Update README.md

README.md

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----
-license: mit
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+---
+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.