🎧 Speech Emotion Recognition with Whisper This project leverages the Whisper model to recognize emotions in speech. The goal is to classify audio recordings into different emotional categories, such as Happy , Sad , Surprised , and etc. 🗂 Dataset The dataset used for training and evaluation is sourced from multiple datasets, including: RAVDESS SAVEE TESS URDU The dataset contains recordings labeled with various emotions. Below is the distribution of the emotions in the dataset: Emotion Count sad 752 happy 752 angry 752 neutral 716 disgust 652 fearful 652 surprised 652 calm 192 This distribution reflects the balance of emotions in the dataset, with some emotions having more samples than others. Excluded the "calm" emotion during training due to its underrepresentation. 🎤 Preprocessing Audio Loading : Using Librosa to load the audio files and convert them to numpy arrays. Feature Extraction : The audio data is processed using the Whisper Feature Extractor , which standardizes and normalizes the audio features for input to the model. 🔧 Model The model used is the Whisper Large V3 model, fine tuned for audio classification tasks: Model : openai/whisper large v3 Output : Emotion lab…
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