ernensbjorn/perch-v2-int8-tflite

Perch V2 — Optimized TFLite Models for Raspberry Pi

Optimized variants of Google's Perch V2 bird vocalization classifier for edge deployment on Raspberry Pi and ARM64 devices.

Three model variants converted directly from the official Google SavedModel, each targeting a different performance/quality trade-off.

Models

Benchmarked on Raspberry Pi 5 Model B (8GB, Cortex-A76 @ 2.4GHz), 20 real bird recordings from 20 species, 5 runs each, 4 threads.

Quick Start

Choose your model

• —RPi 5 (4-8 GB): Use perch_v2_fp16.tflite — near-perfect accuracy, 2x smaller than original
• —RPi 4 (2-4 GB): Use perch_v2_dynint8.tflite — 4x smaller, 31% faster, very good accuracy
• —Desktop / reference: Use perch_v2_original.tflite — exact Google baseline

Usage

# Works with ai-edge-litert, tflite-runtime, or tensorflow
from ai_edge_litert.interpreter import Interpreter
import numpy as np

model_path = "perch_v2_fp16.tflite"  # or dynint8, or original
interpreter = Interpreter(model_path=model_path, num_threads=4)
interpreter.allocate_tensors()

inp = interpreter.get_input_details()
out = interpreter.get_output_details()

# Input: 5 seconds of audio at 32 kHz
audio = np.zeros((1, 160000), dtype=np.float32)  # replace with real audio
interpreter.set_tensor(inp[0]["index"], audio)
interpreter.invoke()

# Get species logits (14,795 classes)
logits = interpreter.get_tensor(out[3]["index"])[0]
top_species = np.argsort(logits)[-5:][::-1]

Download a single model

from huggingface_hub import hf_hub_download

# Download only the model you need
model_path = hf_hub_download(
    "ernensbjorn/perch-v2-int8-tflite",
    "perch_v2_fp16.tflite"
)

Model Details

Architecture

• —Backbone: EfficientNet-B3 (~12M params for embeddings)
• —Classification head: ~91M params (~101.8M total)
• —Input: 5.0 seconds @ 32,000 Hz = 160,000 float32 samples
• —Outputs:
• —Index 0: Spatial embeddings (16 x 4 x 1536)
• —Index 1: Temporal features
• —Index 2: 1536-dim global embedding
• —Index 3: 14,795 species logits (use this for classification)

Species Coverage

~10,340 bird species + frogs, insects, mammals (~14,795 total classes).

Use the included labels.txt for class names and bird_indices.json to filter bird-only species.

Quantization Methods

All variants were converted directly from the official Google Perch V2 SavedModel using tf.lite.TFLiteConverter with appropriate optimization flags. No binary patching or post-hoc manipulation.

Detailed Benchmarks

Raspberry Pi 5 (8 GB, Cortex-A76 @ 2.4 GHz, 4 threads)

• —Embedding cosine: Cosine similarity of the 1536-dim embedding vector vs the float32 baseline. Values > 0.99 indicate negligible quality loss for downstream tasks.
• —Top-1/Top-5 agreement: How often the quantized model's top predicted species matches the original's prediction.
• —Test data: 20 real field recordings from 20 species (Rougegorge familier, Courlis cendré, Grive mauvis, Sarcelle d'hiver, Râle d'eau, etc.)

Raspberry Pi 4 Estimates

The RPi 4 (Cortex-A72 @ 1.8 GHz) is roughly 2-3x slower than the RPi 5. Expected latencies:

For RPi 4 with 2 GB RAM, dynint8 is strongly recommended.

Origin

Converted from the official Google Perch V2 SavedModel (hosted by Google researcher cgeorgiaw on HuggingFace).

Created as part of the Birdash project — an open-source bird detection dashboard and engine for Raspberry Pi.

License

Apache 2.0 (same as the original Perch V2 model by Google)

Citation

If you use these models, please cite the original Perch V2 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}
}