This project aims to recognize emotions in human speech using Machine Learning techniques. It uses various datasets such as RAVDESS, TESS, EMO-DB, and a custom dataset. The project is built with Python and uses libraries like TensorFlow, Librosa, NumPy, Pandas, SoundFile, Wave, Scikit-Learn, TQDM, Matplotlib, PyAudio, and FFMPEG.
This repository uses four datasets:
- RAVDESS: The Ryson Audio-Visual Database of Emotional Speech and Song.
- TESS: Toronto Emotional Speech Set.
- EMO-DB: A database of emotional utterances spoken by actors.
- Custom: An unbalanced noisy dataset that can be easily added to or removed from.
To run this project, you need to have the following Python packages installed:
- tensorflow
- librosa==0.6.3
- numpy
- pandas
- soundfile==0.9.0
- wave
- scikit-learn==0.24.2
- tqdm==4.28.1
- matplotlib==2.2.3
- pyaudio==0.2.11
- ffmpeg (optional)
You can install these packages using pip:
The core of this project lies in preprocessing audio files. We extract features from the audio files using LibROSA and store them in the storage using SoundFile.
We define a convolutional neural network (CNN) architecture with convolutional layers, pooling layers, and fully connected layers. and train the CNN model using the extracted features and the corresponding emotion labels.
To predict an emotion from an audio file, pass the audio path to the rec.predict() method:
This project is licensed under the terms of the MIT license.