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