Update README.md

README.md

@@ -1,92 +1,106 @@
----
-license: cc-by-nc-4.0
----
-# Quefrency Guardian: Chainsaw Noise Detector
-
-An efficient model to detect chainsaw activity in forest soundscapes using spectral and cepstral audio features. The model is designed for environmental conservation and is based on a LightGBM classifier, capable of low-energy inference on both CPU and GPU devices. This repository provides the complete code and configuration for feature extraction, model implementation, and deployment.
-
-## Installation
-
-To use the model, clone the repository and install the dependencies:
-
-```bash
-git clone https://huggingface.co/your_username/your_model_name
-cd your_model_name
-pip install -r requirements.txt
-```
-
-## Model Overview
-
-### Features
-
-The model uses:
-- **Spectrogram Features**: Extracted from frequencies between 70-1525 Hz.
-- **Cepstral Features**: Calculated as the FFT of the log spectrogram in a filtered quefrency range.
-- **Time Averaging**: Both feature sets are averaged over the time domain for robustness in noisy settings.
-
-### LightGBM Model
-
-The model is a **binary classifier** (chainsaw vs environment) trained on the `rfcx/frugalai` dataset. Key model parameters are included in `model/lgbm_params.json`.
-
-## Usage
-
-### Load the Model and Parameters
-
-```python
-import json
-from fast_model import FastModel
-
-# Load parameters
-with open("model/features.json", "r") as f:
-    features = json.load(f)
-
-with open("model/lgbm_params.json", "r") as f:
-    lgbm_params = json.load(f)
-
-# Initialize the model
-model = FastModel(
-    feature_params=features,
-    lgbm_params=lgbm_params,
-    model_file="model/model.txt",  # Path to the serialized model file
-    device="cuda"                  # Use 'cpu' if GPU is unavailable
-)
-
-# Predict on a Dataset
-from datasets import load_dataset
-dataset = load_dataset("rfcx/frugalai")
-predictions = model.predict(dataset["test"])
-print(predictions)
-```
-
-### Performance
-
-- **Accuracy**: 95% on the test set with a 4.5% FPR at the default threshold.
-- **Low-Energy Mode**: Using only 1 second of audio inference reduces energy consumption by 50%, at the cost of ~1% accuracy.
-- **Environmental Impact**: Inference energy consumption is **0.21 Wh**, tracked using CodeCarbon.
-
-### License
-
-This project is licensed under the [Creative Commons Attribution Non-Commercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/). You are free to share and adapt the work for non-commercial purposes, provided attribution is given.
-
----
-
-## File Structure
-📂 your_model_name/ ├── 📂 model/ │ ├── model.txt # Pre-trained LightGBM model │ ├── features.json # Feature extraction parameters │ ├── lgbm_params.json # LightGBM parameters ├── 📜 README.md # Documentation ├── 📜 LICENSE.md # CC BY-NC 4.0 license ├── 📜 requirements.txt # Python dependencies └── 📜 fast_model.py # Model implementation
-
-
-## Dataset
-
-The model was trained and evaluated on the [Rainforest Connection (RFCx) Frugal AI](https://huggingface.co/datasets/rfcx/frugalai) dataset.
-
-#### Labels:
-- `0`: Chainsaw
-- `1`: Environment
-
-## Limitations
-
-- **Audio Length**: The classifier is designed for 1 to 3 seconds of audio sampled at either 12 kHz or 24 kHz.
-- **Environmental Noise**: The model might misclassify if recordings are noisy or machinery similar to chainsaws is present.
-
----
-
+---
+license: cc-by-nc-4.0
+datasets:
+- rfcx/frugalai
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: audio-classification
+tags:
+- acoustics
+- lgbm
+- frugality
+- signal-processing
+- climate
+- chainsaw
+---
+# Quefrency Guardian: Chainsaw Noise Detector
+
+An efficient model to detect chainsaw activity in forest soundscapes using spectral and cepstral audio features. The model is designed for environmental conservation and is based on a LightGBM classifier, capable of low-energy inference on both CPU and GPU devices. This repository provides the complete code and configuration for feature extraction, model implementation, and deployment.
+
+## Installation
+
+To use the model, clone the repository and install the dependencies:
+
+```bash
+git clone https://huggingface.co/your_username/your_model_name
+cd your_model_name
+pip install -r requirements.txt
+```
+
+## Model Overview
+
+### Features
+
+The model uses:
+- **Spectrogram Features**: Extracted from frequencies between 70-1525 Hz.
+- **Cepstral Features**: Calculated as the FFT of the log spectrogram in a filtered quefrency range.
+- **Time Averaging**: Both feature sets are averaged over the time domain for robustness in noisy settings.
+
+### LightGBM Model
+
+The model is a **binary classifier** (chainsaw vs environment) trained on the `rfcx/frugalai` dataset. Key model parameters are included in `model/lgbm_params.json`.
+
+## Usage
+
+### Load the Model and Parameters
+
+```python
+import json
+from fast_model import FastModel
+
+# Load parameters
+with open("model/features.json", "r") as f:
+    features = json.load(f)
+
+with open("model/lgbm_params.json", "r") as f:
+    lgbm_params = json.load(f)
+
+# Initialize the model
+model = FastModel(
+    feature_params=features,
+    lgbm_params=lgbm_params,
+    model_file="model/model.txt",  # Path to the serialized model file
+    device="cuda"                  # Use 'cpu' if GPU is unavailable
+)
+
+# Predict on a Dataset
+from datasets import load_dataset
+dataset = load_dataset("rfcx/frugalai")
+predictions = model.predict(dataset["test"])
+print(predictions)
+```
+
+### Performance
+
+- **Accuracy**: 95% on the test set with a 4.5% FPR at the default threshold.
+- **Low-Energy Mode**: Using only 1 second of audio inference reduces energy consumption by 50%, at the cost of ~1% accuracy.
+- **Environmental Impact**: Inference energy consumption is **0.21 Wh**, tracked using CodeCarbon.
+
+### License
+
+This project is licensed under the [Creative Commons Attribution Non-Commercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/). You are free to share and adapt the work for non-commercial purposes, provided attribution is given.
+
+---
+
+## File Structure
+📂 your_model_name/ ├── 📂 model/ │ ├── model.txt # Pre-trained LightGBM model │ ├── features.json # Feature extraction parameters │ ├── lgbm_params.json # LightGBM parameters ├── 📜 README.md # Documentation ├── 📜 LICENSE.md # CC BY-NC 4.0 license ├── 📜 requirements.txt # Python dependencies └── 📜 fast_model.py # Model implementation
+
+
+## Dataset
+
+The model was trained and evaluated on the [Rainforest Connection (RFCx) Frugal AI](https://huggingface.co/datasets/rfcx/frugalai) dataset.
+
+#### Labels:
+- `0`: Chainsaw
+- `1`: Environment
+
+## Limitations
+
+- **Audio Length**: The classifier is designed for 1 to 3 seconds of audio sampled at either 12 kHz or 24 kHz.
+- **Environmental Noise**: The model might misclassify if recordings are noisy or machinery similar to chainsaws is present.
+
+---
+
 This README serves as the primary documentation for Hugging Face and provides an overview of the model's purpose, data requirements, and usage.