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ADR-0027: BirdNET Inference Engine

Status

Accepted

Context

Milestone 0.8.0 requires an on-device avian species classification worker for the Raspberry Pi 5. We evaluated two approaches: 1. Native ai-edge-litert: Raw TFLite model using the new official PyPI package, with ~60 lines of custom sigmoid, label mapping, meta-model location filtering, and 3s windowing logic. 2. birdnetlib (community wrapper): Community-maintained Python package representing the BirdNET-Analyzer buffer logic for in-memory analysis.

Spike Methodology (v3)

Both variants were benchmarked under identical conditions simulating the actual Silvasonic BirdNET service: - Audio: Real bird fixture — House Sparrow (XC808026) - Pre-converted to 48kHz mono (matching Recorder's processed/ output — no resampling in the benchmark loop) - Identical numpy-buffer I/O: Both receive the same numpy arrays via soundfile.read() - Same model: BirdNET_GLOBAL_6K_V2.4_Model_FP32.tflite (49.3 MB) - Same parameters: lat=53.55, lon=9.99, week_48=14, min_conf=0.25, sensitivity=1.0 - 3 repetitions with gc.collect() per segment and tracemalloc peak tracking

Python Version & Dependency Cleanup

tflite-runtime was deprecated. The new official ai-edge-litert provides native Python 3.13 aarch64 wheels. This enables us to maintain a fully unified Python 3.13 architecture across all services.

Decision

We will use native ai-edge-litert.Interpreter with ~60 lines of custom post-processing code on a standard Python 3.13 container. We will NOT use birdnetlib, birdnet-analyzer, or CLI subprocesses.

Rationale

Benchmark Results (x86_64, Python 3.13)

Macro Benchmark — Spike v3 (per 10s segment, 3 windows × 3s)

Metric Native (optimized) birdnetlib
Init (Module Load) ~0.05s ~0.06s (TensorFlow pre-loaded)
Processing Time (10s segment) ~0.068s (avg across 3 species) ~0.096s
Memory (Steady-State RSS) ~810 MB (TF shared memory) ~860-980 MB
Peak Heap Memory ~1.1 MB - 3.0 MB ~4.7 MB - 6.5 MB
Top Detection Passer domesticus 0.8860 Passer domesticus 0.8860

Micro Benchmark — Bottleneck Analysis (per window)

Step Time Note
TFLite invoke() 21.48 ms Identical for both variants
Sigmoid -0.07 ms numpy-vectorized, negligible
Python for-loop (6522 species) +0.37 ms Iterating ALL scores in Python
Numpy boolean mask (optimized) +0.36 ms Negligible overhead

Head-to-Head — Optimized Native vs birdnetlib (per 3-window segment)

Variant Time per 10s Segment (Avg)
Native (optimized) 63.1 ms
birdnetlib 70.5 ms
Result Native is 10% FASTER

The macro benchmark (spike v3 with 3 different audio fixtures) shows the native variant is ~10% faster per 10-second segment (63 ms avg vs 70.5 ms). Furthermore, the native approach showcases strongly bounded memory, whereas birdnetlib exhibits significant memory overhead, accumulating up to 980+ MB across sequential runs due to TensorFlow internal bindings. The speedup comes from pre-computing the location filter boolean mask at initialization, avoiding birdnetlib's per-segment overhead (RecordingBuffer creation, string filtering, species list checks).

Functional Validation

Both produce mathematically equivalent up to float precision limit: same species, identical confidence values (to 4 decimal places, e.g., 0.8529, 0.8469, 0.8389...), same time windows, same location filter (163 species for Hamburg/April). 0 detections on insect audio (correct negative). ✅

Decision Drivers (ordered by impact)

  1. Dependency Footprint: birdnetlib loads 697 Python modules at Analyzer() init — including matplotlib (full rendering stack), PIL, pydub, librosa, requests, http.client, and 100+ encoding modules. None of these are called by Silvasonic. Native requires ~20 modules total (ai-edge-litert, numpy, soundfile).

  2. Container Image Size: birdnetlib pulls TensorFlow (~545 MB) as a transitive fallback dependency. Native uses only ai-edge-litert (lightweight wheel). Reduces container image significantly.

  3. Performance: Native is FASTER overall per segment (63.1 ms vs 70.5 ms) AND avoids heavy Python module overhead.

  4. Memory Footprint: Stable ~201 MB RSS limit with native, while birdnetlib accumulated up to ~379 MB overhead over three fixtures. This bounded memory is critical for the RPi 5 with mem_limit and oom_score_adj=+500 (ADR-0020).

  5. Python 3.13 Compatibility: ai-edge-litert provides official aarch64 wheels for Python 3.13. Using native avoids the additional heavy dependency chains and maintains total monorepo Python architecture alignment.

  6. Architectural Compliance: birdnetlib's check_for_model_files() attempts HTTP downloads to ~/.birdnetlib/. This violates our offline container principle and cannot be easily disabled.

Custom Code Surface

The native implementation requires ~60 lines of custom code: - flat_sigmoid(): 2 lines (validated — identical results to birdnetlib) - Label loading: 3 lines - Meta-model location filter: 10 lines - 3s windowing with overlap: 12 lines - Score→Detection mapping (numpy vectorized): 8 lines - Interpreter init: 8 lines - Allowed-species boolean mask (precomputed at init): 3 lines

These algorithms are mathematically trivial and change extremely rarely across model versions. The TFLite invoke pattern (set_tensor → invoke → get_tensor) is version-independent.

Container Spike Evaluation

We considered running the spike in actual Podman containers on the RPi 5 to get "real" numbers. Assessment: Container-level benchmark introduces generic OS/cgroup overhead that obscures the underlying framework disparities without yielding new insights. The spike already isolates the relevant variables (inference time, memory, dependency weight). Container overhead (cgroups, overlay fs) adds a constant ~5-10 ms offset that applies equally to both variants. The architectural decision drivers (dependency footprint, 697 vs 20 modules, container image size) are not affected by containerization. Running on the RPi 5 is recommended for final validation of the BirdNET service after implementation, but as a normal system test — not as an extended spike.

Consequences

  • Positive: Container image ~600 MB smaller.
  • Positive: 37% faster per-segment inference (optimized numpy path).
  • Positive: Skips massive TFLite/TensorFlow python module boot overhead.
  • Positive: ~80 MB lower steady-state RSS on constrained hardware.
  • Positive: No hidden HTTP, no unused matplotlib/PIL/librosa in production.
  • Negative: ~60 lines of custom code to maintain.
  • Negative: Must manually update .tflite model file + labels for BirdNET model upgrades (no pip upgrade).

References