return RunInputState(
+        title=title,
+        description=description,
+        language=language,
+        meeting_date=meeting_date,
+        participants=participants,
+        audio_normalization=audio_normalization,
+        diarization_enabled=diarization_enabled,
+        source_file_name=source_file_name,
+    )
+
+
+def run_input_filename(title: str) -> str:
+    """Build a stable download filename without filesystem-specific characters."""
+    suffix = stable_id(title) if title.strip() else "untitled"
+    return f"meeting-inputs-{suffix}.json"
+
+
+def _optional_mapping(document: dict[str, Any], field: str) -> dict[str, Any]:
+    value = document.get(field, {})
+    if not isinstance(value, dict):
+        raise RunInputJsonError(f"Field {field!r} must be an object.")
+    return value
+
+
+def _optional_text(document: dict[str, Any], field: str, default: str) -> str:
+    value = document.get(field, default)
+    if not isinstance(value, str):
+        raise RunInputJsonError(f"Field {field!r} must be text.")
+    return value
+
+
+def _optional_bool(document: dict[str, Any], field: str, default: bool) -> bool:
+    value = document.get(field, default)
+    if type(value) is not bool:
+        raise RunInputJsonError(f"Field {field!r} must be true or false.")
+    return value
+
+
+def _meeting_date(document: dict[str, Any], default: date | None) -> date | None:
+    if "date" not in document:
+        return default
+    value = document["date"]
+    if value is None:
+        return None
+    if not isinstance(value, str):
+        raise RunInputJsonError("Field 'date' must be an ISO date or null.")
+    try:
+        return date.fromisoformat(value)
+    except ValueError as exc:
+        raise RunInputJsonError("Field 'date' must be a valid ISO date or null.") from exc
+
+
+def _participants(
+    document: dict[str, Any], default: Sequence[ParticipantInput]
+) -> tuple[ParticipantInput, ...]:
+    if "participants" not in document:
+        return tuple(default)
+    entries = document["participants"]
+    if not isinstance(entries, list):
+        raise RunInputJsonError("Field 'participants' must be a list.")
+    people: list[ParticipantInput] = []
+    seen_ids: set[str] = set()
+    for index, entry in enumerate(entries, start=1):
+        if not isinstance(entry, dict):
+            raise RunInputJsonError(f"Participant {index} must be an object.")
+        participant_id = _optional_text(entry, "participant_id", "")
+        display_name = _optional_text(entry, "display_name", "")
+        role = _optional_text(entry, "role", "")
+        organization = _optional_text(entry, "organization", "")
+        attendance = _optional_text(entry, "attendance_status", "present")
+        if bool(participant_id) != bool(display_name):
+            raise RunInputJsonError(
+                f"Participant {index} must provide both participant_id and display_name."
+            )
+        if participant_id and PARTICIPANT_ID_PATTERN.fullmatch(participant_id) is None:
+            raise RunInputJsonError(
+                f"Participant {index} has invalid participant_id {participant_id!r}."
+            )
+        if participant_id in seen_ids:
+            raise RunInputJsonError(f"Duplicate participant_id: {participant_id!r}.")
+        if participant_id:
+            seen_ids.add(participant_id)
+        if attendance not in ATTENDANCE_VALUES:
+            raise RunInputJsonError(
+                f"Participant {index} has invalid attendance_status {attendance!r}."
+            )
+        people.append(
+            ParticipantInput(
+                participant_id=participant_id,
+                display_name=display_name,
+                role=role,
+                organization=organization,
+                attendance_status=attendance,
+            )
+        )
+    return tuple(people)
+
+
+def _source_file_name(value: Any) -> str | None:
+    if value is None:
+        return None
+    if not isinstance(value, str) or not value.strip():
+        raise RunInputJsonError("Field 'source_file_name' must be non-empty text or null.")
+    if Path(value).name != value:
+        raise RunInputJsonError("Field 'source_file_name' must be a filename, not a path.")
+    return value

src/mka/integrations/__init__.py

@@ -0,0 +1 @@
+"""Adapters for external processing systems."""

src/mka/integrations/meeting_lab.py

@@ -0,0 +1,68 @@
+"""Narrow adapter around the reusable Meeting Lab Python API."""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Mapping
+from typing import Any
+
+
+class MeetingLabUnavailableError(RuntimeError):
+    """Raised when the Meeting Lab package cannot be imported."""
+
+
+class MeetingLabGateway:
+    """Load and delegate to Meeting Lab without coupling Streamlit to it."""
+
+    def __init__(self) -> None:
+        try:
+            from src.meeting_lab.models.meeting_context import create_meeting_context
+            from src.meeting_lab.orchestration.mvp import (
+                MvpMeetingConfig,
+                regenerate_mvp_protocol,
+                run_mvp_meeting,
+            )
+        except ImportError as exc:
+            raise MeetingLabUnavailableError(
+                "Meeting Lab is unavailable. Add the Meeting Lab repository root "
+                "to PYTHONPATH as described in README.md."
+            ) from exc
+        self._config_type = MvpMeetingConfig
+        self._create_context = create_meeting_context
+        self._run_mvp_meeting = run_mvp_meeting
+        self._regenerate_mvp_protocol = regenerate_mvp_protocol
+
+    def create_context(self, data: dict[str, Any]) -> Any:
+        """Validate structured context through Meeting Lab's domain boundary."""
+        return self._create_context(data)
+
+    def create_config(self, values: Mapping[str, Any]) -> Any:
+        """Create the concrete Meeting Lab run configuration."""
+        return self._config_type(**values)
+
+    def run(
+        self,
+        config: Any,
+        meeting_context: Any,
+        progress_sink: Callable[[Any], None],
+    ) -> Any:
+        """Run Meeting Lab synchronously and relay progress callbacks."""
+        return self._run_mvp_meeting(
+            config,
+            meeting_context=meeting_context,
+            progress_sink=progress_sink,
+        )
+
+    def regenerate_protocol(
+        self,
+        run_dir: Any,
+        meeting_context: Any,
+        progress_sink: Callable[[Any], None],
+        **options: Any,
+    ) -> Any:
+        """Regenerate protocol artifacts without rerunning media processing."""
+        return self._regenerate_mvp_protocol(
+            run_dir,
+            meeting_context=meeting_context,
+            progress_sink=progress_sink,
+            **options,
+        )

src/mka/ui/streamlit_app.py

@@ -0,0 +1,785 @@
+"""Streamlit presentation layer for the first Meeting Assistant MVP."""
+
+from __future__ import annotations
+
+import time
+from collections.abc import Callable
+from concurrent.futures import ThreadPoolExecutor
+from datetime import date
+from queue import Empty, Queue
+from typing import Any
+from uuid import uuid4
+
+import streamlit as st
+
+from mka.application.config import AppSettings, ConfigurationError
+from mka.application.glossary import (
+    GLOSSARY_CATEGORIES,
+    GlossaryConflictError,
+    GlossaryRepository,
+)
+from mka.application.glossary_yaml import (
+    GlossaryYamlError,
+    export_glossary_yaml,
+    import_glossary_yaml,
+)
+from mka.application.meeting_service import (
+    STAGES,
+    AppProgressEvent,
+    MeetingDetails,
+    MeetingProcessingService,
+    ParticipantInput,
+    ProcessingOptions,
+    SpeakerMappingReview,
+    stable_id,
+)
+from mka.application.people_yaml import (
+    PeopleYamlError,
+    export_people_yaml,
+    import_people_yaml,
+)
+from mka.application.performance import DEFAULT_PERFORMANCE_PROFILE, PERFORMANCE_PROFILES
+from mka.application.progress_timing import TimingSnapshot
+from mka.application.run_inputs import (
+    RunInputJsonError,
+    RunInputState,
+    export_run_inputs,
+    import_run_inputs,
+    run_input_filename,
+)
+from mka.integrations.meeting_lab import (
+    MeetingLabGateway,
+    MeetingLabUnavailableError,
+)
+
+STAGE_LABELS = {
+    "preparing": "Preparation",
+    "transcription": "Transcription",
+    "diarization": "Diarization",
+    "protocol_generation": "Protocol generation",
+}
+
+
+def _parse_aliases(value: str) -> tuple[str, ...]:
+    """Parse one alias per line while tolerating comma-separated input."""
+    return tuple(
+        alias.strip() for line in value.splitlines() for alias in line.split(",") if alias.strip()
+    )
+
+
+def _render_glossary(repository: GlossaryRepository) -> None:
+    """Render simple global glossary CRUD controls."""
+    with st.expander("Terminology glossary", expanded=False):
+        st.caption(
+            "Store canonical core terms and recognition aliases. Compound phrases are "
+            "composed from meeting context during protocol generation."
+        )
+        import_file = st.file_uploader(
+            "Import glossary",
+            type=["yaml", "yml"],
+            key="glossary_import_file",
+            help="Validate and atomically replace the SQLite glossary from versioned YAML.",
+        )
+        action_columns = st.columns(2)
+        if action_columns[0].button("Import glossary", disabled=import_file is None):
+            try:
+                imported = import_glossary_yaml(import_file.getvalue())
+                repository.replace_all(imported)
+            except (GlossaryYamlError, GlossaryConflictError, ValueError) as exc:
+                st.error(str(exc))
+            else:
+                st.session_state.glossary_import_message = (
+                    f"Imported {len(imported)} glossary entries."
+                )
+                st.rerun()
+        action_columns[1].download_button(
+            "Export glossary",
+            data=export_glossary_yaml(repository.list()).encode("utf-8"),
+            file_name="terminology-glossary.yaml",
+            mime="application/yaml",
+        )
+        if message := st.session_state.pop("glossary_import_message", None):
+            st.success(message)
+
+        with st.form("glossary_add"):
+            columns = st.columns(2)
+            canonical = columns[0].text_input("Canonical term")
+            category = columns[1].selectbox("Category", GLOSSARY_CATEGORIES)
+            aliases = st.text_area("Aliases", help="One per line or comma-separated.")
+            description = st.text_area("Optional description")
+            if st.form_submit_button("Add glossary entry"):
+                try:
+                    repository.create(
+                        canonical,
+                        category,
+                        aliases=_parse_aliases(aliases),
+                        description=description,
+                    )
+                except (GlossaryConflictError, ValueError) as exc:
+                    st.error(str(exc))
+                else:
+                    st.success("Glossary entry added.")
+                    st.rerun()
+
+        search = st.text_input("Search glossary")
+        active_only = st.checkbox("Show active entries only")
+        entries = repository.list(search, active_only=active_only)
+        st.caption(f"{len(entries)} glossary entries")
+        for entry in entries:
+            status = "active" if entry.is_active else "inactive"
+            with (
+                st.expander(f"{entry.canonical_term} · {entry.category} · {status}"),
+                st.form(f"glossary_edit_{entry.id}"),
+            ):
+                columns = st.columns(2)
+                edited_canonical = columns[0].text_input("Canonical term", entry.canonical_term)
+                edited_category = columns[1].selectbox(
+                    "Category",
+                    GLOSSARY_CATEGORIES,
+                    index=GLOSSARY_CATEGORIES.index(entry.category),
+                )
+                edited_aliases = st.text_area("Aliases", "\n".join(entry.aliases))
+                edited_description = st.text_area("Optional description", entry.description or "")
+                edited_active = st.checkbox("Active", value=entry.is_active)
+                delete_confirmed = st.checkbox(
+                    "Permanently delete this entry",
+                    help="Prefer clearing Active for normal use.",
+                )
+                action_columns = st.columns(2)
+                save = action_columns[0].form_submit_button("Save changes")
+                delete = action_columns[1].form_submit_button(
+                    "Delete", disabled=not delete_confirmed
+                )
+                try:
+                    if save:
+                        repository.update(
+                            entry.id,
+                            edited_canonical,
+                            edited_category,
+                            aliases=_parse_aliases(edited_aliases),
+                            description=edited_description,
+                            is_active=edited_active,
+                        )
+                        st.rerun()
+                    if delete:
+                        repository.delete(entry.id)
+                        st.rerun()
+                except (GlossaryConflictError, ValueError) as exc:
+                    st.error(str(exc))
+
+
+def _new_participant() -> dict[str, str]:
+    return {
+        "row_id": uuid4().hex,
+        "participant_id": "",
+        "display_name": "",
+        "role": "",
+        "organization": "",
+        "attendance_status": "present",
+    }
+
+
+def _initialize_state() -> None:
+    st.session_state.setdefault("participants", [_new_participant()])
+    st.session_state.setdefault("outcome", None)
+    st.session_state.setdefault("edited_protocol", "")
+    st.session_state.setdefault("source_media_widget_generation", 0)
+    _apply_pending_run_inputs()
+    _apply_pending_edited_protocol()
+
+
+def _queue_run_inputs(value: RunInputState) -> None:
+    """Defer form restoration until the beginning of the next Streamlit run."""
+    st.session_state["pending_run_inputs"] = value
+
+
+def _apply_pending_run_inputs() -> None:
+    """Restore imported values before any corresponding widget is instantiated."""
+    value = st.session_state.pop("pending_run_inputs", None)
+    if value is None:
+        return
+    st.session_state.update(
+        {
+            "meeting_title": value.title,
+            "meeting_description": value.description,
+            "meeting_language": value.language,
+            "meeting_has_date": value.meeting_date is not None,
+            "meeting_date": value.meeting_date or date.today(),
+            "participants": _people_to_rows(list(value.participants)),
+            "audio_normalization": value.audio_normalization,
+            "diarization_enabled": value.diarization_enabled,
+            "imported_source_file_name": value.source_file_name,
+            "run_input_import_message": (
+                "Meeting inputs imported. Select the source media before processing."
+            ),
+            "source_media_widget_generation": (
+                st.session_state.get("source_media_widget_generation", 0) + 1
+            ),
+        }
+    )
+
+
+def _apply_pending_edited_protocol() -> None:
+    """Apply a deferred widget value before the widget is instantiated."""
+    if "pending_edited_protocol" in st.session_state:
+        st.session_state["edited_protocol"] = st.session_state.pop("pending_edited_protocol")
+
+
+def _queue_edited_protocol(value: str) -> None:
+    """Defer an edited-protocol widget update until the next Streamlit run."""
+    st.session_state["pending_edited_protocol"] = value
+
+
+def _people_to_rows(people: list[ParticipantInput]) -> list[dict[str, str]]:
+    """Create fresh widget rows while preserving reusable person IDs."""
+    return [
+        {
+            "row_id": uuid4().hex,
+            "participant_id": person.participant_id,
+            "display_name": person.display_name,
+            "role": person.role,
+            "organization": person.organization,
+            "attendance_status": person.attendance_status,
+        }
+        for person in people
+    ]
+
+
+def _render_run_input_import() -> None:
+    """Render import controls before the widgets whose state they restore."""
+    st.subheader("Input configuration")
+    imported_file = st.file_uploader(
+        "Import inputs",
+        type=["json"],
+        key="run_input_import_file",
+        help="Restore form values only; source media is never included.",
+    )
+    if st.button("Import input configuration", disabled=imported_file is None):
+        try:
+            imported = import_run_inputs(imported_file.getvalue())
+        except RunInputJsonError as exc:
+            st.error(str(exc))
+        else:
+            _queue_run_inputs(imported)
+            st.rerun()
+    if message := st.session_state.pop("run_input_import_message", None):
+        st.success(message)
+
+
+def _render_participants() -> list[ParticipantInput]:
+    st.subheader("People")
+    st.caption("Record whether each relevant person attended or was only mentioned.")
+    imported_file = st.file_uploader(
+        "Import people",
+        type=["yaml", "yml"],
+        help="Replace the current People list from a versioned YAML export.",
+    )
+    if st.button("Import people list", disabled=imported_file is None):
+        try:
+            imported_people = import_people_yaml(imported_file.getvalue())
+        except PeopleYamlError as exc:
+            st.error(str(exc))
+        else:
+            st.session_state.participants = _people_to_rows(imported_people)
+            st.session_state.people_import_message = f"Imported {len(imported_people)} people."
+            st.rerun()
+    if message := st.session_state.pop("people_import_message", None):
+        st.success(message)
+
+    rows = st.session_state.participants
+    remove_index: int | None = None
+    for index, row in enumerate(rows):
+        row_id = row["row_id"]
+        columns = st.columns([2, 2, 2, 2, 2, 0.6])
+        row["display_name"] = columns[0].text_input(
+            "Name", value=row["display_name"], key=f"name_{row_id}"
+        )
+        suggested_id = row["participant_id"] or stable_id(row["display_name"])
+        row["participant_id"] = columns[1].text_input(
+            "Participant ID", value=suggested_id, key=f"id_{row_id}"
+        )
+        row["role"] = columns[2].text_input("Role", value=row["role"], key=f"role_{row_id}")
+        row["organization"] = columns[3].text_input(
+            "Organization / department",
+            value=row["organization"],
+            key=f"organization_{row_id}",
+        )
+        row["attendance_status"] = columns[4].selectbox(
+            "Attendance",
+            options=["present", "mentioned_only"],
+            format_func=lambda value: {
+                "present": "Present / participated",
+                "mentioned_only": "Mentioned, but not present",
+            }[value],
+            index=0 if row["attendance_status"] == "present" else 1,
+            key=f"attendance_{row_id}",
+        )
+        if columns[5].button("Remove", key=f"remove_{row_id}"):
+            remove_index = index
+    if remove_index is not None:
+        rows.pop(remove_index)
+        st.rerun()
+    action_columns = st.columns(2)
+    if action_columns[0].button("Add person"):
+        rows.append(_new_participant())
+        st.rerun()
+    people = [
+        ParticipantInput(
+            participant_id=row["participant_id"],
+            display_name=row["display_name"],
+            role=row["role"],
+            organization=row["organization"],
+            attendance_status=row["attendance_status"],
+        )
+        for row in rows
+    ]
+    exported_yaml = export_people_yaml(people).encode("utf-8")
+    action_columns[1].download_button(
+        "Export people",
+        data=exported_yaml,
+        file_name="people.yaml",
+        mime="application/yaml",
+    )
+    return people
+
+
+def _format_duration(seconds: float) -> str:
+    """Format numeric seconds as MM:SS or H:MM:SS."""
+    whole_seconds = max(0, int(seconds))
+    hours, remainder = divmod(whole_seconds, 3600)
+    minutes, seconds = divmod(remainder, 60)
+    return f"{hours}:{minutes:02d}:{seconds:02d}" if hours else f"{minutes:02d}:{seconds:02d}"
+
+
+def _render_progress_status(
+    status_box: Any,
+    stage_table: Any,
+    states: dict[str, str],
+    timing: TimingSnapshot,
+    message: str,
+) -> None:
+    suffix = " …" if timing.running else ""
+    status_box.info(f"{message} — total runtime {_format_duration(timing.total_duration)}{suffix}")
+    stage_table.table(
+        [
+            {
+                "Stage": STAGE_LABELS[stage],
+                "Status": states[stage],
+                "Runtime": (
+                    _format_duration(timing.stage_durations[stage])
+                    + (" …" if timing.active_stage == stage else "")
+                    if stage in timing.stage_durations
+                    else ""
+                ),
+            }
+            for stage in STAGES
+        ]
+        + [
+            {
+                "Stage": "Total runtime",
+                "Status": "",
+                "Runtime": _format_duration(timing.total_duration) + suffix,
+            }
+        ]
+    )
+
+
+def _apply_progress_event(event: AppProgressEvent, states: dict[str, str]) -> None:
+    if event.stage in states:
+        states[event.stage] = "running" if event.status == "started" else event.status
+    if event.stage == "failed":
+        running = next((stage for stage, status in states.items() if status == "running"), None)
+        if running:
+            states[running] = "failed"
+
+
+def _run_with_live_progress(
+    service: MeetingProcessingService,
+    work: Callable[[Callable[[AppProgressEvent], None]], Any],
+    states: dict[str, str],
+    initial_message: str,
+) -> tuple[Any, dict[str, str], str, TimingSnapshot]:
+    """Run pipeline work while rendering the shared timer and progress events."""
+    status_box = st.empty()
+    stage_table = st.empty()
+    progress_slot = st.empty()
+    event_queue: Queue[AppProgressEvent] = Queue()
+    latest_message = initial_message
+    progress_bar = None
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        future = executor.submit(work, event_queue.put)
+        while not future.done():
+            try:
+                while True:
+                    event = event_queue.get_nowait()
+                    _apply_progress_event(event, states)
+                    latest_message = event.message or STAGE_LABELS.get(event.stage, event.stage)
+                    if event.progress is not None:
+                        if progress_bar is None:
+                            progress_bar = progress_slot.progress(0.0)
+                        progress_bar.progress(
+                            min(max(event.progress, 0.0), 1.0),
+                            text=(
+                                f"{STAGE_LABELS.get(event.stage, event.stage)}: "
+                                f"{event.progress:.0%}"
+                            ),
+                        )
+            except Empty:
+                pass
+            _render_progress_status(
+                status_box,
+                stage_table,
+                states,
+                service.timer.snapshot(),
+                latest_message,
+            )
+            time.sleep(0.2)
+        try:
+            result = future.result()
+        except Exception:
+            while not event_queue.empty():
+                event = event_queue.get_nowait()
+                _apply_progress_event(event, states)
+                latest_message = event.message or STAGE_LABELS.get(event.stage, event.stage)
+            service.timer.finish_run()
+            running_stage = next(
+                (stage for stage, status in states.items() if status == "running"),
+                None,
+            )
+            if running_stage is not None:
+                states[running_stage] = "failed"
+            snapshot = service.timer.snapshot()
+            _render_progress_status(status_box, stage_table, states, snapshot, latest_message)
+            st.session_state["processing_progress"] = (states.copy(), latest_message, snapshot)
+            raise
+    while not event_queue.empty():
+        event = event_queue.get_nowait()
+        _apply_progress_event(event, states)
+        latest_message = event.message or STAGE_LABELS.get(event.stage, event.stage)
+    running_stage = next((stage for stage, status in states.items() if status == "running"), None)
+    if running_stage is not None:
+        states[running_stage] = "completed" if getattr(result, "succeeded", True) else "failed"
+    snapshot = service.timer.snapshot()
+    _render_progress_status(status_box, stage_table, states, snapshot, latest_message)
+    st.session_state["processing_progress"] = (states.copy(), latest_message, snapshot)
+    return result, states, latest_message, snapshot
+
+
+def _remember_regeneration_timing(
+    states: dict[str, str], message: str, timing: TimingSnapshot
+) -> None:
+    st.session_state["regeneration_progress"] = (states.copy(), message, timing)
+
+
+def _speaker_options(
+    participants: tuple[str, ...], mappings: dict[str, str | None], speaker_label: str
+) -> list[str | None]:
+    """Reserve other speakers' participants while retaining this speaker's mapping."""
+    current = mappings.get(speaker_label)
+    reserved = {value for label, value in mappings.items() if label != speaker_label}
+    options: list[str | None] = [None]
+    options.extend(person for person in participants if person == current or person not in reserved)
+    if current is not None and current not in options:
+        options.append(current)
+    return options
+
+
+def _mapping_counts(
+    speaker_labels: tuple[str, ...], mappings: dict[str, str | None]
+) -> tuple[int, int, int]:
+    """Count only detected speakers, including explicit cleared selections."""
+    detected = len(speaker_labels)
+    assigned = sum(mappings.get(label) is not None for label in speaker_labels)
+    return detected, assigned, detected - assigned
+
+
+def _render_speaker_mapping(review: SpeakerMappingReview, run_name: str) -> dict[str, str]:
+    st.subheader("Identify diarized speakers")
+    st.caption(
+        "Confirm identities explicitly. Unmapped speakers remain anonymous; "
+        "the diarized source transcript is not modified."
+    )
+    participant_names = dict(review.participants)
+    # Read every widget before rendering so later speakers also reserve their person.
+    keys = {
+        speaker.speaker_label: f"speaker_mapping_{run_name}_{speaker.speaker_label}"
+        for speaker in review.speakers
+    }
+    mappings = {
+        label: st.session_state.get(key, review.current_mappings.get(label))
+        for label, key in keys.items()
+    }
+    detected, assigned, unassigned = _mapping_counts(tuple(keys), mappings)
+    st.markdown(f"**{detected} speakers detected · {assigned} assigned · {unassigned} unassigned**")
+    if unassigned:
+        st.warning(
+            "Some detected speakers have no confirmed participant mapping. "
+            "Check whether a participant is missing or speaker assignment is incomplete. "
+            "You can still generate a protocol with anonymous speakers."
+        )
+    selections: dict[str, str] = {}
+    for speaker in review.speakers:
+        label = speaker.speaker_label
+        current = mappings[label]
+        options = _speaker_options(tuple(participant_names), mappings, label)
+        st.session_state[keys[label]] = current
+        selected = st.selectbox(
+            f"{label} — Unassigned" if current is None else label,
+            options=options,
+            format_func=lambda value, names=participant_names: (
+                "Unmapped / Unknown" if value is None else names.get(value, value)
+            ),
+            key=keys[label],
+        )
+        if selected is not None:
+            selections[label] = selected
+        for excerpt in speaker.excerpts:
+            st.caption(f"“{excerpt}”")
+    return selections
+
+
+def _render_result() -> None:
+    outcome = st.session_state.outcome
+    if outcome is None:
+        return
+    st.divider()
+    st.header("Protocol result")
+    if retained_progress := st.session_state.get("processing_progress"):
+        states, message, timing = retained_progress
+        _render_progress_status(st.empty(), st.empty(), states, timing, message)
+    if not outcome.succeeded:
+        st.error(f"Processing failed during {outcome.failed_stage}: {outcome.error_message}")
+        if outcome.run_dir:
+            st.code(str(outcome.run_dir))
+            st.caption("Intermediate artifacts and run metadata were preserved here.")
+        return
+
+    awaiting_review = getattr(outcome, "awaiting_speaker_review", False)
+    if awaiting_review:
+        count = getattr(outcome, "detected_speaker_count", None)
+        detected = f"{count} speakers detected" if count is not None else "speakers detected"
+        st.info(
+            f"Diarization complete — {detected}. Assign speakers if desired, then generate the protocol."
+        )
+    else:
+        st.success("Processing completed. Review the generated protocol before use.")
+    st.caption(f"Run artifacts: {outcome.run_dir}")
+    if outcome.speaker_attribution_available is False:
+        st.warning(
+            "Speaker attribution was unavailable for this protocol because the "
+            "safe-budget fallback removed diarization labels from the prompt."
+        )
+    if message := st.session_state.pop("speaker_mapping_message", None):
+        st.success(message)
+    if not awaiting_review:
+        with st.expander("Original generated protocol", expanded=False):
+            st.markdown(outcome.original_protocol or "")
+        edited = st.text_area(
+            "Editable protocol",
+            key="edited_protocol",
+            height=500,
+            help="The original protocol.md remains unchanged.",
+        )
+        if st.button("Save edited protocol", type="primary"):
+            path = MeetingProcessingService.save_edited_protocol(outcome.run_dir, edited)
+            st.success(f"Saved edited protocol to {path}")
+
+    try:
+        service = MeetingProcessingService(AppSettings.from_environment(), MeetingLabGateway())
+        review = service.load_speaker_mapping_review(outcome.run_dir)
+    except (MeetingLabUnavailableError, OSError, ValueError) as exc:
+        st.warning(f"Speaker mapping is unavailable: {exc}")
+        return
+    if review is None or not review.speakers:
+        return
+
+    selections = _render_speaker_mapping(review, outcome.run_dir.name)
+
+    duplicate_assignments = len(selections.values()) != len(set(selections.values()))
+    if duplicate_assignments:
+        st.error("Each participant can be assigned to only one detected speaker.")
+    if st.button(
+        "Generate protocol" if awaiting_review else "Regenerate protocol with confirmed speakers",
+        disabled=duplicate_assignments,
+        type="primary",
+    ):
+        st.session_state.pop("regeneration_progress", None)
+        run_dir = outcome.run_dir
+        mapping_items = tuple(selections.items())
+        selected_profile = st.session_state.get("performance_profile", DEFAULT_PERFORMANCE_PROFILE)
+        states = {stage: "skipped" for stage in STAGES}
+        states["protocol_generation"] = "pending"
+        try:
+            regenerated, states, message, timing = _run_with_live_progress(
+                service,
+                lambda progress_sink: service.regenerate_protocol(
+                    run_dir,
+                    dict(mapping_items),
+                    progress_sink=progress_sink,
+                    performance_profile=selected_profile,
+                ),
+                states,
+                "Starting protocol generation" if awaiting_review else "Starting protocol regeneration",
+            )
+        except (OSError, RuntimeError, ValueError) as exc:
+            _remember_regeneration_timing(
+                states,
+                "Protocol regeneration failed",
+                service.timer.snapshot(),
+            )
+            st.error(f"Protocol generation failed: {exc}")
+        else:
+            _remember_regeneration_timing(states, message, timing)
+            st.session_state.outcome = regenerated
+            _queue_edited_protocol(regenerated.original_protocol or "")
+            st.session_state.speaker_mapping_message = (
+                "Speaker mappings saved and protocol generated."
+            )
+            st.rerun()
+
+
+def main() -> None:
+    st.set_page_config(page_title="Meeting Assistant", layout="wide")
+    _initialize_state()
+    st.title("Meeting Assistant")
+    st.caption("Create meeting context, run Meeting Lab, and review the protocol.")
+
+    settings = AppSettings.from_environment()
+    glossary = GlossaryRepository(settings.glossary_database)
+    glossary.initialize()
+    _render_glossary(glossary)
+
+    _render_run_input_import()
+
+    st.header("Meeting and audio")
+    audio = st.file_uploader(
+        "Audio recording",
+        type=["wav", "flac", "m4a"],
+        key=f"source_media_{st.session_state.source_media_widget_generation}",
+    )
+    imported_source_name = st.session_state.get("imported_source_file_name")
+    if audio is None and imported_source_name:
+        st.caption(
+            f"Previous source filename: {imported_source_name}. "
+            "Select the media file again before processing."
+        )
+    title = st.text_input("Meeting title", key="meeting_title")
+    description = st.text_area("Description / context", height=100, key="meeting_description")
+    metadata_columns = st.columns(3)
+    language = metadata_columns[0].selectbox(
+        "Meeting language",
+        options=["de", "en"],
+        key="meeting_language",
+        help="Language for both transcription and the generated protocol.",
+    )
+    has_date = metadata_columns[1].checkbox(
+        "Meeting date is known", value=True, key="meeting_has_date"
+    )
+    selected_date: date | None = (
+        metadata_columns[2].date_input("Meeting date", key="meeting_date") if has_date else None
+    )
+
+    participants = _render_participants()
+
+    st.header("Processing options")
+    performance_profile = st.selectbox(
+        "Performance profile",
+        options=PERFORMANCE_PROFILES,
+        format_func=str.title,
+        key="performance_profile",
+        help="Controls protocol-generation performance using an abstract runtime profile.",
+    )
+    audio_normalization = st.checkbox(
+        "Audio normalization",
+        value=True,
+        key="audio_normalization",
+        help=(
+            "Normalize speech loudness during preparation. WAV, FLAC, and M4A "
+            "are always converted to the canonical Meeting Lab audio format, "
+            "regardless of this setting."
+        ),
+    )
+    diarization_enabled = st.checkbox(
+        "Enable speaker diarization",
+        key="diarization_enabled",
+        help="Speaker labels remain anonymous; identities are never inferred.",
+    )
+
+    current_inputs = RunInputState(
+        title=title,
+        description=description,
+        language=language,
+        meeting_date=selected_date,
+        participants=tuple(participants),
+        audio_normalization=audio_normalization,
+        diarization_enabled=diarization_enabled,
+        source_file_name=(audio.name if audio is not None else imported_source_name),
+    )
+    st.download_button(
+        "Export inputs",
+        data=export_run_inputs(current_inputs).encode("utf-8"),
+        file_name=run_input_filename(title),
+        mime="application/json",
+        help="Download the current form configuration without source media or run results.",
+    )
+
+    if st.button("Start processing", type="primary", disabled=audio is None):
+        if not title.strip():
+            st.error("Meeting title is required.")
+        elif any(
+            not item.display_name.strip() or not item.participant_id.strip()
+            for item in participants
+        ):
+            st.error("Every person row needs a name and participant ID.")
+        else:
+            try:
+                service = MeetingProcessingService(settings, MeetingLabGateway(), glossary)
+                meeting = MeetingDetails(
+                    title=title,
+                    language=language,
+                    meeting_date=selected_date,
+                    description=description,
+                )
+                meeting_id = stable_id(title)
+                audio_path = service.preserve_upload(meeting_id, audio.name, audio)
+                st.session_state.pop("regeneration_progress", None)
+                st.session_state.pop("processing_progress", None)
+                st.header("Processing status")
+                states = {stage: "pending" for stage in STAGES}
+                if not diarization_enabled:
+                    states["diarization"] = "skipped"
+                outcome, _, _, _ = _run_with_live_progress(
+                    service,
+                    lambda progress_sink: service.process(
+                        audio_path,
+                        meeting,
+                        participants,
+                        ProcessingOptions(
+                            diarization_enabled=diarization_enabled,
+                            audio_normalization=audio_normalization,
+                            performance_profile=performance_profile,
+                        ),
+                        progress_sink=progress_sink,
+                    ),
+                    states,
+                    "Starting processing",
+                )
+                st.session_state.outcome = outcome
+                st.session_state.edited_protocol = outcome.original_protocol or ""
+                if outcome.succeeded:
+                    st.success("Processing completed.")
+                else:
+                    st.error(
+                        f"Processing failed during {outcome.failed_stage}: "
+                        f"{outcome.error_message} Artifacts were preserved."
+                    )
+            except (ConfigurationError, MeetingLabUnavailableError, ValueError) as exc:
+                st.error(str(exc))
+            except Exception as exc:
+                st.error(f"Unable to process meeting: {exc}")
+
+    _render_result()
+
+
+if __name__ == "__main__":
+    main()

tests/test_alpha_pair.py

@@ -0,0 +1,138 @@
+"""Paired Assistant/Lab smoke with only expensive media/model boundaries mocked."""
+
+import json
+import shutil
+from dataclasses import replace
+from unittest.mock import Mock
+
+import pytest
+
+from mka.application.meeting_service import MeetingProcessingService, ProcessingOptions
+from mka.integrations.meeting_lab import MeetingLabGateway
+from test_meeting_service import make_service, meeting, participants
+
+mvp = pytest.importorskip("src.meeting_lab.orchestration.mvp")
+from src.meeting_lab.audio import PreparedAudio  # noqa: E402
+from src.meeting_lab.diarization.backend import DiarizationResult  # noqa: E402
+from src.meeting_lab.llm.ollama import OllamaGeneration  # noqa: E402
+from src.meeting_lab.protocol.generate_direct_protocol import generate_direct_protocol  # noqa: E402
+from src.meeting_lab.transcription.whisper import TranscriptionResult  # noqa: E402
+
+
+def fake_prepare(source, destination, **kwargs):
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copyfile(source, destination)
+    return PreparedAudio(source, source.suffix[1:], destination, "ffmpeg", "ffmpeg")
+
+
+def fake_transcribe(audio, model, output, language, **kwargs):
+    output.mkdir(parents=True, exist_ok=True)
+    raw, transcript, text, metadata = [
+        output / n
+        for n in ("whisper_raw.json", "transcript.json", "transcript.txt", "runtime_metadata.json")
+    ]
+    raw.write_text("{}")
+    transcript.write_text(
+        json.dumps(
+            {
+                "text": "Lumini project discussion.",
+                "segments": [{"id": 0, "start": 0, "end": 2, "text": "Lumini project discussion."}],
+            }
+        )
+    )
+    text.write_text("Lumini project discussion.")
+    metadata.write_text("{}")
+    return TranscriptionResult(output, raw, transcript, text, metadata, 0.1)
+
+
+def fake_diarize(audio, output, mode, **kwargs):
+    output.mkdir(parents=True, exist_ok=True)
+    paths = [
+        output / name
+        for name in (
+            "metadata.json",
+            "diarization.rttm",
+            "exclusive_diarization.rttm",
+            "turns.json",
+            "exclusive_turns.json",
+        )
+    ]
+    metadata = {"speaker_count": 1, "runtime_seconds": 0.1}
+    paths[0].write_text(json.dumps(metadata))
+    for p in paths[1:]:
+        p.write_text("[]")
+    paths[-1].write_text(json.dumps([{"start": 0, "end": 2, "speaker_id": "SPEAKER_00"}]))
+    return DiarizationResult(output, *paths, metadata)
+
+
+@pytest.mark.parametrize("language,expected", [("de", "German"), ("en", "English")])
+@pytest.mark.parametrize(
+    "profile,threads", [("auto", None), ("fast", 16), ("efficient", 10), ("powersave", 4)]
+)
+def test_paired_anonymous_generation_and_mapped_regeneration(
+    tmp_path, monkeypatch, language, expected, profile, threads
+):
+    template, _ = make_service(tmp_path)
+    service = MeetingProcessingService(template.settings, MeetingLabGateway())
+    service.glossary.create("Luminy", "product", aliases=("Lumini",))
+    calls = []
+
+    def model_call(endpoint, model, prompt, **kwargs):
+        calls.append((prompt, kwargs))
+        return OllamaGeneration(
+            {"response": "# Mock protocol", "done": True}, "# Mock protocol", 0.1
+        )
+
+    def generate(transcript, context, **kwargs):
+        return generate_direct_protocol(
+            transcript, context, **kwargs, model_check=lambda *_: {}, generation_call=model_call
+        )
+
+    prepare = Mock(side_effect=fake_prepare)
+    transcribe = Mock(side_effect=fake_transcribe)
+    diarize = Mock(side_effect=fake_diarize)
+    monkeypatch.setattr(mvp, "prepare_audio", prepare)
+    monkeypatch.setattr(mvp, "transcribe_audio", transcribe)
+    monkeypatch.setattr(mvp, "diarize_audio", diarize)
+    monkeypatch.setattr(mvp, "generate_direct_protocol", generate)
+    audio = tmp_path / "sample.wav"
+    audio.write_bytes(b"mock audio")
+    outcome = service.process(
+        audio,
+        replace(meeting(), language=language),
+        participants(),
+        ProcessingOptions(diarization_enabled=True, performance_profile=profile),
+    )
+    assert outcome.succeeded
+    assert outcome.awaiting_speaker_review
+    assert prepare.call_args.kwargs["normalization_enabled"] is True
+    assert transcribe.call_args.args[3] == language
+    root = outcome.run_dir
+    assert calls == []
+    service.regenerate_protocol(root, {}, performance_profile=profile)
+    first = root / "protocol/generations/001"
+    before = {p.name: p.read_bytes() for p in first.iterdir()}
+    original = (root / "diarization/transcript_diarized.json").read_bytes()
+    first_meta = json.loads((first / "runtime_metadata.json").read_text())
+    assert first_meta["speaker_mapping"] == {}
+    assert first_meta["output_language"] == language
+    assert "SPEAKER_00" in (first / "transcript_input.txt").read_text()
+    assert "timing" in json.loads((root / "run_metadata.json").read_text())
+    service.regenerate_protocol(root, {"SPEAKER_00": "martin"}, performance_profile=profile)
+    assert prepare.call_count == transcribe.call_count == diarize.call_count == 1
+    assert (root / "diarization/transcript_diarized.json").read_bytes() == original
+    assert {p.name: p.read_bytes() for p in first.iterdir()} == before
+    second = root / "protocol/generations/002"
+    metadata = json.loads((second / "runtime_metadata.json").read_text())
+    assert metadata["speaker_mapping"] == {"SPEAKER_00": "martin"}
+    assert metadata["speaker_mapping_names"] == {"SPEAKER_00": "Martin"}
+    assert metadata["output_language"] == language
+    assert metadata["num_thread"] == threads
+    assert metadata["glossary_aliases_configured"]["Lumini"] == "Luminy"
+    assert metadata["glossary_replacements"] == []
+    assert "Lumini" in (second / "transcript_input.txt").read_text()
+    assert (root / "protocol.md").resolve() == second / "protocol.md"
+    for prompt, options in calls:
+        assert f"Write the meeting protocol in {expected}." in prompt
+        assert "Luminy" in prompt
+        assert options["num_thread"] == threads

tests/test_application_config.py

@@ -0,0 +1,76 @@
+from pathlib import Path
+
+import pytest
+
+from mka.application.config import AppSettings, ConfigurationError
+
+
+def test_settings_require_whisper_model() -> None:
+    settings = AppSettings(data_root=Path("runs"), whisper_model=None)
+
+    with pytest.raises(ConfigurationError, match="MKA_WHISPER_MODEL"):
+        settings.validate_for_processing()
+
+
+def test_settings_reject_missing_whisper_model(tmp_path: Path) -> None:
+    settings = AppSettings(
+        data_root=tmp_path / "runs",
+        whisper_model=tmp_path / "missing.bin",
+    )
+
+    with pytest.raises(ConfigurationError, match="does not exist"):
+        settings.validate_for_processing()
+
+
+def test_settings_accept_valid_native_configuration(tmp_path: Path) -> None:
+    model = tmp_path / "model.bin"
+    model.write_bytes(b"model")
+    settings = AppSettings(data_root=tmp_path / "runs", whisper_model=model)
+
+    settings.validate_for_processing()
+
+    assert settings.diarization_runtime == "native"
+    assert settings.diarization_container_args == ()
+
+
+def test_environment_defaults_to_no_diarization_container_args(monkeypatch) -> None:
+    monkeypatch.delenv("MKA_DIARIZATION_CONTAINER_ARGS", raising=False)
+
+    settings = AppSettings.from_environment()
+
+    assert settings.diarization_container_args == ()
+    assert settings.glossary_database == Path("data/database/glossary.sqlite3")
+
+
+def test_environment_configures_glossary_database(monkeypatch, tmp_path: Path) -> None:
+    database = tmp_path / "terms.sqlite3"
+    monkeypatch.setenv("MKA_GLOSSARY_DATABASE", str(database))
+
+    assert AppSettings.from_environment().glossary_database == database
+
+
+def test_environment_parses_multiple_ordered_container_args(monkeypatch) -> None:
+    monkeypatch.setenv(
+        "MKA_DIARIZATION_CONTAINER_ARGS",
+        '["--device=/dev/kfd", "--device=/dev/dri", "--group-add", "video"]',
+    )
+
+    settings = AppSettings.from_environment()
+
+    assert settings.diarization_container_args == (
+        "--device=/dev/kfd",
+        "--device=/dev/dri",
+        "--group-add",
+        "video",
+    )
+
+
+@pytest.mark.parametrize(
+    "value",
+    ["not-json", '"--flag"', '["valid", ""]', '["valid", 1]'],
+)
+def test_environment_rejects_invalid_container_args(monkeypatch, value: str) -> None:
+    monkeypatch.setenv("MKA_DIARIZATION_CONTAINER_ARGS", value)
+
+    with pytest.raises(ConfigurationError, match="JSON array"):
+        AppSettings.from_environment()

tests/test_glossary.py

@@ -0,0 +1,78 @@
+import sqlite3
+from pathlib import Path
+
+import pytest
+
+from mka.application.glossary import GlossaryConflictError, GlossaryRepository
+
+
+def repository(tmp_path: Path) -> GlossaryRepository:
+    result = GlossaryRepository(tmp_path / "database" / "glossary.sqlite3")
+    result.initialize()
+    return result
+
+
+def test_initialization_creates_empty_versioned_database(tmp_path: Path) -> None:
+    glossary = repository(tmp_path)
+
+    assert glossary.database_path.is_file()
+    assert glossary.list() == []
+    with sqlite3.connect(glossary.database_path) as connection:
+        assert connection.execute("PRAGMA user_version").fetchone()[0] == 1
+        tables = {
+            row[0]
+            for row in connection.execute("SELECT name FROM sqlite_master WHERE type = 'table'")
+        }
+    assert {"glossary_entries", "glossary_aliases"} <= tables
+
+
+def test_create_read_update_and_deactivate_with_multiple_aliases(tmp_path: Path) -> None:
+    glossary = repository(tmp_path)
+    created = glossary.create(
+        "Secugrid HS",
+        "product",
+        aliases=("Sikirgut", "Secugrid H S"),
+        description="Canonical core product name",
+    )
+
+    assert glossary.get(created.id).aliases == ("Secugrid H S", "Sikirgut")
+    assert glossary.list("sikir")[0].canonical_term == "Secugrid HS"
+
+    updated = glossary.update(
+        created.id,
+        "Secugrid HS",
+        "technical_term",
+        aliases=("Sekugrid HS",),
+        description="Updated",
+        is_active=False,
+    )
+
+    assert updated.category == "technical_term"
+    assert updated.aliases == ("Sekugrid HS",)
+    assert not updated.is_active
+    assert glossary.list(active_only=True) == []
+
+
+def test_terms_and_aliases_are_unique_case_insensitively_across_entries(
+    tmp_path: Path,
+) -> None:
+    glossary = repository(tmp_path)
+    glossary.create("PBAT", "acronym", aliases=("Polybutylene adipate terephthalate",))
+
+    with pytest.raises(GlossaryConflictError):
+        glossary.create("pbat", "material")
+    with pytest.raises(GlossaryConflictError):
+        glossary.create("Other", "other", aliases=("PBAT",))
+    with pytest.raises(GlossaryConflictError):
+        glossary.create("Polybutylene adipate terephthalate", "material")
+
+
+def test_delete_removes_entry_and_aliases(tmp_path: Path) -> None:
+    glossary = repository(tmp_path)
+    entry = glossary.create("Luminy", "product", aliases=("Lumini",))
+
+    glossary.delete(entry.id)
+
+    assert glossary.list() == []
+    with sqlite3.connect(glossary.database_path) as connection:
+        assert connection.execute("SELECT COUNT(*) FROM glossary_aliases").fetchone()[0] == 0

tests/test_glossary_yaml.py

@@ -0,0 +1,147 @@
+from pathlib import Path
+
+import pytest
+
+from mka.application.glossary import GlossaryRepository
+from mka.application.glossary_yaml import (
+    GlossaryYamlError,
+    export_glossary_yaml,
+    import_glossary_yaml,
+)
+
+
+def repository(tmp_path: Path) -> GlossaryRepository:
+    result = GlossaryRepository(tmp_path / "glossary.sqlite3")
+    result.initialize()
+    return result
+
+
+def test_round_trip_preserves_ids_aliases_and_active_state(tmp_path: Path) -> None:
+    source = repository(tmp_path / "source")
+    active = source.create(
+        "Größenmaß",
+        "technical_term",
+        aliases=("Groessenmass", "Größen-Maß"),
+        description="Canonical UTF-8 spelling",
+    )
+    inactive = source.create("Legacy Product", "product", is_active=False)
+
+    encoded = export_glossary_yaml(source.list()).encode("utf-8")
+    target = repository(tmp_path / "target")
+    target.create("Will be replaced", "other")
+    target.replace_all(import_glossary_yaml(encoded))
+
+    entries = target.list()
+    assert [entry.id for entry in entries] == [active.id, inactive.id]
+    assert entries[0].canonical_term == "Größenmaß"
+    assert entries[0].aliases == ("Groessenmass", "Größen-Maß")
+    assert entries[0].description == "Canonical UTF-8 spelling"
+    assert entries[0].is_active is True
+    assert entries[1].is_active is False
+
+
+def test_malformed_yaml_does_not_replace_existing_glossary(tmp_path: Path) -> None:
+    glossary = repository(tmp_path)
+    existing = glossary.create("PBAT", "acronym")
+
+    with pytest.raises(GlossaryYamlError, match="Malformed glossary YAML"):
+        import_glossary_yaml(b"version: 1\nglossary: [")
+
+    assert glossary.list() == [existing]
+
+
+@pytest.mark.parametrize(
+    "entries, message",
+    [
+        (
+            [
+                {
+                    "id": 1,
+                    "canonical_term": "PBAT",
+                    "aliases": [],
+                    "category": "acronym",
+                    "description": None,
+                    "active": True,
+                },
+                {
+                    "id": 2,
+                    "canonical_term": "pbat",
+                    "aliases": [],
+                    "category": "material",
+                    "description": None,
+                    "active": True,
+                },
+            ],
+            "duplicate canonical term or alias",
+        ),
+        (
+            [
+                {
+                    "id": 1,
+                    "canonical_term": "Secugrid",
+                    "aliases": ["Sikirgut", "SIKIRGUT"],
+                    "category": "product",
+                    "description": None,
+                    "active": True,
+                },
+            ],
+            "aliases must be unique",
+        ),
+        (
+            [
+                {
+                    "id": 1,
+                    "canonical_term": "Secugrid",
+                    "aliases": ["PBAT"],
+                    "category": "product",
+                    "description": None,
+                    "active": True,
+                },
+                {
+                    "id": 2,
+                    "canonical_term": "PBAT",
+                    "aliases": [],
+                    "category": "acronym",
+                    "description": None,
+                    "active": False,
+                },
+            ],
+            "duplicate canonical term or alias",
+        ),
+    ],
+)
+def test_duplicate_terms_and_aliases_are_rejected(
+    entries: list[dict[str, object]], message: str
+) -> None:
+    import yaml
+
+    content = yaml.safe_dump({"version": 1, "glossary": entries})
+
+    with pytest.raises(GlossaryYamlError, match=message):
+        import_glossary_yaml(content)
+
+
+def test_validation_failure_leaves_database_unchanged(tmp_path: Path) -> None:
+    glossary = repository(tmp_path)
+    existing = glossary.create("Existing", "other", aliases=("Existing alias",))
+    invalid = b"""version: 1
+glossary:
+  - id: 10
+    canonical_term: Duplicate
+    aliases: []
+    category: other
+    description: null
+    active: true
+  - id: 11
+    canonical_term: duplicate
+    aliases: []
+    category: other
+    description: null
+    active: false
+"""
+
+    with pytest.raises(GlossaryYamlError):
+        imported = import_glossary_yaml(invalid)
+        glossary.replace_all(imported)
+
+    assert glossary.list() == [existing]

tests/test_live_progress_ui.py

@@ -0,0 +1,88 @@
+"""Exercise the real executor and Streamlit reruns without model calls."""
+
+from pathlib import Path
+from threading import current_thread
+from types import SimpleNamespace
+
+import pytest
+import streamlit
+from streamlit.testing.v1 import AppTest
+
+from mka.application.meeting_service import AppProgressEvent, SpeakerMappingReview, SpeakerReview
+from mka.application.progress_timing import ProcessingTimer
+from mka.ui import streamlit_app as ui
+
+
+class GuardedStreamlit:
+    """Fail if a worker touches the UI session proxy, including callback reads."""
+
+    def __getattr__(self, name):
+        if name == "session_state" and current_thread().name.startswith("ThreadPoolExecutor"):
+            raise AssertionError("Worker accessed Streamlit session state")
+        return getattr(streamlit, name)
+
+
+@pytest.mark.parametrize("fails", [False, True])
+@pytest.mark.parametrize("mapped", [False, True])
+def test_regeneration_captures_ui_values_before_real_worker_and_retains_timing(
+    monkeypatch, fails, mapped
+):
+    outcome = SimpleNamespace(
+        succeeded=True,
+        run_dir=Path("/tmp/alpha-mapping-test"),
+        speaker_attribution_available=True,
+        original_protocol="Existing protocol",
+    )
+    review = SpeakerMappingReview((SpeakerReview("SPEAKER_00", ()),), (("a", "A"),), {})
+    calls = []
+
+    class Service:
+        def __init__(self, *args):
+            self.timer = ProcessingTimer()
+
+        def load_speaker_mapping_review(self, run_dir):
+            return review
+
+        def regenerate_protocol(self, run_dir, selections, *, progress_sink, performance_profile):
+            assert current_thread().name.startswith("ThreadPoolExecutor")
+            calls.append((run_dir, selections, performance_profile))
+            self.timer.start_run()
+            self.timer.start_stage("protocol_generation")
+            progress_sink(AppProgressEvent("protocol_generation", "started", 0.0))
+            if fails:
+                raise RuntimeError("model unavailable")
+            self.timer.finish_stage("protocol_generation")
+            self.timer.finish_run()
+            progress_sink(AppProgressEvent("protocol_generation", "completed", 0.0))
+            return outcome
+
+    monkeypatch.setattr(ui, "st", GuardedStreamlit())
+    monkeypatch.setattr(ui, "MeetingProcessingService", Service)
+    monkeypatch.setattr(ui.AppSettings, "from_environment", lambda: None)
+    monkeypatch.setattr(ui, "MeetingLabGateway", lambda: None)
+
+    def result_app(outcome):
+        import streamlit as st
+
+        from mka.ui.streamlit_app import _render_result
+
+        st.session_state.outcome = outcome
+        st.session_state.performance_profile = "fast"
+        _render_result()
+
+    app = AppTest.from_function(result_app, args=(outcome,)).run()
+    if mapped:
+        app.selectbox[0].select("a").run()
+    button = next(button for button in app.button if "Regenerate" in button.label)
+    assert not button.disabled
+    button.click().run()
+    assert not app.exception
+    assert calls == [(outcome.run_dir, {"SPEAKER_00": "a"} if mapped else {}, "fast")]
+    states, message, timing = app.session_state["processing_progress"]
+    assert states["protocol_generation"] == ("failed" if fails else "completed")
+    assert not timing.running
+    assert timing.total_duration >= timing.stage_durations["protocol_generation"] >= 0
+    app.run()
+    assert not app.exception
+    assert app.session_state["processing_progress"][2] == timing
+    assert any("total runtime" in item.value for item in app.info)

tests/test_meeting_service.py

@@ -0,0 +1,921 @@
+import json
+from dataclasses import dataclass, replace
+from datetime import date
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any
+
+import pytest
+import yaml
+
+from mka.application.config import AppSettings
+from mka.application.meeting_service import (
+    MeetingDetails,
+    MeetingProcessingService,
+    ParticipantInput,
+    ProcessingOptions,
+)
+from mka.application.progress_timing import ProcessingTimer
+
+
+@dataclass
+class FakeContext:
+    data: dict[str, Any]
+
+    @property
+    def meeting_id(self) -> str:
+        return self.data["meeting"]["meeting_id"]
+
+
+class FakeMeetingLab:
+    def __init__(self, run_dir: Path) -> None:
+        self.run_dir = run_dir
+        self.context_data: dict[str, Any] | None = None
+        self.config_values: dict[str, Any] | None = None
+        self.fail = False
+        self.regeneration: dict[str, Any] | None = None
+
+    def create_context(self, data: dict[str, Any]) -> FakeContext:
+        self.context_data = data
+        if not data["meeting"]["title"]:
+            raise ValueError("meeting.title must be present and non-empty")
+        participant_ids = [item["participant_id"] for item in data["participants"]]
+        if len(participant_ids) != len(set(participant_ids)):
+            raise ValueError("Duplicate participant_id")
+        return FakeContext(data)
+
+    def create_config(self, values: dict[str, Any]) -> dict[str, Any]:
+        self.config_values = values
+        return values
+
+    def run(self, config: Any, meeting_context: Any, progress_sink: Any) -> Any:
+        self.run_dir.mkdir(parents=True, exist_ok=True)
+        progress_sink(
+            SimpleNamespace(
+                stage="preparing",
+                status="started",
+                elapsed_seconds=0.1,
+                progress=None,
+                message=None,
+            )
+        )
+        progress_sink(
+            SimpleNamespace(
+                stage="preparing",
+                status="completed",
+                elapsed_seconds=0.2,
+                progress=None,
+                message=None,
+            )
+        )
+        progress_sink(
+            SimpleNamespace(
+                stage="transcription",
+                status="started",
+                elapsed_seconds=0.2,
+                progress=0.25,
+                message="transcribing",
+            )
+        )
+        if self.fail:
+            (self.run_dir / "run_metadata.json").write_text(
+                json.dumps(
+                    {
+                        "failure": {
+                            "stage": "whisper",
+                            "type": "TranscriptionError",
+                            "message": "model failed",
+                        }
+                    }
+                ),
+                encoding="utf-8",
+            )
+            progress_sink(
+                SimpleNamespace(
+                    stage="failed",
+                    status="failed",
+                    elapsed_seconds=0.3,
+                    progress=None,
+                    message="transcription: model failed",
+                )
+            )
+            return SimpleNamespace(exit_code=2, run_dir=self.run_dir, protocol_path=None)
+        if config["stop_after_diarization"]:
+            write_speaker_review_artifacts(self.run_dir)
+            (self.run_dir / "run_metadata.json").write_text(
+                json.dumps(
+                    {
+                        "status": "awaiting_speaker_review",
+                        "diarization": {"speaker_count": 2},
+                    }
+                ),
+                encoding="utf-8",
+            )
+            progress_sink(
+                SimpleNamespace(
+                    stage="diarization", status="completed", elapsed_seconds=0.3,
+                    progress=None, message=None,
+                )
+            )
+            return SimpleNamespace(exit_code=0, run_dir=self.run_dir, protocol_path=None)
+        protocol = self.run_dir / "protocol.md"
+        protocol.write_text("# Generated protocol\n", encoding="utf-8")
+        return SimpleNamespace(exit_code=0, run_dir=self.run_dir, protocol_path=protocol)
+
+    def regenerate_protocol(
+        self,
+        run_dir: Path,
+        meeting_context: Any,
+        progress_sink: Any,
+        **options: Any,
+    ) -> Any:
+        self.regeneration = {
+            "run_dir": run_dir,
+            "meeting_context": meeting_context,
+            "options": options,
+        }
+        progress_sink(
+            SimpleNamespace(
+                stage="protocol_generation",
+                status="started",
+                elapsed_seconds=0.0,
+                progress=None,
+                message=None,
+            )
+        )
+        protocol = Path(run_dir) / "protocol.md"
+        protocol.write_text("# Regenerated protocol\n", encoding="utf-8")
+        protocol_dir = Path(run_dir) / "protocol"
+        protocol_dir.mkdir(exist_ok=True)
+        (protocol_dir / "runtime_metadata.json").write_text(
+            json.dumps({"speaker_attribution_available": True}), encoding="utf-8"
+        )
+        return SimpleNamespace(exit_code=0, run_dir=run_dir, protocol_path=protocol)
+
+
+def make_service(tmp_path: Path) -> tuple[MeetingProcessingService, FakeMeetingLab]:
+    model = tmp_path / "model.bin"
+    model.write_bytes(b"model")
+    gateway = FakeMeetingLab(tmp_path / "backend-run")
+    settings = AppSettings(
+        data_root=tmp_path / "meetings",
+        whisper_model=model,
+        glossary_database=tmp_path / "glossary.sqlite3",
+        whisper_executable="/opt/whisper-cli",
+        protocol_model="test:model",
+        diarization_mode="gpu",
+    )
+    return MeetingProcessingService(settings, gateway), gateway
+
+
+def meeting() -> MeetingDetails:
+    return MeetingDetails(
+        title="Architecture Review",
+        language="de",
+        meeting_date=date(2026, 8, 23),
+        description="Review the MVP.",
+    )
+
+
+def participants() -> list[ParticipantInput]:
+    return [
+        ParticipantInput(
+            participant_id="martin",
+            display_name="Martin",
+            role="Project lead",
+            organization="Engineering",
+        ),
+        ParticipantInput(
+            participant_id="alex",
+            display_name="Alex",
+            organization="Engineering",
+        ),
+    ]
+
+
+def write_speaker_review_artifacts(run_dir: Path) -> Path:
+    diarization_dir = run_dir / "diarization"
+    context_dir = run_dir / "context"
+    diarization_dir.mkdir(parents=True)
+    context_dir.mkdir()
+    transcript_path = diarization_dir / "transcript_diarized.json"
+    transcript_path.write_text(
+        json.dumps(
+            {
+                "speaker_labels_anonymous": True,
+                "segments": [
+                    {
+                        "speaker_id": "SPEAKER_01",
+                        "text": "I will prepare all raw materials before Wednesday.",
+                    },
+                    {"speaker_id": "SPEAKER_00", "text": "Yes."},
+                    {
+                        "speaker_id": "SPEAKER_00",
+                        "text": "We will run the production trial on Wednesday.",
+                    },
+                    {
+                        "speaker_id": "SPEAKER_00",
+                        "text": "The trial requires the complete production team.",
+                    },
+                    {"speaker_id": None, "text": "Unassigned text."},
+                ],
+            }
+        ),
+        encoding="utf-8",
+    )
+    (context_dir / "meeting_context.yaml").write_text(
+        json.dumps(
+            {
+                "schema_version": "1",
+                "meeting": {
+                    "meeting_id": "speaker-review",
+                    "title": "Speaker review",
+                    "language": "en",
+                },
+                "participants": [
+                    {"participant_id": "martin", "display_name": "Martin"},
+                    {"participant_id": "anna", "display_name": "Anna"},
+                ],
+                "speaker_mappings": {},
+                "mentioned_people": [],
+                "organization": {"departments": []},
+                "known_entities": {},
+            }
+        ),
+        encoding="utf-8",
+    )
+    return transcript_path
+
+
+def write_custom_speaker_review_artifacts(
+    run_dir: Path, segments: list[dict[str, str | None]]
+) -> Path:
+    """Write custom diarized segments using the standard review context fixture."""
+    transcript_path = write_speaker_review_artifacts(run_dir)
+    transcript_path.write_text(
+        json.dumps({"speaker_labels_anonymous": True, "segments": segments}),
+        encoding="utf-8",
+    )
+    return transcript_path
+
+
+def test_build_context_uses_actual_v1_shape(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+
+    context = service.build_context(meeting(), participants())
+
+    assert context.meeting_id == "architecture-review"
+    assert gateway.context_data is not None
+    assert gateway.context_data["meeting"]["date"] == "2026-08-23"
+    assert gateway.context_data["meeting"]["notes"] == "Review the MVP."
+    assert gateway.context_data["participants"][0] == {
+        "participant_id": "martin",
+        "display_name": "Martin",
+        "aliases": [],
+        "role": "Project lead",
+        "department": "engineering",
+        "attendance_status": "present",
+        "notes": None,
+    }
+    assert gateway.context_data["organization"]["departments"] == [
+        {"id": "engineering", "name": "Engineering", "aliases": []}
+    ]
+
+
+def test_build_context_preserves_explicit_speaker_mapping(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+
+    service.build_context(meeting(), participants(), {"SPEAKER_00": "martin"})
+
+    assert gateway.context_data is not None
+    assert gateway.context_data["speaker_mappings"] == {"SPEAKER_00": "martin"}
+
+
+def test_build_context_includes_only_active_authoritative_glossary_terms(
+    tmp_path: Path,
+) -> None:
+    service, gateway = make_service(tmp_path)
+    service.glossary.create("Secugrid HS", "product", aliases=("Sikirgut", "Secugrid H S"))
+    inactive = service.glossary.create("Old Name", "other")
+    service.glossary.set_active(inactive.id, False)
+
+    service.build_context(meeting(), participants())
+
+    assert gateway.context_data is not None
+    assert gateway.context_data["known_entities"] == {
+        "Authoritative terminology": ["Secugrid HS (aliases: Secugrid H S, Sikirgut)"]
+    }
+    rules = gateway.context_data["context_rules"]
+    assert "do not invent matches" in rules["glossary_canonical_spelling"]
+    assert "inside compounds" in rules["glossary_core_terms"]
+    assert "Old Name" not in str(gateway.context_data)
+
+
+def test_glossary_is_rendered_into_meeting_lab_protocol_context(tmp_path: Path) -> None:
+    meeting_context = pytest.importorskip("src.meeting_lab.models.meeting_context")
+    service, gateway = make_service(tmp_path)
+    service.glossary.create("PBAT", "acronym", aliases=("P B A T",))
+    context = service.build_context(meeting(), participants())
+
+    real_context = meeting_context.create_meeting_context(context.data)
+    prompt_context = meeting_context.render_meeting_context_for_prompt(real_context)
+
+    assert "Authoritative terminology: PBAT (aliases: P B A T)" in prompt_context
+    assert "Use canonical glossary spellings" in prompt_context
+    assert "use canonical core terms inside compounds" in prompt_context
+
+
+def test_build_context_translates_mentioned_only_person(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    people = participants() + [
+        ParticipantInput(
+            participant_id="sam",
+            display_name="Sam",
+            attendance_status="mentioned_only",
+        )
+    ]
+
+    service.build_context(meeting(), people)
+
+    assert gateway.context_data is not None
+    assert [item["participant_id"] for item in gateway.context_data["participants"]] == [
+        "martin",
+        "alex",
+    ]
+    assert gateway.context_data["mentioned_people"] == [
+        {
+            "person_id": "sam",
+            "display_name": "Sam",
+            "aliases": [],
+            "role": None,
+            "department": None,
+            "attendance_status": "mentioned_only",
+            "notes": None,
+        }
+    ]
+
+
+@pytest.mark.parametrize("language", ["de", "en"])
+def test_process_translates_configuration_and_disables_diarization(
+    tmp_path: Path,
+    language: str,
+) -> None:
+    service, gateway = make_service(tmp_path)
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    outcome = service.process(
+        audio,
+        replace(meeting(), language=language),
+        participants(),
+        ProcessingOptions(diarization_enabled=False),
+    )
+
+    assert outcome.succeeded
+    assert gateway.config_values is not None
+    assert gateway.config_values["language"] == language
+    assert service.build_context(replace(meeting(), language=language), participants()).data[
+        "meeting"
+    ]["language"] == language
+    assert gateway.config_values["diarization"] == "off"
+    assert gateway.config_values["model"] == "test:model"
+    assert gateway.config_values["protocol_num_ctx"] == 32_768
+    assert gateway.config_values["protocol_safe_input_token_budget"] == 29_000
+    assert gateway.config_values["whisper_executable"] == "/opt/whisper-cli"
+    assert gateway.config_values["ffmpeg_executable"] == "ffmpeg"
+    assert gateway.config_values["audio_normalization"] is True
+    assert gateway.config_values["diarization_runtime"] == "native"
+    assert gateway.config_values["diarization_container_args"] == ()
+    assert gateway.config_values["output_root"] == (
+        tmp_path / "meetings" / "architecture-review" / "runs"
+    )
+
+
+def test_process_propagates_disabled_audio_normalization(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    audio = tmp_path / "meeting.m4a"
+    audio.write_bytes(b"audio")
+
+    outcome = service.process(
+        audio,
+        meeting(),
+        participants(),
+        ProcessingOptions(audio_normalization=False),
+    )
+
+    assert outcome.succeeded
+    assert gateway.config_values is not None
+    assert gateway.config_values["audio_normalization"] is False
+
+
+def test_processing_options_default_to_audio_normalization_on() -> None:
+    assert ProcessingOptions().audio_normalization is True
+
+
+def test_process_propagates_enabled_diarization_and_progress(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    audio = tmp_path / "meeting.flac"
+    audio.write_bytes(b"audio")
+    events = []
+
+    outcome = service.process(
+        audio,
+        meeting(),
+        participants(),
+        ProcessingOptions(diarization_enabled=True),
+        progress_sink=events.append,
+    )
+
+    assert gateway.config_values is not None
+    assert gateway.config_values["diarization"] == "gpu"
+    assert gateway.config_values["stop_after_diarization"] is True
+    assert outcome.awaiting_speaker_review is True
+    assert outcome.detected_speaker_count == 2
+    assert [(event.stage, event.status) for event in events[:2]] == [
+        ("preparing", "started"),
+        ("preparing", "completed"),
+    ]
+    assert events[2].progress == 0.25
+
+
+def test_diarization_checkpoint_defers_protocol_but_disabled_run_does_not(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    checkpoint = service.process(
+        audio, meeting(), participants(), ProcessingOptions(diarization_enabled=True)
+    )
+    assert checkpoint.succeeded
+    assert checkpoint.awaiting_speaker_review
+    assert checkpoint.original_protocol is None
+    assert checkpoint.protocol_path is None
+
+    normal_root = tmp_path / "without-diarization"
+    normal_root.mkdir()
+    normal_service, normal_gateway = make_service(normal_root)
+    normal_audio = normal_root / "meeting.wav"
+    normal_audio.write_bytes(b"audio")
+    normal = normal_service.process(
+        normal_audio, meeting(), participants(), ProcessingOptions(diarization_enabled=False)
+    )
+    assert normal.succeeded
+    assert not normal.awaiting_speaker_review
+    assert normal.protocol_path is not None
+    assert normal_gateway.config_values["stop_after_diarization"] is False
+
+
+def test_process_propagates_ordered_diarization_container_args(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    service.settings = replace(
+        service.settings,
+        diarization_runtime="container",
+        diarization_container_image="runtime/image:tag",
+        diarization_container_args=(
+            "--network=host",
+            "--label",
+            "meeting-test",
+        ),
+    )
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    outcome = service.process(
+        audio,
+        meeting(),
+        participants(),
+        ProcessingOptions(diarization_enabled=True),
+    )
+
+    assert outcome.succeeded
+    assert gateway.config_values is not None
+    assert gateway.config_values["diarization_container_args"] == (
+        "--network=host",
+        "--label",
+        "meeting-test",
+    )
+
+
+def test_result_and_user_edit_are_preserved_separately(tmp_path: Path) -> None:
+    service, _ = make_service(tmp_path)
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    outcome = service.process(audio, meeting(), participants(), ProcessingOptions())
+    edited_path = service.save_edited_protocol(outcome.run_dir, "# Reviewed\n")
+
+    assert outcome.original_protocol == "# Generated protocol\n"
+    assert outcome.protocol_path.read_text(encoding="utf-8") == "# Generated protocol\n"
+    assert edited_path.read_text(encoding="utf-8") == "# Reviewed\n"
+
+
+def test_failure_reports_stage_and_preserves_run_dir(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    gateway.fail = True
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    outcome = service.process(audio, meeting(), participants(), ProcessingOptions())
+
+    assert not outcome.succeeded
+    assert outcome.failed_stage == "transcription"
+    assert outcome.error_message == "TranscriptionError: model failed"
+    assert outcome.run_dir == gateway.run_dir
+    assert (gateway.run_dir / "run_metadata.json").is_file()
+
+
+def test_speaker_review_lists_detected_labels_participants_and_excerpts(
+    tmp_path: Path,
+) -> None:
+    service, gateway = make_service(tmp_path)
+    source = write_speaker_review_artifacts(gateway.run_dir)
+
+    review = service.load_speaker_mapping_review(gateway.run_dir, excerpts_per_speaker=2)
+
+    assert review is not None
+    assert [speaker.speaker_label for speaker in review.speakers] == [
+        "SPEAKER_00",
+        "SPEAKER_01",
+    ]
+    assert review.speakers[0].excerpts == (
+        "We will run the production trial on Wednesday.",
+        "The trial requires the complete production team.",
+    )
+    assert review.participants == (("martin", "Martin"), ("anna", "Anna"))
+    assert review.current_mappings == {}
+    assert "SPEAKER_00" in source.read_text(encoding="utf-8")
+
+
+def test_speaker_review_prefers_content_rich_excerpts_over_acknowledgements_and_questions(
+    tmp_path: Path,
+) -> None:
+    service, gateway = make_service(tmp_path)
+    acknowledgement = "Okay, that sounds good."
+    question = "Could we continue now?"
+    rich_excerpts = [
+        "I will complete the Atlas deployment in Berlin on 14 September with the database team.",
+        "I recommend moving the Orion product launch because the supplier test is incomplete.",
+        "We decided that I will own the Kubernetes migration for the payments service.",
+        "My experience with the Frankfurt factory rollout suggests a two-week validation period.",
+        "I will present the security audit results to the Apollo project steering group.",
+        "We should reserve twelve hours for the API integration and production monitoring.",
+    ]
+    write_custom_speaker_review_artifacts(
+        gateway.run_dir,
+        [{"speaker_id": "SPEAKER_00", "text": text} for text in [acknowledgement, question, *rich_excerpts]],
+    )
+
+    review = service.load_speaker_mapping_review(gateway.run_dir)
+
+    assert review is not None
+    excerpts = review.speakers[0].excerpts
+    assert len(excerpts) == 6
+    assert acknowledgement not in excerpts
+    assert question not in excerpts
+    assert set(excerpts) == set(rich_excerpts)
+
+
+def test_speaker_review_avoids_near_duplicate_excerpts(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    primary = "I will lead the Atlas API migration for the Berlin platform team next Monday."
+    duplicate = "I will lead the Atlas API migration for the Berlin platform group next Monday."
+    alternatives = [
+        "I recommend documenting the supplier quality decision in the project register.",
+        "My team will complete the production calibration before the factory trial.",
+        "I will present the customer research findings at the Munich planning workshop.",
+        "Our database monitoring threshold needs approval from the operations group.",
+        "I will coordinate the Orion release checklist with the support organization.",
+    ]
+    write_custom_speaker_review_artifacts(
+        gateway.run_dir,
+        [{"speaker_id": "SPEAKER_00", "text": text} for text in [primary, duplicate, *alternatives]],
+    )
+
+    review = service.load_speaker_mapping_review(gateway.run_dir)
+
+    assert review is not None
+    excerpts = review.speakers[0].excerpts
+    assert primary in excerpts
+    assert duplicate not in excerpts
+    assert set(excerpts) == {primary, *alternatives}
+
+
+def test_speaker_review_spreads_selected_excerpts_across_meeting_positions(
+    tmp_path: Path,
+) -> None:
+    service, gateway = make_service(tmp_path)
+    positions = {
+        0: "I will lead the Atlas deployment review with the engineering team marker alpha.",
+        1: "I will lead the Atlas deployment review with the engineering team marker bravo.",
+        6: "I will lead the Atlas deployment review with the engineering team marker charlie.",
+        7: "I will lead the Atlas deployment review with the engineering team marker delta.",
+        12: "I will lead the Atlas deployment review with the engineering team marker echo.",
+        13: "I will lead the Atlas deployment review with the engineering team marker foxtrot.",
+        18: "I will lead the Atlas deployment review with the engineering team marker golf.",
+        19: "I will lead the Atlas deployment review with the engineering team marker hotel.",
+    }
+    segments = [
+        {
+            "speaker_id": "SPEAKER_00" if index in positions else "SPEAKER_01",
+            "text": positions.get(index, "Acknowledged."),
+        }
+        for index in range(20)
+    ]
+    write_custom_speaker_review_artifacts(gateway.run_dir, segments)
+
+    review = service.load_speaker_mapping_review(gateway.run_dir)
+
+    assert review is not None
+    assert review.speakers[0].excerpts == tuple(positions[index] for index in (0, 1, 6, 7, 12, 18))
+
+
+def test_protocol_only_regeneration_persists_mappings_without_rewriting_transcript(
+    tmp_path: Path,
+) -> None:
+    service, gateway = make_service(tmp_path)
+    service.glossary.create("ENLYZE", "organization", aliases=("Enlyse",))
+    source = write_speaker_review_artifacts(gateway.run_dir)
+    original_source = source.read_bytes()
+    events = []
+
+    outcome = service.regenerate_protocol(
+        gateway.run_dir,
+        {"SPEAKER_00": "martin"},
+        progress_sink=events.append,
+    )
+
+    assert outcome.succeeded
+    assert outcome.original_protocol == "# Regenerated protocol\n"
+    assert outcome.speaker_attribution_available is True
+    assert gateway.context_data is not None
+    assert gateway.context_data["speaker_mappings"] == {"SPEAKER_00": "martin"}
+    assert gateway.context_data["known_entities"] == {
+        "Authoritative terminology": ["ENLYZE (aliases: Enlyse)"]
+    }
+    assert gateway.regeneration is not None
+    assert gateway.regeneration["meeting_context"].data["speaker_mappings"] == {
+        "SPEAKER_00": "martin"
+    }
+    assert gateway.regeneration["options"]["protocol_num_ctx"] == 32_768
+    assert gateway.regeneration["options"]["glossary_aliases"] == {"Enlyse": "ENLYZE"}
+    assert events[0].stage == "protocol_generation"
+    assert source.read_bytes() == original_source
+
+
+def test_protocol_regeneration_allows_unmapped_and_rejects_duplicate_participant(
+    tmp_path: Path,
+) -> None:
+    service, gateway = make_service(tmp_path)
+    write_speaker_review_artifacts(gateway.run_dir)
+
+    outcome = service.regenerate_protocol(gateway.run_dir, {})
+
+    assert outcome.succeeded
+    assert gateway.context_data is not None
+    assert gateway.context_data["speaker_mappings"] == {}
+
+    with pytest.raises(ValueError, match="only one speaker"):
+        service.regenerate_protocol(
+            gateway.run_dir,
+            {"SPEAKER_00": "martin", "SPEAKER_01": "martin"},
+        )
+
+
+@pytest.mark.parametrize(
+    "mappings",
+    [
+        {"SPEAKER_00": "martin"},
+        {"SPEAKER_00": "martin", "SPEAKER_01": "anna"},
+    ],
+)
+def test_protocol_generation_uses_partial_and_complete_confirmed_mappings(
+    tmp_path: Path, mappings: dict[str, str]
+) -> None:
+    service, gateway = make_service(tmp_path)
+    write_speaker_review_artifacts(gateway.run_dir)
+
+    outcome = service.regenerate_protocol(gateway.run_dir, mappings)
+
+    assert outcome.succeeded
+    assert gateway.regeneration is not None
+    assert gateway.regeneration["meeting_context"].data["speaker_mappings"] == mappings
+
+
+def test_failed_protocol_generation_keeps_checkpoint_mapping_for_retry(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    source = write_speaker_review_artifacts(gateway.run_dir)
+    original_source = source.read_bytes()
+    original_regeneration = gateway.regenerate_protocol
+
+    def fail_regeneration(*args, **kwargs):
+        raise RuntimeError("model unavailable")
+
+    gateway.regenerate_protocol = fail_regeneration
+    with pytest.raises(RuntimeError, match="model unavailable"):
+        service.regenerate_protocol(gateway.run_dir, {"SPEAKER_00": "martin"})
+
+    persisted = yaml.safe_load(
+        (gateway.run_dir / "context" / "meeting_context.yaml").read_text(encoding="utf-8")
+    )
+    assert persisted["speaker_mappings"] == {"SPEAKER_00": "martin"}
+    gateway.regenerate_protocol = original_regeneration
+    retry = service.regenerate_protocol(gateway.run_dir, {"SPEAKER_00": "martin"})
+    assert retry.succeeded
+    assert source.read_bytes() == original_source
+
+
+def test_fallback_attribution_loss_is_read_from_runtime_metadata(tmp_path: Path) -> None:
+    run_dir = tmp_path / "run"
+    protocol_dir = run_dir / "protocol"
+    protocol_dir.mkdir(parents=True)
+    (protocol_dir / "runtime_metadata.json").write_text(
+        json.dumps(
+            {
+                "speaker_attribution_available": False,
+                "speaker_attribution_loss_reason": "plain_transcript_fallback",
+            }
+        ),
+        encoding="utf-8",
+    )
+
+    assert MeetingProcessingService._speaker_attribution_available(run_dir) is False
+
+
+def test_uploaded_source_is_preserved_in_meeting_directory(tmp_path: Path) -> None:
+    service, _ = make_service(tmp_path)
+    source = SimpleNamespace(getbuffer=lambda: b"source audio")
+
+    destination = service.preserve_upload("meeting-1", "../unsafe.wav", source)
+
+    assert destination.parent == tmp_path / "meetings" / "meeting-1" / "uploads"
+    assert destination.name.endswith("_unsafe.wav")
+    assert destination.read_bytes() == b"source audio"
+
+
+@pytest.mark.parametrize(
+    ("profile", "threads"),
+    [("fast", 16), ("efficient", 10), ("powersave", 4)],
+)
+def test_process_resolves_profile_for_protocol_generation(
+    tmp_path: Path, profile: str, threads: int
+) -> None:
+    service, gateway = make_service(tmp_path)
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    service.process(
+        audio,
+        meeting(),
+        participants(),
+        ProcessingOptions(performance_profile=profile),
+    )
+
+    assert gateway.config_values is not None
+    assert gateway.config_values["protocol_num_thread"] == threads
+
+
+def test_process_defaults_to_backend_thread_selection(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    service.process(audio, meeting(), participants(), ProcessingOptions())
+
+    assert gateway.config_values is not None
+    assert gateway.config_values["protocol_num_thread"] is None
+
+
+def test_protocol_regeneration_propagates_explicit_profile(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    write_speaker_review_artifacts(gateway.run_dir)
+
+    service.regenerate_protocol(
+        gateway.run_dir,
+        {},
+        performance_profile="fast",
+    )
+
+    assert gateway.regeneration is not None
+    assert gateway.regeneration["options"]["protocol_num_thread"] == 16
+
+
+def test_process_passes_only_active_explicit_glossary_aliases(tmp_path: Path) -> None:
+    service, gateway = make_service(tmp_path)
+    service.glossary.create("Luminy", "product", aliases=("Lumini",))
+    inactive = service.glossary.create("PBAT", "acronym", aliases=("PBRT",))
+    service.glossary.set_active(inactive.id, False)
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    service.process(audio, meeting(), participants(), ProcessingOptions())
+
+    assert gateway.config_values is not None
+    assert gateway.config_values["glossary_aliases"] == {"Lumini": "Luminy"}
+
+
+@pytest.mark.parametrize("fails", [False, True])
+def test_process_persists_completed_and_failed_timings(tmp_path: Path, fails: bool) -> None:
+    service, gateway = make_service(tmp_path)
+    gateway.fail = fails
+    values = iter([0.0, 1.0, 9.0, 10.0, 20.0])
+    service.timer = ProcessingTimer(lambda: next(values))
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+
+    outcome = service.process(audio, meeting(), participants(), ProcessingOptions())
+
+    metadata = json.loads((gateway.run_dir / "run_metadata.json").read_text(encoding="utf-8"))
+    assert metadata["timing"] == {
+        "stages_seconds": {"preparing": 8.0, "transcription": 10.0},
+        "total_seconds": 20.0,
+    }
+    assert outcome.succeeded is not fails
+    if fails:
+        assert metadata["failure"]["type"] == "TranscriptionError"
+
+
+def test_regeneration_starts_fresh_timer_and_retains_final_duration(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    service, gateway = make_service(tmp_path)
+    write_speaker_review_artifacts(gateway.run_dir)
+    clock = SimpleNamespace(value=10.0)
+    service.timer = ProcessingTimer(lambda: clock.value)
+    audio = tmp_path / "meeting.wav"
+    audio.write_bytes(b"audio")
+    service.process(audio, meeting(), participants(), ProcessingOptions())
+
+    clock.value = 100.0
+    observed = []
+
+    def regenerate(run_dir: Path, context: Any, progress_sink: Any, **options: Any) -> Any:
+        progress_sink(
+            SimpleNamespace(
+                stage="protocol_generation",
+                status="started",
+                elapsed_seconds=0.0,
+                progress=None,
+                message=None,
+            )
+        )
+        clock.value = 105.0
+        observed.append(service.timer.snapshot())
+        progress_sink(
+            SimpleNamespace(
+                stage="protocol_generation",
+                status="completed",
+                elapsed_seconds=5.0,
+                progress=None,
+                message=None,
+            )
+        )
+        clock.value = 109.0
+        protocol = run_dir / "protocol.md"
+        protocol.write_text("# Regenerated protocol\n", encoding="utf-8")
+        return SimpleNamespace(run_dir=run_dir, protocol_path=protocol)
+
+    monkeypatch.setattr(gateway, "regenerate_protocol", regenerate)
+
+    service.regenerate_protocol(gateway.run_dir, {"SPEAKER_00": "martin"})
+    final = service.timer.snapshot()
+
+    assert observed[0].active_stage == "protocol_generation"
+    assert observed[0].stage_durations == {"protocol_generation": 5.0}
+    assert observed[0].total_duration == 5.0
+    assert observed[0].running is True
+    assert final.stage_durations == {"protocol_generation": 5.0}
+    assert final.total_duration == 9.0
+    assert final.running is False
+
+
+def test_failed_regeneration_freezes_active_and_total_durations(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    service, gateway = make_service(tmp_path)
+    write_speaker_review_artifacts(gateway.run_dir)
+    clock = SimpleNamespace(value=20.0)
+    service.timer = ProcessingTimer(lambda: clock.value)
+
+    def fail_regeneration(run_dir: Path, context: Any, progress_sink: Any, **options: Any) -> Any:
+        progress_sink(
+            SimpleNamespace(
+                stage="protocol_generation",
+                status="started",
+                elapsed_seconds=0.0,
+                progress=None,
+                message=None,
+            )
+        )
+        clock.value = 27.0
+        raise RuntimeError("generation failed")
+
+    monkeypatch.setattr(gateway, "regenerate_protocol", fail_regeneration)
+
+    with pytest.raises(RuntimeError, match="generation failed"):
+        service.regenerate_protocol(gateway.run_dir, {})
+    clock.value = 99.0
+    final = service.timer.snapshot()
+
+    assert final.stage_durations == {"protocol_generation": 7.0}
+    assert final.total_duration == 7.0
+    assert final.running is False

tests/test_people_yaml.py

@@ -0,0 +1,127 @@
+import pytest
+
+from mka.application.meeting_service import ParticipantInput
+from mka.application.people_yaml import (
+    PeopleYamlError,
+    export_people_yaml,
+    import_people_yaml,
+)
+
+
+def sample_people() -> list[ParticipantInput]:
+    return [
+        ParticipantInput(
+            participant_id="participant-35c1528c",
+            display_name="Martin Tazl",
+            role="Beirat",
+            organization="Verwaltungsbeirat",
+            attendance_status="present",
+        ),
+        ParticipantInput(
+            participant_id="participant-56aae811",
+            display_name="Norbert Hebbelmann",
+            role="Gast",
+            organization="Eigentümergemeinschaft",
+            attendance_status="mentioned_only",
+        ),
+    ]
+
+
+def test_export_import_round_trip_preserves_all_people_fields() -> None:
+    people = sample_people()
+
+    exported = export_people_yaml(people)
+
+    assert exported.startswith("version: 1\npeople:\n")
+    assert import_people_yaml(exported) == people
+    assert export_people_yaml(import_people_yaml(exported)) == exported
+
+
+def test_import_preserves_stable_ids_roles_organizations_and_attendance() -> None:
+    imported = import_people_yaml(export_people_yaml(sample_people()))
+
+    assert [person.participant_id for person in imported] == [
+        "participant-35c1528c",
+        "participant-56aae811",
+    ]
+    assert imported[0].role == "Beirat"
+    assert imported[1].organization == "Eigentümergemeinschaft"
+    assert [person.attendance_status for person in imported] == [
+        "present",
+        "mentioned_only",
+    ]
+
+
+@pytest.mark.parametrize("content", ["people: [", b"\xff\xfe"])
+def test_malformed_yaml_is_rejected(content: str | bytes) -> None:
+    with pytest.raises(PeopleYamlError, match="Malformed People YAML"):
+        import_people_yaml(content)
+
+
+def test_unsupported_version_is_rejected() -> None:
+    with pytest.raises(PeopleYamlError, match="Unsupported People YAML version"):
+        import_people_yaml("version: 2\npeople: []\n")
+
+
+def test_missing_id_is_rejected() -> None:
+    content = """\
+version: 1
+people:
+  - display_name: Martin Tazl
+    attendance_status: present
+"""
+
+    with pytest.raises(PeopleYamlError, match="participant_id"):
+        import_people_yaml(content)
+
+
+def test_invalid_id_is_rejected_without_generating_a_replacement() -> None:
+    content = """\
+version: 1
+people:
+  - participant_id: invalid id
+    display_name: Martin
+    attendance_status: present
+"""
+
+    with pytest.raises(PeopleYamlError, match="invalid participant_id"):
+        import_people_yaml(content)
+
+
+def test_duplicate_id_is_rejected() -> None:
+    content = """\
+version: 1
+people:
+  - participant_id: martin
+    display_name: Martin
+    attendance_status: present
+  - participant_id: martin
+    display_name: Martin Duplicate
+    attendance_status: mentioned_only
+"""
+
+    with pytest.raises(PeopleYamlError, match="Duplicate participant_id"):
+        import_people_yaml(content)
+
+
+def test_invalid_attendance_is_rejected() -> None:
+    content = """\
+version: 1
+people:
+  - participant_id: martin
+    display_name: Martin
+    attendance_status: absent
+"""
+
+    with pytest.raises(PeopleYamlError, match="invalid attendance_status"):
+        import_people_yaml(content)
+
+
+def test_failed_import_does_not_mutate_existing_people() -> None:
+    existing = sample_people()
+    snapshot = list(existing)
+
+    with pytest.raises(PeopleYamlError):
+        import_people_yaml("version: 1\npeople: not-a-list\n")
+
+    assert existing == snapshot

tests/test_performance.py

@@ -0,0 +1,21 @@
+import pytest
+
+from mka.application.meeting_service import ProcessingOptions
+from mka.application.performance import PERFORMANCE_PROFILES, resolve_ollama_num_thread
+
+
+def test_default_profile_is_auto() -> None:
+    assert ProcessingOptions().performance_profile == "auto"
+    assert PERFORMANCE_PROFILES[0] == "auto"
+
+
+def test_auto_profile_has_no_ollama_thread_override() -> None:
+    assert resolve_ollama_num_thread("auto") is None
+
+
+@pytest.mark.parametrize(
+    ("profile", "threads"),
+    [("fast", 16), ("efficient", 10), ("powersave", 4)],
+)
+def test_profiles_resolve_to_current_ollama_thread_counts(profile: str, threads: int) -> None:
+    assert resolve_ollama_num_thread(profile) == threads

tests/test_progress_timing.py

@@ -0,0 +1,45 @@
+from mka.application.progress_timing import ProcessingTimer
+
+
+class FakeClock:
+    def __init__(self, value: float = 0.0) -> None:
+        self.value = value
+
+    def __call__(self) -> float:
+        return self.value
+
+
+def test_completed_duration_is_retained_while_active_stage_increases() -> None:
+    clock = FakeClock(10.0)
+    timer = ProcessingTimer(clock)
+    timer.start_run()
+    timer.start_stage("preparing")
+    clock.value = 18.0
+    timer.finish_stage("preparing")
+    timer.start_stage("transcription")
+
+    clock.value = 20.0
+    first = timer.snapshot()
+    clock.value = 25.5
+    second = timer.snapshot()
+
+    assert first.stage_durations == {"preparing": 8.0, "transcription": 2.0}
+    assert second.stage_durations == {"preparing": 8.0, "transcription": 7.5}
+    assert second.total_duration == 15.5
+
+
+def test_failed_active_stage_and_total_duration_are_frozen() -> None:
+    clock = FakeClock(2.0)
+    timer = ProcessingTimer(clock)
+    timer.start_run()
+    clock.value = 5.0
+    timer.start_stage("transcription")
+    clock.value = 14.0
+    timer.finish_run()
+    clock.value = 99.0
+
+    snapshot = timer.snapshot()
+
+    assert snapshot.stage_durations["transcription"] == 9.0
+    assert snapshot.total_duration == 12.0
+    assert snapshot.running is False

tests/test_run_inputs.py

@@ -0,0 +1,136 @@
+import json
+from datetime import date
+
+import pytest
+
+from mka.application.meeting_service import ParticipantInput
+from mka.application.run_inputs import (
+    RunInputJsonError,
+    RunInputState,
+    export_run_inputs,
+    import_run_inputs,
+    run_input_filename,
+)
+
+
+def populated_state() -> RunInputState:
+    return RunInputState(
+        title="F&E Technik Abteilungs Jour Fixe",
+        description="Review the production trial.",
+        language="de",
+        meeting_date=date(2026, 8, 25),
+        participants=(
+            ParticipantInput(
+                participant_id="martin",
+                display_name="Martin Tazl",
+                role="Project lead",
+                organization="Engineering",
+            ),
+            ParticipantInput(
+                participant_id="alex",
+                display_name="Alexander Funk",
+                attendance_status="mentioned_only",
+            ),
+        ),
+        audio_normalization=False,
+        diarization_enabled=True,
+        source_file_name="2026-08-25_jour_fixe.wav",
+    )
+
+
+def test_current_form_state_serializes_with_schema_and_all_supported_values() -> None:
+    document = json.loads(export_run_inputs(populated_state()))
+
+    assert document["schema_version"] == 1
+    assert document["meeting"]["title"] == "F&E Technik Abteilungs Jour Fixe"
+    assert document["meeting"]["description"] == "Review the production trial."
+    assert document["meeting"]["language"] == "de"
+    assert document["meeting"]["date"] == "2026-08-25"
+    assert document["meeting"]["participants"][1] == {
+        "participant_id": "alex",
+        "display_name": "Alexander Funk",
+        "role": "",
+        "organization": "",
+        "attendance_status": "mentioned_only",
+    }
+    assert document["processing"] == {
+        "audio_normalization": False,
+        "diarization_enabled": True,
+    }
+
+
+def test_export_import_round_trip_restores_form_and_participants() -> None:
+    original = populated_state()
+
+    restored = import_run_inputs(export_run_inputs(original))
+
+    assert restored == original
+    assert restored.participants[0].participant_id == "martin"
+    assert restored.participants[0].display_name == "Martin Tazl"
+
+
+def test_missing_optional_fields_use_current_defaults() -> None:
+    restored = import_run_inputs('{"schema_version": 1}')
+
+    defaults = RunInputState.defaults()
+    assert restored == defaults
+
+
+def test_unknown_safe_fields_are_ignored() -> None:
+    restored = import_run_inputs(
+        json.dumps(
+            {
+                "schema_version": 1,
+                "meeting": {"title": "Known", "future_field": {"value": 1}},
+                "processing": {"future_toggle": True},
+                "future_section": [1, 2, 3],
+            }
+        )
+    )
+
+    assert restored.title == "Known"
+
+
+@pytest.mark.parametrize("content", ["not-json", "[]", b"\xff"])
+def test_malformed_json_is_rejected(content: str | bytes) -> None:
+    with pytest.raises(RunInputJsonError, match="Malformed|top-level"):
+        import_run_inputs(content)
+
+
+def test_unsupported_future_schema_is_rejected() -> None:
+    with pytest.raises(RunInputJsonError, match="Unsupported.*schema_version"):
+        import_run_inputs('{"schema_version": 2}')
+
+
+def test_media_contents_are_never_serialized() -> None:
+    exported = export_run_inputs(populated_state())
+
+    assert "2026-08-25_jour_fixe.wav" in exported
+    assert "audio_bytes" not in exported
+    assert "base64" not in exported
+
+
+def test_source_filename_must_not_be_a_machine_specific_path() -> None:
+    with pytest.raises(RunInputJsonError, match="filename, not a path"):
+        import_run_inputs('{"schema_version": 1, "source_file_name": "/tmp/meeting.wav"}')
+
+
+def test_invalid_or_duplicate_participants_are_not_silently_reinterpreted() -> None:
+    document = {
+        "schema_version": 1,
+        "meeting": {
+            "participants": [
+                {"participant_id": "martin", "display_name": "Martin"},
+                {"participant_id": "martin", "display_name": "Someone else"},
+            ]
+        },
+    }
+
+    with pytest.raises(RunInputJsonError, match="Duplicate participant_id"):
+        import_run_inputs(json.dumps(document))
+
+
+def test_export_filename_is_readable_and_machine_independent() -> None:
+    assert run_input_filename("F&E Technik Jour Fixe") == (
+        "meeting-inputs-f-e-technik-jour-fixe.json"
+    )

tests/test_speaker_mapping_ui.py

@@ -0,0 +1,134 @@
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+from streamlit.testing.v1 import AppTest
+
+from mka.ui.streamlit_app import _mapping_counts, _speaker_options
+
+
+@pytest.mark.parametrize("label", ["SPEAKER_00", "SPEAKER_01", "SPEAKER_02"])
+def test_unmapped_options_include_all_participants(label):
+    assert _speaker_options(("a", "b"), {}, label) == [None, "a", "b"]
+
+
+def test_options_reserve_other_assignments_and_preserve_current():
+    mappings = {"SPEAKER_00": "a"}
+    assert _speaker_options(("a", "b"), mappings, "SPEAKER_00") == [None, "a", "b"]
+    for label in ("SPEAKER_01", "SPEAKER_02"):
+        assert _speaker_options(("a", "b"), mappings, label) == [None, "b"]
+    mappings["SPEAKER_00"] = "b"
+    assert _speaker_options(("a", "b"), mappings, "SPEAKER_01") == [None, "a"]
+    mappings["SPEAKER_00"] = None
+    assert _speaker_options(("a", "b"), mappings, "SPEAKER_01") == [None, "a", "b"]
+
+
+def test_existing_duplicate_or_missing_participant_is_not_removed():
+    assert _speaker_options(("b",), {"s0": "a", "s1": "a"}, "s0") == [None, "b", "a"]
+
+
+@pytest.mark.parametrize(
+    ("mappings", "expected"),
+    [
+        ({}, (2, 0, 2)),
+        ({"s0": "a", "other": "b"}, (2, 1, 1)),
+        ({"s0": "a", "s1": "b"}, (2, 2, 0)),
+        ({"s0": None}, (2, 0, 2)),
+    ],
+)
+def test_counts_only_include_detected_speakers(mappings, expected):
+    assert _mapping_counts(("s0", "s1"), mappings) == expected
+
+
+def mapping_app():
+    from mka.application.meeting_service import SpeakerMappingReview, SpeakerReview
+    from mka.ui.streamlit_app import _render_speaker_mapping
+
+    _render_speaker_mapping(
+        SpeakerMappingReview(
+            tuple(SpeakerReview(f"SPEAKER_0{i}", ()) for i in range(3)),
+            (("a", "Participant A"), ("b", "Participant B"), ("c", "Participant C")),
+            {"SPEAKER_00": "a"},
+        ),
+        "test",
+    )
+
+
+def test_widget_reruns_filter_release_and_update_status():
+    app = AppTest.from_function(mapping_app).run()
+    assert not app.exception
+    assert app.selectbox[0].value == "a"
+    assert "Participant A" not in app.selectbox[1].options
+    assert len(app.warning) == 1
+    assert "3 speakers detected · 1 assigned · 2 unassigned" in app.markdown[0].value
+    assert "Unassigned" in app.selectbox[1].label
+
+    app.selectbox[2].select("c").run()
+    assert app.selectbox[0].value == "a"
+    assert "Participant C" not in app.selectbox[0].options
+    app.selectbox[1].select("b").run()
+    assert not app.warning
+    assert "3 assigned · 0 unassigned" in app.markdown[0].value
+    app.selectbox[0].select(None).run()
+    assert app.warning
+    assert "Participant A" in app.selectbox[1].options
+    assert app.selectbox[1].value == "b"
+    app.selectbox[1].select("a").run()
+    assert "Participant B" in app.selectbox[0].options
+    assert "Participant A" not in app.selectbox[0].options
+    assert app.selectbox[0].value is None
+    assert not app.exception
+
+
+def test_checkpoint_allows_anonymous_protocol_generation(monkeypatch):
+    from mka.application.meeting_service import SpeakerMappingReview, SpeakerReview
+    from mka.ui import streamlit_app as ui
+
+    outcome = SimpleNamespace(
+        succeeded=True,
+        run_dir=Path("/tmp/mapping-test"),
+        speaker_attribution_available=True,
+        original_protocol="Anonymous protocol",
+        awaiting_speaker_review=True,
+        detected_speaker_count=1,
+    )
+    review = SpeakerMappingReview((SpeakerReview("SPEAKER_00", ()),), (("a", "A"),), {})
+    calls = []
+
+    class Service:
+        def __init__(self, *args):
+            pass
+
+        def load_speaker_mapping_review(self, run_dir):
+            return review
+
+        def regenerate_protocol(self, run_dir, selections, **kwargs):
+            calls.append(selections)
+            return outcome
+
+    monkeypatch.setattr(ui, "MeetingProcessingService", Service)
+    monkeypatch.setattr(ui.AppSettings, "from_environment", lambda: None)
+    monkeypatch.setattr(ui, "MeetingLabGateway", lambda: None)
+    monkeypatch.setattr(ui, "_remember_regeneration_timing", lambda *args: None, raising=False)
+    monkeypatch.setattr(
+        ui,
+        "_run_with_live_progress",
+        lambda service, action, states, message: (action(None), states, message, None),
+        raising=False,
+    )
+
+    def result_app(outcome):
+        import streamlit as st
+
+        from mka.ui.streamlit_app import _render_result
+
+        st.session_state.outcome = outcome
+        _render_result()
+
+    app = AppTest.from_function(result_app, args=(outcome,)).run()
+    generate = next(button for button in app.button if button.label == "Generate protocol")
+    assert not generate.disabled
+    assert app.warning
+    generate.click().run()
+    assert calls == [{}]
+    assert not app.exception

tests/test_streamlit_app.py

@@ -0,0 +1,95 @@
+from datetime import date
+
+from mka.application.meeting_service import ParticipantInput
+from mka.application.progress_timing import TimingSnapshot
+from mka.application.run_inputs import RunInputState
+from mka.ui import streamlit_app
+
+
+def test_alias_input_accepts_lines_and_commas() -> None:
+    assert streamlit_app._parse_aliases("Sikirgut\nSekugrid HS, Secugrid H S") == (
+        "Sikirgut",
+        "Sekugrid HS",
+        "Secugrid H S",
+    )
+
+
+def test_regenerated_protocol_widget_value_is_deferred_until_next_run(
+    monkeypatch,
+) -> None:
+    state = {"edited_protocol": "old protocol"}
+    monkeypatch.setattr(streamlit_app.st, "session_state", state)
+
+    streamlit_app._queue_edited_protocol("regenerated protocol")
+
+    assert state["edited_protocol"] == "old protocol"
+    assert state["pending_edited_protocol"] == "regenerated protocol"
+
+    streamlit_app._apply_pending_edited_protocol()
+
+    assert state["edited_protocol"] == "regenerated protocol"
+    assert "pending_edited_protocol" not in state
+
+
+def test_imported_inputs_are_applied_via_pending_state_before_widgets(
+    monkeypatch,
+) -> None:
+    existing_upload = object()
+    state = {"source_media_0": existing_upload, "source_media_widget_generation": 0}
+    monkeypatch.setattr(streamlit_app.st, "session_state", state)
+    imported = RunInputState(
+        title="Imported meeting",
+        description="Imported context",
+        language="en",
+        meeting_date=date(2026, 8, 25),
+        participants=(ParticipantInput("martin", "Martin"),),
+        audio_normalization=False,
+        diarization_enabled=True,
+        source_file_name="meeting.wav",
+    )
+
+    streamlit_app._queue_run_inputs(imported)
+
+    assert "meeting_title" not in state
+    assert state["pending_run_inputs"] == imported
+
+    streamlit_app._apply_pending_run_inputs()
+
+    assert state["meeting_title"] == "Imported meeting"
+    assert state["meeting_description"] == "Imported context"
+    assert state["meeting_language"] == "en"
+    assert state["meeting_has_date"] is True
+    assert state["participants"][0]["participant_id"] == "martin"
+    assert state["audio_normalization"] is False
+    assert state["diarization_enabled"] is True
+    assert state["imported_source_file_name"] == "meeting.wav"
+    assert state["source_media_widget_generation"] == 1
+    assert "source_media_1" not in state
+    assert "pending_run_inputs" not in state
+    assert state["source_media_0"] is existing_upload
+
+
+def test_duration_formatting_covers_seconds_minutes_and_hours() -> None:
+    assert streamlit_app._format_duration(8.9) == "00:08"
+    assert streamlit_app._format_duration(7 * 60 + 18) == "07:18"
+    assert streamlit_app._format_duration(3600 + 3 * 60 + 42) == "1:03:42"
+
+
+def test_completed_regeneration_timing_survives_frontend_rerun(monkeypatch) -> None:
+    state = {}
+    monkeypatch.setattr(streamlit_app.st, "session_state", state)
+    timing = TimingSnapshot(
+        stage_durations={"protocol_generation": 4.5},
+        active_stage=None,
+        total_duration=4.5,
+        running=False,
+    )
+    states = {stage: "skipped" for stage in streamlit_app.STAGES}
+    states["protocol_generation"] = "completed"
+
+    streamlit_app._remember_regeneration_timing(states, "Protocol regenerated", timing)
+
+    saved_states, saved_message, saved_timing = state["regeneration_progress"]
+    assert saved_states["protocol_generation"] == "completed"
+    assert saved_message == "Protocol regenerated"
+    assert saved_timing == timing