Add BirdNET v2.4 ONNX variants (int8_arm, fp32), labels, model card, license, checksums

BirdNET_v2.4_fp32.onnx

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+size 62269581

BirdNET_v2.4_int8_arm.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a2323fe8f99c2af4bb325cf8f11b52748a86aa1f2c9e706c1ae7f907289521a
+size 46886045

LICENSE

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+BirdNET v2.4 (GLOBAL 6K) - License
+==================================
+
+These model files are derived from BirdNET v2.4, developed by the K. Lisa Yang Center
+for Conservation Bioacoustics (Cornell Lab of Ornithology) and Chemnitz University of
+Technology.
+
+Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
+International License (CC BY-NC-SA 4.0).
+
+  Full license text: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
+  Summary:           https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+You are free to share and adapt the material under these terms:
+
+  - Attribution   - You must give appropriate credit to BirdNET and indicate if changes
+                    were made. Required credit, displayed wherever the model is used:
+
+                        Powered by BirdNET (https://birdnet.cornell.edu/)
+
+  - NonCommercial - You may not use the material for commercial purposes.
+  - ShareAlike    - If you remix, transform, or build upon the material, you must
+                    distribute your contributions under the same license.
+
+Attribution to BirdNET is a hard requirement of this license and must not be removed.

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+  - audio
+  - audio-classification
+  - bioacoustics
+  - birds
+  - birdnet
+  - onnx
+library_name: onnx
+pipeline_tag: audio-classification
+---
+
+# BirdNET v2.4 (GLOBAL 6K) - ONNX variants
+
+ONNX builds of the **BirdNET GLOBAL 6K V2.4** bird sound classifier, optimized for
+edge deployment in [BirdNET-Go](https://github.com/tphakala/birdnet-go). This repo holds
+the precision/backend variants; the stock upstream TFLite model is unchanged and not
+re-hosted here.
+
+> **Powered by BirdNET (https://birdnet.cornell.edu/)**
+>
+> BirdNET is developed by the K. Lisa Yang Center for Conservation Bioacoustics at the
+> Cornell Lab of Ornithology and Chemnitz University of Technology. These ONNX files are
+> derived from the upstream BirdNET v2.4 model. Attribution to BirdNET is a hard license
+> requirement: do not strip it.
+
+## Model summary
+
+- **Classes:** 6,522 species (scientific + common name, see `labels.txt`)
+- **Sample rate:** 48 kHz
+- **Clip length:** 3 s (raw PCM waveform)
+- **Input tensor:** `input`, `float32`, shape `[batch, 144000]` (3 s x 48 kHz)
+- **Output tensor:** `output`, `float32`, shape `[batch, 6522]` (per-class logits; apply
+  sigmoid for confidence scores in `[0, 1]`)
+
+The two variants share an identical input/output interface, so they are drop-in
+replacements for one another.
+
+## Variants
+
+| File | Precision | Size | Backend / target | Notes |
+| --- | --- | --- | --- | --- |
+| `BirdNET_v2.4_int8_arm.onnx` | INT8 (MatMul-only) + FP32 conv | ~47 MB | ONNX Runtime on ARM / low-RAM CPU | Dynamic INT8 applied only to the 1024x6522 classification head; the CNN backbone stays FP32. ~98% top-1 agreement vs FP32. The recommended low-RAM CPU build. |
+| `BirdNET_v2.4_fp32.onnx` | FP32 | ~62 MB | OpenVINO (and full-precision reference) | Canonical full-precision master. Under OpenVINO it runs at f16 or f32 via `INFERENCE_PRECISION_HINT`. |
+
+### Precision notes
+
+- **CPU / ARM:** use `int8_arm`. Full all-ops INT8 (ConvInteger) is *not* shipped: it
+  breaks accuracy (~34% top-1) and has no fast ARM kernel. Only MatMul-only quantization
+  of the head is accuracy-safe.
+- **OpenVINO:** use `fp32`. The empty `INFERENCE_PRECISION_HINT` resolves to f16 on
+  fp16-capable hardware (A76 NEON, AVX512-FP16) and to f32 elsewhere. **Force
+  `INFERENCE_PRECISION_HINT=FP32` on GPU**, where f16 miscompiles.
+- f16 is intentionally not provided as a separate file: OpenVINO derives it from the FP32
+  master via the precision hint, and on CPU f16 uses *more* RAM than fp32 (the runtime
+  up-converts f16 weights to f32 at load).
+
+> Note: this is the **bird classifier**. The BirdNET v2.4 backbone is also used as an
+> embedding extractor for bat detection; that embedding model lives separately at
+> [`tphakala/BattyBirdNET-onnx`](https://huggingface.co/tphakala/BattyBirdNET-onnx) and
+> must stay FP32 (its raw embedding output overflows at f16).
+
+## Labels
+
+`labels.txt` has 6,522 lines, one per class, in BirdNET order. Format is
+`Scientific name_Common name`, for example:
+
+```
+Abroscopus albogularis_Rufous-faced Warbler
+```
+
+Output index `i` corresponds to line `i` of `labels.txt`.
+
+## Usage (ONNX Runtime, Python)
+
+```python
+import numpy as np, onnxruntime as ort
+
+sess = ort.InferenceSession("BirdNET_v2.4_int8_arm.onnx")
+
+# 3 s of 48 kHz mono PCM as float32, shape [1, 144000]
+audio = np.zeros((1, 144000), dtype=np.float32)
+
+logits = sess.run(["output"], {"input": audio})[0]   # [1, 6522]
+conf = 1.0 / (1.0 + np.exp(-logits))                  # sigmoid -> [0, 1]
+labels = open("labels.txt").read().splitlines()
+top = conf[0].argmax()
+print(labels[top], float(conf[0, top]))
+```
+
+## Checksums
+
+See `SHA256SUMS`.
+
+## License
+
+BirdNET v2.4 is distributed under **CC BY-NC-SA 4.0** (non-commercial, share-alike,
+attribution required). See `LICENSE` and keep the BirdNET attribution above with any use
+or redistribution.
+
+## Source
+
+- Upstream: [birdnet-team/BirdNET-Analyzer](https://github.com/birdnet-team/BirdNET-Analyzer)
+- ONNX conversion + quantization recipes: [tphakala/birdnet-go](https://github.com/tphakala/birdnet-go)

SHA256SUMS

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+6a2323fe8f99c2af4bb325cf8f11b52748a86aa1f2c9e706c1ae7f907289521a  BirdNET_v2.4_int8_arm.onnx
+f49b686838f62fc8c5bcb4364cd514c64882f6fd666c204aa5d4eb80a7795264  BirdNET_v2.4_fp32.onnx
+487937b6ad132b8506215523209a87b86adc9dce8e5ed3048ce9268189dddd3d  labels.txt