Document development workflow

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

@@ -10,60 +10,16 @@ The project requires high-quality speech-to-text transcription for German and En
 
 The transcription component should support local execution where practical and should remain replaceable in the future. The project should not depend on a meeting bot or on a proprietary meeting platform.
 
-Practical testing showed that long continuous recordings may suffer from error propagation when the decoder continuously conditions on previously generated text.
-
-Independent transcription of smaller audio chunks produced significantly more stable results.
-
 ## Decision
 
 The project will use `faster-whisper` as the initial transcription engine.
 
-The preferred first model is `large-v3-turbo`, with `large-v3` as a quality-oriented fallback if required.
+The preferred first model is `large-v3-turbo`, with `large-v3` as quality-oriented fallback if required.
-
-The transcription pipeline shall process recordings as independent audio chunks rather than as a single continuous recording.
-
-The default processing strategy is:
-
-```text
-Recording (WAV)
-        │
-        ▼
-Split into 5-minute chunks
-        │
-        ▼
-Independent Whisper transcription
-(condition_on_previous_text = False)
-        │
-        ▼
-JSON transcript per chunk
-        │
-        ▼
-Merge chunk transcripts
-        │
-        ▼
-Canonical transcript
-```
-
-Chunk duration shall be configurable, with **5 minutes** as the default value.
-
-Each chunk shall be processed independently.
-
-The decoder shall not use text generated from previous chunks as context.
-
-Each chunk shall produce its own intermediate JSON transcript before merging.
-
-The merge step shall preserve timestamps and chunk ordering.
 
 ## Consequences
 
-- Long recordings become more robust.
-- Error propagation across chunk boundaries is prevented.
-- Failed chunks can be retranscribed independently.
-- Parallel processing of multiple chunks becomes possible.
-- Intermediate JSON files simplify debugging and future reprocessing.
-- Chunk size can be tuned later without changing the overall architecture.
 - Transcription can run locally on suitable hardware.
 - NVIDIA CUDA acceleration can be used later on an office AI PC.
 - The transcription module must hide the concrete engine behind an internal interface.
-- Model name, language setting, timestamp mode, engine version, chunk duration and relevant decoding parameters shall be stored with every transcript.
+- Model name, language setting, timestamp and engine version should be stored with every transcript.
 - The decision can be revisited if another engine provides clearly better quality, speed or deployment characteristics.

docs/adr/0008-meeting-storage-layout.md

@@ -54,12 +54,7 @@ meeting/
 ├── transcript/
 │   ├── canonical.json
 │   ├── working_v1.json
-│
+│   └── working_v2.json
-├── chunks/
-│   ├── chunk_000.json
-│   ├── chunk_001.json
-│   ├── chunk_002.json
-│   ├── ...
 │
 ├── artifacts/
 │   ├── summary.md