Improve speaker-review excerpts

.env.example

@@ -1,9 +1,10 @@
 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_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

CHANGELOG.md

@@ -4,6 +4,14 @@
 
 ### 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
@@ -15,6 +23,8 @@
 
 ### 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

PROJECT_KNOWLEDGE.md

@@ -1,5 +1,14 @@
 # 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
 
 Meeting Assistant turns recorded meetings into reviewed, user-facing protocols
@@ -37,9 +46,19 @@ Meeting Assistant owns user interaction and product workflow:
 - 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.
@@ -62,7 +81,8 @@ Imported audio
        (optionally normalize loudness; default on)
     -> transcribe with whisper.cpp and large-v3-turbo
     -> optionally diarize with pyannote.audio Community-1
-    -> generate a protocol directly from the full transcript
+    -> 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
 ```
 
@@ -76,6 +96,14 @@ 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,
@@ -90,6 +118,15 @@ 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:
@@ -107,7 +144,7 @@ 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;
+`qwen3.8:27b` 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.
@@ -131,3 +168,19 @@ product work, not a current MVP requirement.
 - no automatic identity claims
 - human review of generated protocols
 - small modules and simple interfaces
+
+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.

README.md

@@ -23,12 +23,20 @@ reference recorder, but imported audio is not tied to OBS-specific behavior.
 
 ## Processing Pipeline
 
+The existing **Meeting language** selector controls both transcription and protocol
+output: `de` means German for both, and `en` means English for both. The selection
+is saved in Meeting Context (`meeting.language`) and reused for protocol-only
+regeneration without retranscription. Older contexts without a language retain
+German protocol output. Names, speaker mappings and authored Meeting Context are
+preserved; no transcript translation stage is added.
+
 ```text
 Audio file
     -> FFmpeg preparation (mono, 16 kHz PCM WAV; normalization optional)
     -> whisper.cpp transcription (large-v3-turbo)
     -> optional pyannote.audio Community-1 diarization
-    -> direct full-transcript protocol generation
+    -> optional speaker review and explicit protocol generation (when diarized)
+       or direct full-transcript protocol generation (without diarization)
     -> human review and export
 ```
 
@@ -42,6 +50,17 @@ 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.
 
+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.
+
 ## Product Outputs
 
 The product direction includes:
@@ -64,6 +83,14 @@ 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.
+
 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
@@ -94,7 +121,7 @@ load `.env` files implicitly:
 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
+export MKA_PROTOCOL_MODEL=qwen3.8:27b
 ```
 
 Optional machine-specific settings include:
@@ -108,6 +135,62 @@ Optional machine-specific settings include:
 - `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
+
+## Terminology glossary
+
+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.
+
+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:
+
+```yaml
+version: 1
+glossary:
+  - id: 1
+    canonical_term: Secugrid HS
+    aliases:
+      - Sikirgut
+    category: product
+    description: Canonical product spelling
+    active: true
+```
+
+`id` is the stable SQLite glossary-entry identifier. Categories are `product`,
+`material`, `organization`, `technical_term`, `acronym`, or `other`.
+
+## Input configuration import and export
+
+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.
+
+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:
@@ -130,6 +213,22 @@ From the Meeting Assistant repository, start the UI with:
 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
@@ -147,3 +246,19 @@ 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.

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/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"

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/config.py

@@ -18,10 +18,13 @@ class AppSettings:
 
     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"
+    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"
@@ -38,9 +41,12 @@ class AppSettings:
         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"),
+            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,

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

@@ -12,9 +12,49 @@ 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):
@@ -31,6 +71,14 @@ class MeetingLabPort(Protocol):
         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:
@@ -54,6 +102,7 @@ class ParticipantInput:
 class ProcessingOptions:
     diarization_enabled: bool = False
     audio_normalization: bool = True
+    performance_profile: str = DEFAULT_PERFORMANCE_PROFILE
 
 
 @dataclass(frozen=True)
@@ -73,6 +122,105 @@ class ProcessingOutcome:
     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:
@@ -84,9 +232,18 @@ def stable_id(value: str) -> str:
 class MeetingProcessingService:
     """Translate UI input into Meeting Lab calls and persisted artifacts."""
 
-    def __init__(self, settings: AppSettings, meeting_lab: MeetingLabPort) -> None:
+    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,
@@ -154,6 +311,7 @@ class MeetingProcessingService:
                 "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:
@@ -195,20 +353,29 @@ class MeetingProcessingService:
                 "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,
@@ -219,7 +386,13 @@ class MeetingProcessingService:
             if progress_sink is not None:
                 progress_sink(app_event)
 
-        result = self.meeting_lab.run(config, context, relay)
+        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")
@@ -243,14 +416,286 @@ class MeetingProcessingService:
                 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:
@@ -270,3 +715,38 @@ class MeetingProcessingService:
         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/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/meeting_lab.py

@@ -18,6 +18,7 @@ class MeetingLabGateway:
             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:
@@ -28,6 +29,7 @@ class MeetingLabGateway:
         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."""
@@ -49,3 +51,18 @@ class MeetingLabGateway:
             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

@@ -2,13 +2,27 @@
 
 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,
@@ -16,6 +30,7 @@ from mka.application.meeting_service import (
     MeetingProcessingService,
     ParticipantInput,
     ProcessingOptions,
+    SpeakerMappingReview,
     stable_id,
 )
 from mka.application.people_yaml import (
@@ -23,6 +38,15 @@ from mka.application.people_yaml import (
     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,
@@ -36,6 +60,114 @@ STAGE_LABELS = {
 }
 
 
+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,
@@ -51,6 +183,51 @@ 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]]:
@@ -68,6 +245,27 @@ def _people_to_rows(people: list[ParticipantInput]) -> list[dict[str, str]]:
     ]
 
 
+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.")
@@ -145,40 +343,199 @@ def _render_participants() -> list[ParticipantInput]:
     return people
 
 
-def _progress_callback(
+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,
-    progress_slot: Any,
     states: dict[str, str],
-) -> Any:
+    timing: TimingSnapshot,
-    progress_bar = None
+    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 update(event: AppProgressEvent) -> None:
+
-        nonlocal progress_bar
+def _apply_progress_event(event: AppProgressEvent, states: dict[str, str]) -> None:
-        if event.stage in states:
+    if event.stage in states:
-            states[event.stage] = "running" if event.status == "started" else event.status
+        states[event.stage] = "running" if event.status == "started" else event.status
-        if event.stage == "failed":
+    if event.stage == "failed":
-            running = next(
+        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:
+            if running_stage is not None:
-                states[running] = "failed"
+                states[running_stage] = "failed"
-        elapsed = f"{event.elapsed_seconds:.1f} s"
+            snapshot = service.timer.snapshot()
-        message = event.message or STAGE_LABELS.get(event.stage, event.stage)
+            _render_progress_status(status_box, stage_table, states, snapshot, latest_message)
-        status_box.info(f"{message} — elapsed {elapsed}")
+            st.session_state["processing_progress"] = (states.copy(), latest_message, snapshot)
-        stage_table.table(
+            raise
-            [{"Stage": STAGE_LABELS[stage], "Status": states[stage]} for stage in STAGES]
+    while not event_queue.empty():
-        )
+        event = event_queue.get_nowait()
-        if event.progress is not None:
+        _apply_progress_event(event, states)
-            if progress_bar is None:
+        latest_message = event.message or STAGE_LABELS.get(event.stage, event.stage)
-                progress_bar = progress_slot.progress(0.0)
+    running_stage = next((stage for stage, status in states.items() if status == "running"), None)
-            progress_bar.progress(
+    if running_stage is not None:
-                min(max(event.progress, 0.0), 1.0),
+        states[running_stage] = "completed" if getattr(result, "succeeded", True) else "failed"
-                text=f"{STAGE_LABELS.get(event.stage, event.stage)}: {event.progress:.0%}",
+    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
 
-    return update
+
+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:
@@ -187,6 +544,9 @@ def _render_result() -> 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:
@@ -194,19 +554,88 @@ def _render_result() -> None:
             st.caption("Intermediate artifacts and run metadata were preserved here.")
         return
 
-    st.success("Processing completed. Review the generated protocol before use.")
+    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}")
-    with st.expander("Original generated protocol", expanded=False):
+    if outcome.speaker_attribution_available is False:
-        st.markdown(outcome.original_protocol or "")
+        st.warning(
-    edited = st.text_area(
+            "Speaker attribution was unavailable for this protocol because the "
-        "Editable protocol",
+            "safe-budget fallback removed diarization labels from the prompt."
-        key="edited_protocol",
+        )
-        height=500,
+    if message := st.session_state.pop("speaker_mapping_message", None):
-        help="The original protocol.md remains unchanged.",
+        st.success(message)
-    )
+    if not awaiting_review:
-    if st.button("Save edited protocol", type="primary"):
+        with st.expander("Original generated protocol", expanded=False):
-        path = MeetingProcessingService.save_edited_protocol(outcome.run_dir, edited)
+            st.markdown(outcome.original_protocol or "")
-        st.success(f"Saved edited protocol to {path}")
+        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:
@@ -215,23 +644,55 @@ def main() -> None:
     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"])
+    audio = st.file_uploader(
-    title = st.text_input("Meeting title")
+        "Audio recording",
-    description = st.text_area("Description / context", height=100)
+        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"])
+    language = metadata_columns[0].selectbox(
-    has_date = metadata_columns[1].checkbox("Meeting date is known", value=True)
+        "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") if has_date else 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, "
@@ -240,9 +701,28 @@ def main() -> None:
     )
     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.")
@@ -252,9 +732,8 @@ def main() -> None:
         ):
             st.error("Every person row needs a name and participant ID.")
         else:
-            settings = AppSettings.from_environment()
             try:
-                service = MeetingProcessingService(settings, MeetingLabGateway())
+                service = MeetingProcessingService(settings, MeetingLabGateway(), glossary)
                 meeting = MeetingDetails(
                     title=title,
                     language=language,
@@ -263,30 +742,34 @@ def main() -> None:
                 )
                 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")
-                status_box = st.empty()
-                stage_table = st.empty()
-                progress_slot = st.empty()
                 states = {stage: "pending" for stage in STAGES}
                 if not diarization_enabled:
                     states["diarization"] = "skipped"
-                callback = _progress_callback(status_box, stage_table, progress_slot, states)
+                outcome, _, _, _ = _run_with_live_progress(
-                outcome = service.process(
+                    service,
-                    audio_path,
+                    lambda progress_sink: service.process(
-                    meeting,
+                        audio_path,
-                    participants,
+                        meeting,
-                    ProcessingOptions(
+                        participants,
-                        diarization_enabled=diarization_enabled,
+                        ProcessingOptions(
-                        audio_normalization=audio_normalization,
+                            diarization_enabled=diarization_enabled,
+                            audio_normalization=audio_normalization,
+                            performance_profile=performance_profile,
+                        ),
+                        progress_sink=progress_sink,
                     ),
-                    progress_sink=callback,
+                    states,
+                    "Starting processing",
                 )
                 st.session_state.outcome = outcome
                 st.session_state.edited_protocol = outcome.original_protocol or ""
                 if outcome.succeeded:
-                    status_box.success("Processing completed.")
+                    st.success("Processing completed.")
                 else:
-                    status_box.error(
+                    st.error(
                         f"Processing failed during {outcome.failed_stage}: "
                         f"{outcome.error_message} Artifacts were preserved."
                     )

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

@@ -39,6 +39,14 @@ def test_environment_defaults_to_no_diarization_container_args(monkeypatch) -> N
     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:

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

@@ -5,6 +5,9 @@ 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,
@@ -12,6 +15,7 @@ from mka.application.meeting_service import (
     ParticipantInput,
     ProcessingOptions,
 )
+from mka.application.progress_timing import ProcessingTimer
 
 
 @dataclass
@@ -29,6 +33,7 @@ class FakeMeetingLab:
         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
@@ -95,10 +100,58 @@ class FakeMeetingLab:
                 )
             )
             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"
@@ -107,6 +160,7 @@ def make_service(tmp_path: Path) -> tuple[MeetingProcessingService, FakeMeetingL
     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",
@@ -139,6 +193,72 @@ def participants() -> list[ParticipantInput]:
     ]
 
 
+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)
 
@@ -171,6 +291,40 @@ def test_build_context_preserves_explicit_speaker_mapping(tmp_path: Path) -> Non
     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() + [
@@ -201,21 +355,32 @@ def test_build_context_translates_mentioned_only_person(tmp_path: Path) -> 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, meeting(), participants(), ProcessingOptions(diarization_enabled=False)
+        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
@@ -253,7 +418,7 @@ def test_process_propagates_enabled_diarization_and_progress(tmp_path: Path) ->
     audio.write_bytes(b"audio")
     events = []
 
-    service.process(
+    outcome = service.process(
         audio,
         meeting(),
         participants(),
@@ -263,6 +428,9 @@ def test_process_propagates_enabled_diarization_and_progress(tmp_path: Path) ->
 
     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"),
@@ -270,6 +438,33 @@ def test_process_propagates_enabled_diarization_and_progress(tmp_path: Path) ->
     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(
@@ -329,6 +524,223 @@ def test_failure_reports_stage_and_preserves_run_dir(tmp_path: Path) -> None:
     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")
@@ -338,3 +750,172 @@ def test_uploaded_source_is_preserved_in_meeting_directory(tmp_path: Path) -> No
     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_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