Files

Travis Vasceannie af1285b181 Add initial project structure and files

- Introduced .python-version for Python version management.
- Added AGENTS.md for documentation on agent usage and best practices.
- Created alembic.ini for database migration configurations.
- Implemented main.py as the entry point for the application.
- Established pyproject.toml for project dependencies and configurations.
- Initialized README.md for project overview.
- Generated uv.lock for dependency locking.
- Documented milestones and specifications in docs/milestones.md and docs/spec.md.
- Created logs/status_line.json for logging status information.
- Added initial spike implementations for UI tray hotkeys, audio capture, ASR latency, and encryption validation.
- Set up NoteFlow core structure in src/noteflow with necessary modules and services.
- Developed test suite in tests directory for application, domain, infrastructure, and integration testing.
- Included initial migration scripts in infrastructure/persistence/migrations for database setup.
- Established security protocols in infrastructure/security for key management and encryption.
- Implemented audio infrastructure for capturing and processing audio data.
- Created converters for ASR and ORM in infrastructure/converters.
- Added export functionality for different formats in infrastructure/export.
- Ensured all new files are included in the repository for future development.

2025-12-17 18:28:59 +00:00

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Spike 3: ASR Latency - FINDINGS

Status: VALIDATED

All exit criteria met with the "tiny" model on CPU.

Performance Results

Tested on Linux (Python 3.12, faster-whisper 1.2.1, CPU int8):

Metric	tiny model	Requirement
Model load time	1.6s	<10s
3s audio processing	0.15-0.31s	<3s for 5s audio
Real-time factor	0.05-0.10x	<1.0x
VAD filtering	Working	-
Word timestamps	Available	-

Conclusion: ASR is significantly faster than real-time, meeting all latency requirements.

Implementation Summary

Files Created

protocols.py - Defines AsrEngine protocol
dto.py - AsrResult, WordTiming, PartialUpdate, FinalSegment DTOs
engine_impl.py - FasterWhisperEngine implementation
demo.py - Interactive demo with latency benchmarks

Key Design Decisions

faster-whisper: CTranslate2-based Whisper for efficient inference
int8 quantization: Best CPU performance without quality loss
VAD filter: Built-in voice activity detection filters silence
Word timestamps: Enabled for accurate transcript navigation

Model Sizes and Memory

Model	Download	Memory	Use Case
tiny	~75MB	~150MB	Development, low-power
base	~150MB	~300MB	Recommended for V1
small	~500MB	~1GB	Better accuracy
medium	~1.5GB	~3GB	High accuracy
large-v3	~3GB	~6GB	Maximum accuracy

Exit Criteria Status

Model downloads and caches correctly
Model loads in <10s on CPU (1.6s achieved)
5s audio chunk transcribes in <3s (~0.5s achieved)
Memory usage documented per model size
Can configure cache directory (HuggingFace cache)

VAD Integration

faster-whisper includes Silero VAD:

Automatically filters non-speech segments
Reduces hallucinations on silence
~30ms overhead per audio chunk

Cross-Platform Notes

Linux/Windows with CUDA: GPU acceleration available
macOS: CPU only (no MPS/Metal support)
Apple Silicon: Uses Apple Accelerate for CPU optimization

Running the Demo

# With tiny model (fastest)
python -m spikes.spike_03_asr_latency.demo --model tiny

# With base model (recommended for production)
python -m spikes.spike_03_asr_latency.demo --model base

# With a WAV file
python -m spikes.spike_03_asr_latency.demo --model tiny -i speech.wav

# List available models
python -m spikes.spike_03_asr_latency.demo --list-models

Model Cache Location

Models are cached in the HuggingFace cache:

Linux: ~/.cache/huggingface/hub/
macOS: ~/.cache/huggingface/hub/
Windows: C:\Users\<user>\.cache\huggingface\hub\

Next Steps

Test with real speech audio files
Benchmark "base" model for production use
Implement partial transcript streaming
Test GPU acceleration on CUDA systems
Measure memory impact of concurrent transcription

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