---
license: mit
language:
- en
base_model:
- ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition
---


Speech Emotion Recognition - 6-Class Classifier

This model is a fine-tuned version of ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition, specifically designed to classify emotions in English speech.

🧠 Emotion Classes

The model predicts one of the following six emotions:

Happy

Angry

Disgust

Fearful

Neutral

Sad

📊 Dataset

The model was trained on the Speech Emotion Recognition dataset from Kaggle:
🔗 https://www.kaggle.com/datasets/kevinignatiuswijaya/speech-emotion-recognition-dl

🎯 Accuracy
Achieved an accuracy of 84% on the test set.

🔧 Base Model
Fine-tuned from the pretrained model:
ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition


# Load model and feature extractor
model = Wav2Vec2ForSequenceClassification.from_pretrained("your-username/your-model-name")
extractor = Wav2Vec2FeatureExtractor.from_pretrained("your-username/your-model-name")

# Create pipeline
classifier = pipeline("audio-classification", model=model, feature_extractor=extractor)

# Predict emotion
result = classifier("path/to/audio.wav")
print(result)


🧪 Applications
This model can be used for:

Emotion-aware virtual assistants

Mental health monitoring tools

Human-computer interaction research

Call center emotion analytics

📁 License

Ensure compliance with the licenses for both the Kaggle dataset and the pretrained model used.