Create README.md

README.md

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+---
+language:
+- en
+- id
+tags:
+- poultry
+- chicken
+- animal-health
+- vocalization-analysis
+- early-disease-detection
+- sound-classification
+- pytorch
+- Indonesia
+datasets:
+- IceKhoffi/chicken-health-behavior-multimodal
+---
+
+# `Chicken Vocalization Classifier`
+
+This model is designed for classifying chicken vocalizations into categories indicative of health status or environmental noise. It serves as a crucial audio-based component within the "Chicken Health & Behavior Detection" multimodal project, aiming to aid in the early detection of poultry diseases and the monitoring of farm conditions.
+
+## Model Description
+
+The `chicken-vocalization-classifier` is a Convolutional Neural Network (CNN) built with PyTorch, designed to process Log-Mel Spectrogram representations of audio recordings. It categorizes chicken sounds into three classes: `Healthy`, `Noise`, and `Unhealthy`. This model can help identify abnormal vocalizations (e.g., coughing, distress calls) that might signal health issues, or distinguish between relevant chicken sounds and general farm noise.
+
+## Training Data
+
+This model was trained using the "Poultry Vocalization Signal Dataset for Early Disease Detection".
+
+## Training Procedure
+
+The model was implemented and trained using the PyTorch framework
+
+* **Model Architecture:** The model, named `ModdifiedModel`, consists of a `features` extractor and a `classifier` head.
+  * **Features Extractor (Sequential):** Composed of three blocks, each containing a `Conv2d` layer, `BatchNorm2d`, `ReLU` activation, and `MaxPool2d`.
+    * Block 1: `Conv2d(1, 32, kernel_size=3)`, `BatchNorm2d(32)`, `ReLU()`, `MaxPool2d(2)`
+    * Block 2: `Conv2d(32, 64, kernel_size=3)`, `BatchNorm2d(64)`, `ReLU()`, `MaxPool2d(2)`
+    * Block 3: `Conv2d(64, 128, kernel_size=3)`, `BatchNorm2d(128)`, `ReLU()`, `MaxPool2d(2)`
+  * **Classifier (Sequential):** Contains a `Flatten` layer, two `Linear` layers, `Dropout`, and `ReLU` activation.
+    * `Linear(in_features=25088, out_features=256)`
+    * `Dropout(0.5)`
+    * `ReLU()`
+    * `Linear(in_features=256, out_features=3)` (for 3 classes)
+
+* **Preprocessing:** Audio files are converted to Log-Mel Spectrograms using `librosa`.
+  * `SAMPLE_RATE = 22050` Hz
+  * Audio is sampled to approximately 1.5 seconds (`WAV_SIZE = int(1.5 * SAMPLE_RATE)`)
+  * `MEL_BANDS = 128`
+  * `N_FFT = 2648`
+  * `HOP_LENGTH = 256`
+
+* **Data Splitting:** The dataset was split into training and testing sets using `train_test_split` with `test_size=0.2` and `random_state=27`
+* **Loss Function:** `nn.CrossEntropyLoss()`
+* **Optimizer:** `torch.optim.Adam` with a learning rate (`lr`) of `0.001`
+* **Epochs:** The model was trained for `30` epochs
+* **Batch Size:** Training was performed with a `batch_size` of `32`
+
+## Performance
+
+The Modified model was evaluated on a test set.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/67524d7300134bb0ad1503a7/hKRNactESnugCNA5abcQO.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/67524d7300134bb0ad1503a7/ttLqWCRr-6L63pRZ5tasu.png)
+
+## How to Use
+
+You can load this trained model's weights with PyTorch. For full usage examples, including audio preprocessing steps and inference, please refer to the `CHBD_Vocalization_Analysis.ipynb` notebook provided in this repository.
+
+```python
+import torch
+import torch.nn as nn
+
+# Define the ModdifiedModel class
+# (You will need to copy this class definition from the CHBD_Vocalization_Analysis.ipynb file)
+class ModdifiedModel(nn.Module):
+    def __init__(self, num_classes=3):
+        super(ModdifiedModel, self).__init__()
+        # ... (copy the full model architecture definition here) ...
+
+    def forward(self, x):
+        # ... (copy the forward pass definition here) ...
+
+# Instantiate the model
+model = ModdifiedModel(num_classes=3)
+
+# Define the path to your model weights
+model_weights_path = ''
+
+state_dict = torch.hub.load_state_dict_from_url(model_weights_path, map_location='cpu')
+model.load_state_dict(state_dict)
+
+# Set model to evaluation mode
+model.eval()
+
+# The model is now loaded and ready for inference.
+# Refer to the provided .ipynb for detailed preprocessing and inference examples.