Simplify architecture: remove overengineering, flatten structure

- Remove dead code: cache.py, context_managers.py, decorators.py, flask-limiter
- Replace AudioSource ABC + FakeFile (5 classes) with 4 plain functions
- Replace TempFileManager class with create_temp_file/cleanup_temp_files functions
- Simplify RequestLogger from 233 to 65 lines, remove file reading side-effect
- Convert HistoryLogger and AudioUtils classes to module-level functions
- Remove unused speed_up_audio and audio_speed_factor config
- Flatten single-file directories: shared/, api/, storage/, validation/, async_tasks/, logging/
- Merge logging config + request_logger into single infrastructure/log.py
- Fix request_logging config key (was request_logger)
- Trim CLAUDE.md to high-level only

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

CLAUDE.md

@@ -15,83 +15,8 @@ Local, OpenAI-compatible speech recognition API service using the Whisper model.
 
 ## Architecture
 
-```
-server.py                          # Entry point, argparse, launches WhisperServiceAPI
-app/__init__.py                    # WhisperServiceAPI: Flask init, wires all components
-app/core/
-  config.py                        # load_config() from JSON
-  transcriber.py                   # WhisperTranscriber: model load, device select, inference
-  transcription_service.py         # TranscriptionService: orchestrates source -> validate -> transcribe -> log
-app/audio/
-  processor.py                     # AudioProcessor: WAV convert, normalize, compress, speedup, silence
-  sources.py                       # AudioSource (abstract) + UploadedFile/URL/Base64/LocalFile sources
-  utils.py                         # AudioUtils: load audio as numpy, get duration via ffprobe
-app/api/
-  routes.py                        # All Flask endpoints (OpenAI-compatible + local + async)
-app/infrastructure/
-  storage/cache.py                 # SimpleCache with TTL
-  storage/file_manager.py          # TempFileManager: temp file lifecycle with context managers
-  logging/config.py                # setup_logging(): console + rotating file handler
-  logging/request_logger.py        # RequestLogger: HTTP request/response middleware
-  validation/validators.py         # FileValidator: size, extension, MIME checks
-  async_tasks/manager.py           # AsyncTaskManager: thread-based async with status tracking
-app/shared/
-  history_logger.py                # HistoryLogger: saves transcription results as JSON by date
-  decorators.py                    # log_invalid_file_request decorator
-  context_managers.py              # open_file context manager
-app/static/
-  index.html                       # Built-in web UI client
-```
-
-## Request Flow
-
-```
-Flask Request
-  -> RequestLogger middleware (logs request)
-  -> Routes (endpoint handler)
-  -> TranscriptionService.transcribe_from_source()
-     -> AudioSource.get_audio_file()        # fetch from upload/URL/base64/local
-     -> FileValidator.validate_file()        # size/extension/MIME
-     -> WhisperTranscriber.process_file()
-        -> AudioProcessor.process_audio()    # WAV 16kHz, normalize, compress, speedup, silence
-        -> WhisperTranscriber.transcribe()   # model inference
-     -> HistoryLogger.save()                 # persist result JSON
-  -> JSON Response (text, processing_time, duration, model)
-```
-
-## API Endpoints
-
-| Method | Path | Purpose |
-|--------|------|---------|
-| GET | `/` | Web UI |
-| GET | `/health` | Service status |
-| GET | `/config` | Current configuration |
-| GET | `/v1/models` | List models (OpenAI-compatible) |
-| GET | `/v1/models/<id>` | Model details |
-| POST | `/v1/audio/transcriptions` | Transcribe uploaded file (multipart) |
-| POST | `/v1/audio/transcriptions/url` | Transcribe from URL |
-| POST | `/v1/audio/transcriptions/base64` | Transcribe from base64 |
-| POST | `/v1/audio/transcriptions/async` | Async transcription |
-| GET | `/v1/tasks/<task_id>` | Async task status |
-| POST | `/local/transcriptions` | Transcribe local server file |
-
-## Configuration
-
-All settings in `config.json`. Key parameters:
-
-* `service_port`: server port (default 5042)
-* `model_path`: path to Whisper model directory
-* `language`: recognition language
-* `device_id`: GPU index for CUDA
-* Audio processing: `norm_level`, `compand_params`, `audio_speed_factor`, `audio_rate`
-* Inference: `chunk_length_s`, `batch_size`, `max_new_tokens`, `temperature`
-* `file_validation`: max size, allowed extensions/MIME types
-* `request_logging`: excluded endpoints, sensitive headers
-
-## Key Design Decisions
-
-* **Config-driven**: All behavior controlled via `config.json`. No hardcoded model paths or thresholds.
-* **Source abstraction**: `AudioSource` ABC unifies all input methods. New sources implement `get_audio_file()`.
-* **Temp file lifecycle**: `TempFileManager` with context managers ensures cleanup even on errors.
-* **OpenAI compatibility**: `/v1/audio/transcriptions` matches OpenAI API contract for drop-in replacement.
-* **Device fallback**: CUDA -> MPS -> CPU, with Flash Attention 2 attempted first on CUDA.
+* Entry: `server.py` -> `app/__init__.py` (WhisperServiceAPI)
+* Modules: `app/core/` (transcriber, config), `app/audio/` (processor, sources, utils), `app/infrastructure/` (logging, validation, storage, async tasks)
+* Request flow: source function -> validate -> transcribe (AudioProcessor -> Whisper inference) -> save history -> JSON response
+* OpenAI-compatible API: `/v1/audio/transcriptions` matches OpenAI contract for drop-in replacement
+* All settings in `config.json`. Device fallback: CUDA -> MPS -> CPU