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mindcare-audio-emotion
Kruti1234/mindcare-audio-emotion
mindcare-audio-emotion is a machine learning model from Kruti1234. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model detects emotions from speech audio.
All-time downloads
44
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Repo size
158 MB
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0
Public
Repo makeup
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.h5158 MB · 100%
At a glance
- Model type
- CNN
- Access
- Public
- Created
- Dec 26, 2025
- Updated
- Feb 16, 2026
- SHA
- c105e70b
Model card
From the Hugging Face model README
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
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
- Type
- CNN
- Created
- Dec 26, 2025
- Updated
- Feb 16, 2026