Update read me

README.md

@@ -20,19 +20,30 @@ tags:
 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
+
 You can install and use the model in two different ways:
+
 ### Option 1: Clone the repository
+
 To download the entire repository containing the code, model, and associated files, follow these steps:
-``` bash
+
+```bash
 git clone https://huggingface.co/tlmk22/QuefrencyGuardian
 cd QuefrencyGuardian
 pip install -r requirements.txt
 ```
+
 Once installed, you can directly import the files into your existing project and use the model.
+
+---
+
 ### Option 2: Dynamically load from the Hub
+
 If you only want to download the required files to use the model (without cloning the full repository), you can use the `hf_hub_download` function provided by Hugging Face. This method downloads only what is necessary directly from the Hub.
+
 Here's an example:
-``` python
+
+```python
 from huggingface_hub import hf_hub_download
 import importlib.util
 
@@ -58,8 +69,10 @@ result = model.predict("path/to/audio.wav")
 map_labels = {0: "chainsaw", 1: "environment"}
 print(f"Prediction Result: {map_labels[result[0]]}")
 ```
+
 Depending on your needs, you can either clone the repository for a full installation or use Hugging Face's dynamic download functionalities for lightweight and direct usage.
 
+---
 
 ## Model Overview
 
@@ -67,26 +80,35 @@ Depending on your needs, you can either clone the repository for a full installa
 
 The model uses:
 - **Spectrogram Features**
-- **Cepstral Features**: Calculated as the FFT of the log spectrogram between [`f_min`-`f_max`] in a filtered quefrency range  [`fc_min`-`fc_max`].
-- **Time Averaging**: Both feature sets are averaged along the whole audio clip for robustness in noisy settings (Welch methodology)
+- **Cepstral Features**: Calculated as the FFT of the log spectrogram between [`f_min`-`f_max`] in a filtered quefrency range [`fc_min`-`fc_max`].
+- **Time Averaging**: Both feature sets are averaged across the entire audio clip for robustness in noisy settings (Welch methodology).
+
+---
 
 ### LightGBM Model
 
-The model is a **binary classifier** (chainsaw vs environment) trained on the `rfcx/frugalai` dataset. 
+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
 
 Two example scripts demonstrating how to use the repository or the model downloaded from Hugging Face are available in the `examples` directory.
 
+---
+
 ### Performance
 
 - **Accuracy**: Achieved 95% on the test set with a 4.5% FPR at the default threshold during the challenge, where this model won first place.
-- **Environmental Impact**: Inference energy consumption was measured at **0.21 Wh**, tracked using CodeCarbon. This metric is dependent on the challenge's infrastructure, as the code was run within a Docker container provided by the platform.
+- **Environmental Impact**: Inference energy consumption was measured at **0.21 Wh**, tracked using CodeCarbon. This metric is dependent on the challenge's infrastructure, as the code was executed within a Docker container provided by the platform.
+
+---
 
 ### 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.
+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.
 
 ---
 
@@ -98,11 +120,9 @@ The model was trained and evaluated on the [Rainforest Connection (RFCx) Frugal
 - `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.
+- **Environmental Noise**: The model might misclassify if recordings are noisy or if machinery similar to chainsaws is present.