Saltar a contenido

Inference Engine

ONNX Runtime integration and model inference.

Architecture

InferenceIsolate (compatibility wrapper; root Dart isolate)
  └─ InferenceService (high-level coordinator)
      └─ ClassifierModel (low-level ONNX session wrapper)

ClassifierModel

Low-level flutter_onnxruntime session wrapper:

  • loadModelFromFile(path) — create OrtSession from an on-device model file
  • predict(audioSamples, windowSamples) — run inference, return ModelOutput
  • Resolves output tensor indices by name (handles any graph ordering)

InferenceService

High-level coordinator:

  • initialize(modelFilePath, labelsCsv, config) — load model + parse labels
  • infer(audioSamples) — model → temporal pooling → sensitivity → top-K
  • Log-Mean-Exp temporal smoothing across recent windows (α=5.0, max 5 windows)

InferenceIsolate

Keeps the historical public API while delegating to InferenceService on the root isolate:

  • start(modelFilePath, labelsCsv, config) — load model from a resolved file path
  • infer(audioSamples) — serialize the request and run it through the native session
  • stop() — release the service and close native resources

flutter_onnxruntime runs native inference on a platform background queue, so there is no dedicated Dart inference isolate now. Inference calls are serialized before entering the native session.

Geo-Model

GeoModel runs the smaller ONNX geo-model (~6 MB) through the same runtime:

  • Input: [latitude, longitude, week] (normalized)
  • Output: per-species probability scores
  • Used for geographic species filtering

Model Config

All model parameters are in assets/models/model_config.json:

  • Tensor names, sample rate, window duration
  • Label file format (delimiter, columns)
  • Inference defaults (sensitivity, threshold, top-K, pooling)