emonet

A package to model negative emotion severity in patients with adverse childhood events (ACE).

Contributors: @chris-santiago, @gonzalezeric, @costargc

Installation & Environment Setup

Using Docker

1. Open Terminal on Mac or PowerShell on Windows from the root directory.
2. When the application is open, in the command line, build the image using the following: docker build -t cjsantiago/emonet-model -f docker/model/Dockerfile .
3. Run container docker run -it --name emonet -p 8888:8888 -v "${HOME}"/emonet-data:/home/jovyan/emonet-data cjsantiago/emonet-model
   • Important: Training the model or running batch inference presumes that you have a emonet-data directory within your home folder, containing the original voice_labeling_report directory. This will allow you to replicate all batch preprocessing done prior to model training.
   • You can score file(s) or signal(s), either on their own or with your own custom DataLoader, without the data directory (described above).
   • See docker/model/README.md for more.
4. Once the container has been created, you may access the files using one of the URLs generated in the CLI.

Using Conda

1. Clone this repo, then cd emonet
2. Create a virtual environment
   • For training and notebooks, use conda env create -f env-base-plus.yml
   • For scoring, only, use conda env create -f env-base-yml
3. Install emonet package, pip install -e .

NOTE: We're installing in editable mode (-e flag) as we expect to run training and/or scoring from this cloned repo. Editable mode will symlink source code from the cloned repo directory to the appropriate Python interpreter, enabling source code edits and easy-access to our saved models under the saved_models directory.

Installing ffmpeg

ffmpeg is required to convert .m4a to .wav. On Mac this can be installed via Homebrew. Skip this if you're running via Docker.

Data Setup

To use our original datset splits, we recommend downloading directly from our S3 bucket. This also removes the need to complete some time-consuming preprocessing steps.

Download from S3

Assumes that you have the AWS CLI tool installed on your machine (and that you have our credentials :grinning:).

Within your home folder, create a directory called emonet-data. You could also use our directory setup script python emonet/dir_setup.py.

From the emonet-data directory, run this command to sync (copy) the required directories and files directly from our S3 bucket.

Note that this assumes our credentials are located within ~/.aws/credentials

bash aws s3 sync s3://gatech-emonet/eval/ .

From Scratch

Once you've setup the environment and installed the emonet package:

Run python emonet/data_setup.py

Note: you can pass an optional number of max_workers to this command; the default is 8 (threads).

python emonet/data_setup.py 16

This script will run and perform the following:

1. dir_setup.py: Set up a data directory within the home folder
2. m4atowav.py: Convert any .m4a files to .wav
3. batch_resample.py: Resample files to 16,000Hz
4. batch_vad.py: Run voice activity detection (VAD)
5. data_prep.py: Create train/valid/test splits and respective manifests
6. wav_splitter.py: Split .wav files into 8-second chunks, the create new train/valid/test manifests that use the chunked .wav files

Training

Now that files have all been converted to WAV, preprocessed with VAD and split training, validation and testing sets, and chunked into 8-second segments:

Command Line Tool

The easiest way to run the model is via the CLI:

Run

bash python emonet/train.py <num_workers> <max_epochs> <emotion>

and pass in the desired number of workers, epochs and emotion.

Example:

bash python train.py 12 300 anger

Python

You can also train the model in Python:

Open a .py file or notebook and run

```python from emonet import DATA_DIR from emonet.train import main

main(workers=12, epochs=300, emotion="sadness", usewandb=False, datadir=DATA_DIR) ```

and pass in the desired number of workers, epochs and emotion; you can log runs to Weights & Biases by setting use_wandb=True, and change the default data directory using the data_dir parameter.

Pretrained Models

No pretrained models are included in this public-facing repo.

Scoring

Command Line Tool

The easiest way to score (using our pretrained models) is via our CLI tool, emonet. The syntax for this tool is:

bash emonet <emotion> <file to score>

Example:

bash emonet anger /Users/christophersantiago/emonet-data/wavs/6529_53113_1602547200.wav

Note: This CLI tool will run VAD on the .wav file, and can accept arbitrary length-- despite model being trained on 8-second chunks. Therefore, you should use an original .wav of the sample you wish to score, not a .wav that's been preprocessed with VAD.

Python

You can also score via Python:

```python from emonet import DATA_DIR, ROOT from emonet.model import EmotionRegressor

def getsaved(emotion: str): return ROOT.joinpath('savedmodels', f'{emotion}.ckpt')

emotion = 'fear' model = EmotionRegressor.loadfromcheckpoint(get_saved(emotion))

file = 'path-to-my-file'

model.scorefile(file=file, samplerate=16000, vad=True) ```

See inference.py for an example of how we scored our testing sets.