vistec-ser 0.4.6a3

Speech Emotion Recognition models and training using PyTorch

Vistec-AIS Speech Emotion Recognition

Speech Emotion Recognition Model and Inferencing using Pytorch

Installation

From Pypi

pip install vistec-ser

From source

git clone https://github.com/tann9949/vistec-ser.git
cd vistec-ser
python setup.py install

Usage

Training with THAI SER Dataset

We provide Google Colaboratory example for training the THAI SER dataset using our repository.

Training using provided scripts

Note that currently, this workflow only supports pre-loaded features. So it might comsume an additional overhead of ~2 Gb or RAM. To run the experiment. Run the following command

Since there are 80 studios recording and 20 zoom recording. We split the dataset into 10-fold, 10 studios each. Then evaluate using k-fold cross validation method. We provide 2 k-fold experiments: including and excluding zoom recording. This can be configured in config file (see examples/aisser.yaml)

python examples/train_fold_aisser.py --config-path <path-to-config> --n-iter <number-of-iterations>  

Inferencing

We also implement a FastAPI backend server as an example of deploying a SER model. To run the server, run

cd examples
uvicorn server:app --reload

You can customize the server by modifying example/thaiser.yaml in inference field.

Once the server spawn, you can do HTTP POST request in form-data format. and JSON will return as the following format:

[
  {
    "name": <request-file-name>,
    "prob": {
      "neutral": <p(neu)>,
      "anger": <p(ang)>,
      "happiness": <p(hap)>,
      "sadness": <p(sad)>
    }
  }, ...
]

See an example below:

Author & Sponsor

Chompakorn Chaksangchaichot

Email: [e-mail]