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Kruti1234/
mindcare-audio-emotion

Name: mindcare-audio-emotion
Availability: InStock

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MindCare Audio Emotion Recognition Model

This model detects emotions from speech audio.

Model Details

Task: Audio Emotion Recognition
Dataset: RAVDESS
Architecture: CNN
Accuracy: 91.32%
Emotions: 8 classes

Emotions Detected

angry, calm, disgust, fearful, happy, neutral, sad, surprised

Usage

python

from huggingface_hub import hf_hub_download
import tensorflow as tf
import pickle

# Download model
model_path = hf_hub_download(repo_id="Kruti1234/mindcare-audio-emotion", filename="mindcare_audio_emotion_model.h5")
model = tf.keras.models.load_model(model_path)

# Download label encoder
encoder_path = hf_hub_download(repo_id="Kruti1234/mindcare-audio-emotion", filename="audio_label_encoder.pkl")
with open(encoder_path, 'rb') as f:
    label_encoder = pickle.load(f)

Model Info

Input: MFCC features (40, 174, 1)
Output: 7 emotion probabilities
Framework: TensorFlow/Keras

Training

Trained on RAVDESS dataset with data augmentation.

License

Apache 2.0