Refine chunked transcription strategy

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

@@ -10,16 +10,60 @@ 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 quality-oriented fallback if required.
+The preferred first model is `large-v3-turbo`, with `large-v3` as a 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 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.
+- Model name, language setting, timestamp mode, engine version, chunk duration and relevant decoding parameters shall be stored with every transcript.
+- The decision can be revisited if another engine provides clearly better quality, speed or deployment characteristics.