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README.md

@@ -1,3 +1,60 @@
 ---
 license: apache-2.0
+library_name: pytorch
+pipeline_tag: audio-classification
+base_model:
+- justinchuby/Perch-onnx
+- wrice/perch-v2-efficientnet-b3
+tags:
+- audio
+- bioacoustics
+- bird-classification
+- embeddings
 ---
+
+# PERCH 2 PyTorch
+
+PyTorch implementation of the complete PERCH 2 waveform model for
+bioacoustic classification and 1536-dimensional audio embeddings.
+
+## Load From Hugging Face
+
+```python
+import torch
+
+model = torch.hub.load("janclemenslab/perch2_torch", "perch_v2").eval()
+waveform = torch.zeros(5 * 32_000)  # Mono 32 kHz audio.
+
+with torch.no_grad():
+    outputs = model(waveform)
+
+index = outputs["label"][0].argmax()
+print(model.labels[index])
+```
+
+This call downloads `perch_v2_torch.pt` from this Hugging Face repository and
+caches it locally.
+
+Inputs are mono, 32 kHz waveforms with shape `(time,)` or `(batch, time)`.
+Audio longer than five seconds is processed in overlapping windows and pooled.
+
+## Outputs
+
+- `embedding`: `(batch, 1536)` global embedding
+- `spatial_embedding`: `(batch, time, frequency, 1536)` unpooled embedding
+- `spectrogram`: log-mel spectrogram
+- `label`: `(batch, 14795)` uncalibrated class logits
+
+`model.labels[index]` maps a logit index to its embedded class name. Thresholds
+should be calibrated for the target data.
+
+## Reproducibility
+
+The conversion code, demo notebook, and verification command are available in
+[janclemenslab/perch2_torch](https://github.com/janclemenslab/perch2_torch).
+
+## Attribution And License
+
+Derived from the Apache-2.0 [PERCH ONNX model](https://huggingface.co/justinchuby/Perch-onnx)
+and Apache-2.0 [wrice EfficientNet-B3 checkpoint](https://huggingface.co/wrice/perch-v2-efficientnet-b3).
+The class taxonomy comes from the Apache-2.0 [PERCH model release](https://huggingface.co/cgeorgiaw/Perch).