---
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
- das
- 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).