AccentRecognition

This Repository contains the code for detecting the accent of a speaker with their speech signal. The repository experiments with AESRC2020 Dataset.

Installation

Use the package manager pip to install the required packages for preparing the dataset, training and testing the model.

pip install -r requirements.txt

Usage

Noise Dataset

# Noise Dataset is optional, but recommended
# 1. Download noise Dataset with wavencoder
# 2. Update config.py with the noise_dataset_path = 'folder-to-download-noise-data'

import wavencoder
wavencoder.utils.download_noise_dataset('folder-to-download-noise-data', sample_rate='16k', download_all=True)

Config

Edit the config.py for configurations on data path, lr, batch size, ... etc.
Or you can optionally give as a command line parameters for train and test

Download the dataset

# AESRC2020 Dataset

Prepare the dataset for training and testing(Not Required)

# prepare csv with path to wav files and labels for training
# Train and Test csv files are available at Dataset folder

python prepare_aesrc_data.py --path='path to aesrc wav data folder'

Logger

#Change the logger to Tensorboard or Wandb based on your need in train.py
logger = WandbLogger()
logger = TensorBoardLogger()

Training(Dev Model, to make sure everything is set as expected for training)

python train.py --dev=True 

Training(also check for other arguments in the train.py file)

python train.py 

Test the Model(also check for other arguments in the test.py file)

python test.py  
# Edit config.py for trained model checkpoint
# or add --model_checkpoint='path to saved model checkpoint'

Results

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

License

MIT