Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+---
+# Emotion Detection From Speech
+
+This model is the fine-tuned version of **DistilHuBERT** which classifies emotions from audio inputs.
+
+## Approach
+1. **Dataset:** The **Ravdess** dataset, comprising 1,440 audio files with 8 emotion labels: calm, happy, sad, angry, fearful, surprise, neutral, and disgust.
+2. **Model Fine-Tuning:** The DistilHuBERT model was fine-tuned for 7 epochs with a learning rate of 5e-5, achieving an accuracy of 98% on the test dataset.
+
+## Data Preprocessing
+- **Sampling Rate**: Audio files were resampled to 16kHz to match the model's requirements.
+- **Padding:** Audio clips shorter than 30 seconds were zero-padded.
+- **Train-Test Split:** 80% of the samples were used for training, and 20% for testing.
+
+##  Model Architecture
+- **DistilHuBERT:** A lightweight variant of HuBERT, fine-tuned for emotion classification.
+- **Fine-Tuning Setup:**
+    - Optimizer: AdamW
+    - Loss Function: Cross-Entropy
+    - Learning Rate: 5e-5
+    - Warm-up Ratio: 0.1
+    - Epochs: 7
+ 
+## Results
+- **Accuracy:** 0.98 on the test dataset
+- **Loss:** 0.10 on the test dataset
+
+## Usage
+from transformers import pipeline
+
+pipe = pipeline(
+    "audio-classification",
+    model="BilalHasan/distilhubert-finetuned-ravdess",
+)
+
+emotion = pipe(path_to_your_audio)
+
+## Demo
+You can access the live demo of the app on [Hugging Face Spaces](https://huggingface.co/spaces/BilalHasan/Mood-Based-Yoga-Session-Recommendation).