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ilyassmoummad/
ProtoCLR

Name: ProtoCLR
Availability: InStock

Bioacoustics

pytorch

feature-extraction

ilyassmoummad/Xeno-Canto-6s-16khz

2103.15808

2409.08589

cc-by-4.0

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ProtoCLR

This repository contains a CvT-13 Convolutional Vision Transformer model trained from scratch on the Xeno-Canto dataset, specifically on 6-second audio segments sampled at 16 kHz. The model is trained on Mel spectrograms of bird sounds using ProtoCLR (Prototypical Contrastive Loss) for 300 epochs and can be used as a feature extractor for bird audio classification and related tasks.

Files

cvt.py: Defines the CvT-13 model architecture.
protoclr.pth: Pre-trained model weights for ProtoCLR.
config/: Configuration files for CvT-13 setup.
mel_spectrogram.py: Contains the MelSpectrogramProcessor class, which converts audio waveforms into Mel spectrograms, a format suitable for model input.

Setup

Clone this repository: Clone the repository and navigate into the project directory: git clone https://huggingface.co/ilyassmoummad/ProtoCLR cd ProtoCLR/
Install dependencies: Ensure you have the required Python packages, including torch and any other dependencies listed in requirements.txt.

pip install -r requirements.txt

Usage

Prepare the Audio:
To ensure compatibility with the model, follow these preprocessing steps for your audio files:
- Mono Channel (Mandatory):
  If the audio has multiple channels, convert it to a single mono channel by averaging the channels.
- Sample Rate (Mandatory):
  Resample the audio to a consistent sample rate of 16 kHz.
- Padding (Recommended):
  For audio files shorter than 6 seconds, pad with zeros or repeat the audio until it reaches a length of 6 seconds.
- Chunking (Recommended):
  For audio files longer than 6 seconds, split them into chunks of 6 seconds each for better processing.
Process the Audio:
Use the MelSpectrogramProcessor (from melspectrogram.py) to transform the prepared audio into a Mel spectrogram, a format suitable for model input, as demonstrated in the following example.

Example Code

The following example demonstrates loading, processing, and running inference on an audio file:

python

import torch
from cvt import cvt13  # Import model architecture
from melspectrogram import MelSpectrogramProcessor  # Import Mel spectrogram processor

# Initialize the preprocessor and model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
preprocessor = MelSpectrogramProcessor(device=device)
model = cvt13()

# Load weights trained using Cross-Entropy
model.load_state_dict(torch.load("ce.pth", map_location="cpu")['encoder'])

# Load weights trained using SimCLR (self-supervised contrastive learning)
model.load_state_dict(torch.load("simclr.pth", map_location="cpu"))

# Load weights trained using SupCon (supervised contrastive learning)
model.load_state_dict(torch.load("supcon.pth", map_location="cpu"))

# Load weights trained using ProtoCLR (supervised contrastive learning using prototypes)
model.load_state_dict(torch.load("protoclr.pth", map_location="cpu"))

# Optional: Move the model to GPU for faster processing if available using : model = model.to('cuda') , for instance.
model = model.to(device)
model.eval()

# Load and preprocess a sample audio waveform
def load_waveform(file_path):
    # Replace this with your specific audio loading function
    # For example, using torchaudio to load and resample
    pass

waveform = load_waveform("path/to/audio.wav").to(device)  # Load your audio file here and convert it to a PyTorch tensor.

# Ensure waveform is sampled at 16 kHz, then pad/chunk as needed for 6s length
input_tensor = preprocessor.process(waveform).unsqueeze(0)  # Add batch dimension

# Run the model on the preprocessed audio
with torch.no_grad():
    output = model(input_tensor)
    print("Model output shape:", output.shape)

Model Performance Comparison

The following table presents the classification accuracy of various models on one-shot and five-shot bird sound classification tasks, evaluated across different soundscape datasets.

Model	Training Params (M)	PER	NES	UHH	HSN	SSW	SNE	Mean
Random Guessing	-	0.75	1.12	3.70	5.26	1.04	1.78	2.22

1-Shot Classification
BirdAVES-biox-base	95	7.41±1.0	26.4±2.3	13.2±3.1	9.84±3.5	8.74±0.6	14.1±3.1	13.2
BirdAVES-bioxn-large	316	7.59±0.8	27.2±3.6	13.7±2.9	12.5±3.6	10.0±1.4	14.5±3.2	14.2
BioLingual	153	6.21±1.1	37.5±2.9	17.8±3.5	17.6±5.1	22.5±4.0	26.4±3.4	21.3
Perch	80	9.10±5.3	42.4±4.9	19.8±5.0	26.7±9.8	22.3±3.3	29.1±5.9	24.9
CE (Ours)	23	9.55±1.5	41.3±3.6	19.7±4.7	25.2±5.7	17.8±1.4	31.5±5.4	24.2
SimCLR (Ours)	19	7.85±1.1	31.2±2.4	14.9±2.9	19.0±3.8	10.6±1.1	24.0±4.1	17.9
SupCon (Ours)	19	8.53±1.1	39.8±6.0	18.8±3.0	20.4±6.9	12.6±1.6	23.2±3.1	20.5
ProtoCLR (Ours)	19	9.23±1.6	38.6±5.1	18.4±2.3	21.2±7.3	15.5±2.3	25.8±5.2	21.4

5-Shot Classification
BirdAVES-biox-base	95	11.6±0.8	39.7±1.8	22.5±2.4	22.1±3.3	16.1±1.7	28.3±2.3	23.3
BirdAVES-bioxn-large	316	15.0±0.9	42.6±2.7	23.7±3.8	28.4±2.4	18.3±1.8	27.3±2.3	25.8
BioLingual	153	13.6±1.3	65.2±1.4	31.0±2.9	34.3±3.5	43.9±0.9	49.9±2.3	39.6
Perch	80	21.2±1.2	71.7±1.5	39.5±3.0	52.5±5.9	48.0±1.9	59.7±1.8	48.7
CE (Ours)	23	21.4±1.3	69.2±1.8	35.6±3.4	48.2±5.5	39.9±1.1	57.5±2.3	45.3
SimCLR (Ours)	19	15.4±1.0	54.0±1.8	23.0±2.3	32.8±4.0	22.0±1.2	40.7±2.4	31.3
SupCon (Ours)	19	17.2±1.3	64.6±2.4	34.1±2.9	42.5±2.9	30.8±0.8	48.1±2.4	39.5
ProtoCLR (Ours)	19	19.2±1.1	67.9±2.8	36.1±4.3	48.0±4.3	34.6±2.3	48.6±2.8	42.4

For additional details, please see the pre-print on arXiv and the official GitHub repository.

Citation

If you use our model in your research, please cite the following paper:

bibtex

@misc{moummad2024dirlbs,
      title={Domain-Invariant Representation Learning of Bird Sounds}, 
      author={Ilyass Moummad and Romain Serizel and Emmanouil Benetos and Nicolas Farrugia},
      year={2024},
      eprint={2409.08589},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2409.08589}, 
}

ilyassmoummad/ProtoCLR