Perch V2 8-bit

Perch is a bioacoustics model trained to classify nearly 15,000 species, of which ~10,000 are birds.

✨ Key Highlights

• Optimized for edge deployment — A quantized 8-bit variant of the Perch model capable of running in real time on Raspberry Pi 5.

📦 Model Details

Model Description

This package contains a modified variant of the Perch V2 audio classifier distributed in the ernensbjorn/perch-v2-int8-tflite repository. It accepts a batch containing one five-second, 32 kHz audio clip and returns probabilities for 14,795 classes together with intermediate model representations.

• Developed by: Google (upstream model)
• Model type: Audio classifier
• License: Apache-2.0
• Base model: ernensbjorn/perch-v2-int8-tflite
• Packaged variant: 8-bit LiteRT model with softmax output

Model Sources

• Base model artifact: perch_v2_original.tflite at revision 71fd62f8144c6214e494da9a70065b2ce55a5ad4
• Upstream Perch V2 model: Google Bird Vocalization Classifier

🚀 Get Started with the Model

Run with Compute Flow

Follow these steps to run Perch V2 8-bit on macOS® with Apple Silicon or on Linux® with an Arm®-based CPU, such as a Raspberry Pi® 5.

Step 1: Clone Compute Flow and set up the virtual environment

git clone https://github.com/ARM-software/ComputeFlow.git && cd ComputeFlow

uv sync && source .venv/bin/activate

Step 2: Install the brick and model

compute-flow update
compute-flow model install audio_detection:perch perch-v2-8bit
compute-flow build native

Step 3: Run the Python CLI example

python -m cli_examples.audio_detection_cli \
  -m @manifest/perch/perch-v2-8bit/perch_v2_8bit_manifest.json \
  -a @sample/erithacus_rubecula.wav \
  -t 1

The example classifies the bundled samples/erithacus_rubecula.wav recording. Replace -a with a path to your own audio file.

📊 Quality evaluation

For accuracy results, refer to the ernensbjorn/perch-v2-int8-tflite model card.

🎯 Performance evaluation

Performance results for this package will be reported in a future update.

🛠️ Technical Specifications

Objective

The package provides Perch V2 for audio detection and classification.

Runtime Architecture

Precision and Quantization

The packaged artifact is the repository's 8-bit LiteRT variant. Its public tensor interface uses float32 for the audio input and all four outputs.

Input Specification

Output Specification

Repository Contents

• assets/class_names.json — class-name mapping referenced by the manifest.
• perch_v2_8bit.tflite — LiteRT audio-classification model referenced by the manifest.
• perch_v2_8bit_manifest.json — audio-classifier manifest.
• samples/erithacus_rubecula.wav — audio sample for testing the model with audio_detection_cli.
• SHA256SUMS — model-package checksums for reproducibility.

🗂️ Model and Asset Origin

• Model: perch_v2_8bit.tflite was derived from perch_v2_original.tflite. The following changes were made for this package:

  • Set the batch dimension to 1.
  • Added a softmax layer to the label output so it returns probabilities.
  • Corrected the label signature entry to reference the actual subgraph output tensor.
  • Replaced 365 explicit BROADCAST_TO operations with equivalent implicit broadcasting and removed unreachable shape calculations.

• Class names: assets/class_names.json was transformed from labels.txt.

• Sample: samples/erithacus_rubecula.wav was created from Wikimedia Commons' European-robin recording, credited to Gilles San Martin, with:

  ffmpeg -y \
      -i 120401-132827_Erithacus_rubecula.ogg \
      -ac 1 \
      -ar 32000 \
      -t 55 \
      -af apad \
      -c:a pcm_f32le \
      samples/erithacus_rubecula.wav
  

🔐 Checksums

SHA256SUMS was generated by recursively hashing every regular file in the model bundle, including files in subdirectories, except the generated root SHA256SUMS and paths with a dotfile component.

From the model bundle root, verify the checked-out files with:

shasum -a 256 -c SHA256SUMS

Citation

If you use this model, cite the original Perch work:

@article{ghani2023global,
  title={Global birdsong embeddings enable superior transfer learning for bioacoustic classification},
  author={Ghani, Burooj and Denton, Tom and Kahl, Stefan and Klinck, Holger},
  journal={Scientific Reports},
  year={2023}
}