Simple neural net to classify the emotion in an audio
In this notebook I experienced with audio classification, the dataset has 1400 audios of spoken sentences in each of the 7 emotional states (happy, angry, pleasant, disgust, sad, neutral and fear). My goal is to preprocess the raw audio into a form that a neural network can learn from, for that I used Melspectrograms, and them build a model that can accurately classify a voice tone as being in one of the mentioned class.
- Process raw audio data to be neural net ready
- Build a classififer that can tell the emotion in a voice
- Extracting audio files
- Extracting Mel Spectrograms
- Audio transformation
- Creating the target dataset
- Train/Test split
- Creating the data loaders
- Building the Classifier
- Training and inference
- Trying out the model
- Conclusion
- Pandas
- Nmupy
- Torchaudio
- Librosa
- Iphython
- Sklearn
- Glob