Pre-trained Models

Trained and converted models are published as GitHub release assets, not tracked repository files. Download a bundle from the latest release.

What's in a bundle

Every file in a bundle shares one basename, BirdNET_Tiny_N6_<REGION>_<SPECIES>_V<VERSION>, where the species count covers bird species only — nuisance and background outputs are not counted in the name but are part of the ordered output contract.

File	Use it for
<basename>_INT8.tflite	Deploying to the STM32N6 — this is the model you flash
<basename>_model_config.json	Input, frontend, and class contract; drives the firmware config
<basename>_labels.txt	Ordered output labels
<basename>_FP32.keras	Host inference, fine-tuning, re-conversion
<basename>_original_FP32.keras	Pre-QAT checkpoint, for retraining from an untouched state
<basename>_FP32.onnx	Host and interchange inference
<basename>_INT8_stedgeai_report.txt	Memory footprint and NPU operator coverage
<basename>_model_card.md	Contract, provenance, measured accuracy, and on-board timing

A bundle whose model was exported split carries these as well. The firmware runs a single network, so _INT8.tflite above is still what you flash; these exist so a species-list change can be pushed without resending the whole model.

File	Use it for
<basename>_INT8_backbone.tflite	Audio to embeddings. Flashed once and reused across heads
<basename>_INT8_classifier.tflite	Embeddings to scores. The only part that changes with the species list
<basename>_INT8_backbone.tflite.gz, <basename>_INT8_classifier.tflite.gz	The same two, gzipped; the head is what an over-the-air update carries
<basename>_INT8_backbone.tflite.fingerprint.json	Identity of the backbone. A replacement head must be calibrated against a matching one
<basename>_INT8_classifier_labels.txt	The head's own ordered labels, so an updated head brings its species list with it

Chaining the backbone into the classifier reproduces _INT8.tflite; both halves are gated on that equivalence before either is published. To build a further head for an already-flashed backbone, see Updating the head against a flashed backbone.

LICENSE-MODELS.md and ACCEPTABLE_USE.md ship alongside — see License & Acceptable Use.

The model, config, and labels are one contract

The TFLite model, _model_config.json, and _labels.txt describe a single trained model. Keep them together and never mix sidecars across bundles or versions — the frontend parameters and output ordering will not match.

The model card records the exact input contract (sample rate, chunk duration, output count), the accuracy the model reached on its evaluation catalog, and its measured on-board timing. Read it before deploying.

Running a bundle on the board

Everything the firmware needs is in the bundle; no extra downloads.

1. Install the toolchain and create config.json — see Deployment for X-CUBE-AI, STM32CubeProgrammer, and ARM GNU setup.
2. Prepare an SD card with test audio — see SD card preparation. WAV sample rate must match the sample_rate in _model_config.json; mismatched files are skipped.

3. Compile, flash, and run:

   # Wherever the extracted bundle lives; the globs below pick the files out
   # of it, so nothing depends on which region or version you downloaded.
   BUNDLE=~/Downloads/<bundle>
   
   python -m birdnet_stm32 board-test \
     --model_path    "$BUNDLE"/*_INT8.tflite \
     --model_config  "$BUNDLE"/*_model_config.json \
     --labels        "$BUNDLE"/*_labels.txt
   

board-test generates the N6-optimized binary with stedgeai, flashes it over serial, runs inference on every WAV on the SD card, and streams the top predictions back over UART. Add --save_results results.csv to capture them.

Validating on device

Release bundles do not ship fixed validation inputs. To run stedgeai validate against a downloaded model, supply your own representative audio — see Deployment — or re-run conversion locally to generate a validation set.