Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- audio-classification
+- emotion-recognition
+- hubert
+- speech
+library_name: transformers
+pipeline_tag: audio-classification
+---
+
+# HuBERT Emotion Recognition Model
+
+Fine-tuned HuBERT model for emotion recognition in speech audio.
+
+## Model Description
+
+This model classifies speech audio into 5 emotion categories:
+
+1. **Angry/Fearful** - Expressions of anger or fear
+2. **Happy/Laugh** - Joyful or laughing expressions  
+3. **Neutral/Calm** - Neutral or calm speech
+4. **Sad/Cry** - Expressions of sadness or crying
+5. **Surprised/Amazed** - Surprised or amazed reactions
+
+## Quick Start
+
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline("audio-classification", model="YOUR_USERNAME/hubert-emotion-recognition")
+
+# Predict emotion
+result = classifier("audio.wav")
+print(result)
+```
+
+## Detailed Usage
+
+```python
+from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
+import torch
+import librosa
+
+# Load model and processor
+model = AutoModelForAudioClassification.from_pretrained("YOUR_USERNAME/hubert-emotion-recognition")
+processor = Wav2Vec2FeatureExtractor.from_pretrained("YOUR_USERNAME/hubert-emotion-recognition")
+
+# Load audio (16kHz)
+audio, sr = librosa.load("audio.wav", sr=16000)
+
+# Prepare inputs
+inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
+
+# Get predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)[0]
+    pred_id = torch.argmax(probs).item()
+
+# Show results
+emotions = ["Angry/Fearful", "Happy/Laugh", "Neutral/Calm", "Sad/Cry", "Surprised/Amazed"]
+print(f"Emotion: {emotions[pred_id]}")
+print(f"Confidence: {probs[pred_id]:.3f}")
+```
+
+## Model Details
+
+- **Base Model**: HuBERT
+- **Task**: Audio Classification
+- **Sample Rate**: 16kHz
+- **Max Duration**: 3 seconds
+- **Framework**: PyTorch + Transformers
+
+## Training Data
+
+[Describe your training dataset here - name, size, speakers, etc.]
+
+## Performance
+
+[Add your evaluation metrics here]
+
+Example:
+- Accuracy: 87.3%
+- F1 Score: 85.1%
+
+## Limitations
+
+- Optimized for English speech
+- Works best with clear audio (3 seconds)
+- Performance may vary with background noise
+- Emotion expression varies across cultures
+
+## Intended Uses
+
+✅ Call center analytics  
+✅ Mental health monitoring  
+✅ Voice assistants  
+✅ Media analysis  
+✅ Research in affective computing  
+
+## License
+
+Apache 2.0
+
+## Citation
+
+```bibtex
+@misc{hubert_emotion_2024,
+  author = {YOUR_NAME},
+  title = {HuBERT Emotion Recognition},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/YOUR_USERNAME/hubert-emotion-recognition}
+}
+```