Real-Time Speech Emotion Recognition System Based on Wav2Vec 2.0: Let AI Understand Your Emotions

Introducing the open-source project Speech-Emotion-Recognition, built using Meta (formerly Facebook) Wav2Vec2.0 pre-trained model and deep learning technologies. It supports detection of 8 emotions and real-time microphone input, making it an excellent practice in the field of speech emotion recognition.

Speech Emotion Recognition (SER) is a key direction in human-computer interaction. Traditional methods rely on handcrafted features like MFCC, which struggle to capture rich contextual information. This project uses Wav2Vec2.0 as the core feature extractor, which automatically learns deep speech representations (including semantic and emotional information) from raw audio through large-scale unsupervised pre-training.

The system workflow is concise and efficient: Raw speech audio → Wav2Vec2.0 encoder → Speech embedding vector → Emotion classifier → Emotion prediction result. It supports 8 basic emotions: Happy (rising and light tone), Sad (slow speech rate and low pitch), Angry (loud volume and fast speech rate), Fearful (trembling voice and unstable tone), Neutral (stable with no obvious tendency), Calm (soft and soothing), Disgust (tone of repulsion), Surprised (sudden tone change).

The RAVDESS emotional speech dataset is used for training and evaluation. This dataset contains 8 emotion samples recorded by 24 professional actors, with features: accurate emotional expression, diverse sentence content to avoid bias, high audio quality, and unified sampling rate.

Supports real-time microphone input detection and can run on Google Colab. After browser authorization, it analyzes the speech stream in real time and outputs results. The real-time capability is due to: Wav2Vec2.0's efficient encoder, GPU-accelerated inference, and optimized audio preprocessing. Tech stack: Python, PyTorch, Hugging Face Transformers, Librosa, Scikit-learn, Google Colab.

Potential application scenarios: Customer service industry (real-time monitoring of customer emotions for early warning of escalation), mental health (assisting in emotion recognition to support counseling), education (analyzing student engagement and learning emotions), in-vehicle systems (monitoring driver emotions for safety reminders). Future expansions: BiLSTM+Attention to improve accuracy, integration with Whisper for joint speech recognition and emotion modeling, Streamlit web interface, FastAPI deployment, Docker containerization to simplify deployment.

The Speech-Emotion-Recognition project demonstrates the practical application of cutting-edge pre-trained speech models in emotion recognition tasks. It balances accuracy and real-time performance through Wav2Vec2.0 feature extraction and deep learning classification. It is an excellent learning resource and starting point for developers in the speech AI field.