Emotion Detector

--- 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 co...