mood-class is a classification tool that accepts inputs in the form of audio files and returns outputs in the form of text labels, corresponding to one in five emotion classes:
anger disgust fear happiness sadness
Have a speech utterance you'd like to know the overall tone of? Read on to learn more about how to use mood-class!
These next set of instructions will help you get a copy of mood-class and its corresponding requirements installed on your local machine.
mood-class and its various dependencies are all built on top of Python. If you do not have Python installed on your machine, follow this guide on how to do so via Homebrew. mood-class will run on Python versions 3 or higher.
We'll be making use of several Python libraries to get our classifier to work. We can use pip to pre-install these.
pip install numpy matplotlib scipy sklearn hmmlearn simplejson eyed3 pydub
If you don't have pip installed, you can check out the documentation for how to do so here. The above list of libraries are courtesy of pyAudioAnalysis, which is the framework we will use to perform the classification task.
Navigate to your preferred directory location and clone this repository.
git clone https://github.com/navierula/mood-class
git clone https://github.com/tyiannak/pyAudioAnalysis
Make sure that you place this repository within your mood-class directory, specifically where you will choose to perform the classification task. For reference, my placement looks like this:
mood-class/
|_audioClassication/
|__pyAudioAnalysis/
In order to get pyAudioAnalysis to work, you would need to update the Python path in your bashrc file. You can access this hidden file via your terminal by any text editor of your choice. Here's how you might edit the file using emacs:
emacs .bashrc
Once you have the file open, set the Python path to where you saved pyAudioAnalysis locally.
export PYTHONPATH=$PYTHONPATH:"/Users/navrajnarula/Desktop/audioClassification/pyAudioAnalysis
To update these specifications, specify the source in your terminal.
source ~/.bashrc
FFmpeg is a free software that is designed for command-line-based processing of audio files. Since our classifier works with audio files, I would suggest downloading FFmpeg here to avoid trivial errors when it comes to reading in inputs.
Now that everything has been set up on your local machine, we can go ahead and run the classifier!
You can use any audio data that you have available for this classifier. Since my focus is on emotional analysis, I utilized CREMA-D. This dataset contains over 7,000 instances of speech utterances delivered by actors that fall into labeled categories of anger, disgust, fear, happiness, and sadness. To obtain the full dataset, head on over to CREMA-D's repository and download the data using Git Large File Storage.
For testing purposes, I have included a small subset of CREMA-D's files in the praat_viz directory of my repository, which are distinguished by male and female voices. This is further broken down by emotion, as stated within each file name.
mood-class/
|_audioClassification/
|__trainingdata/
|___anger/
|___disgust/
|___fear/
|___happiness/
|___sadness/
Note that pyAudioAnalysis will work on both .mp3 and .wav files. My training files are in .wav format.
mood-class currently uses an SVM model in this example, but if you prefer to use another one, check out pyAudioAnalysis for varying models it may support. To train mood-class on your training data, simply run this command:
python createClassifierModel.py trainingData
trainingData is the directory I have stored my training files in.
Once the data is trained, you'll see a contingency table representing the output.
dis fear hap
dis 16.67 1.67 2.75
fear 5.08 11.08 5.67
hap 4.00 5.92 13.50
The above displays the matrix for disgust, fear, and happiness.
mood-class will categorize any audio file into any class of your labeled dataset.
python testClassifierModel.py happiness_test.wav
Passing in an untrained happy file to mood-class returns the probability for which class the file would fall into:
classNames is ['disgust', 'fear', 'happiness']
P is [0.01042657 0.04208108 0.94749235]
result is 2.0
File: happiness_test.wav is in category: happiness, with probability: 0.94749235
This project is authored and maintained by Navie Narula. mood-class works in beta, but is still a work in progress. New contributors, pull requests, and issues are always welcome.