An approach is made to classify orca calls using recurrent neural networks in comparison to conventional convolutional neural networks with CI/CD in action using cml.
- tar.gz files are downloaded from remote storage which is data versioned by DVC
- dvc.yaml executed using
dvc reprothis runs the complete pipeline of execution from extracting tar files to their preprocessing and model training. - To run the preprocessing.py alone run:
python scripts/preprocessing/preprocess.py --tsv_path <path/to/data_csv> --files_dir <path/to/wav_files>(Fow Windows)
python3 scripts/preprocessing/preprocess.py --tsv_path <path/to/data_csv> --files_dir <path/to/wav_files>(For linux)
Add--reduce_noiseto remove background noise from the extracted calls. - The model has been created with Kapre ( Keras Audio Preprocessing ) at the first layer, it computes STFT, ISTFT, Melspectrogram, and others on GPU real-time. For more info see keunwoochoi/kapre.
- To view the summary of the models run:
python scripts/models.py --summary(-sum)(For Windows)
python3 scripts/models.py --summary(-sum)(For Linux) - To train the model alone from the available options [conv1,conv2,lstm] run:
python scripts/train.py --model_type <model_name_from_list>(For Windows)python3 scripts/train.py --model_type <model_name_from_list>(For linux)
Add --plt_grph(-pg), --force(-f) for plotting the graphs for all the models and for forcefully training even when previously trained respectively.
Note:
1. For the plotting the metrics for all the models must be present in logs directory else the result would be faulty.
2. Without--forceby the default command, the models will train only if not trained previously. - Three models have been created for a better comparison of there respective metrics, the main objective here is to make the LSTM model perform the best, a 1-D Convolutional Network and a 2-D Convolutional Network have been created for the same.
- The output metrics instantiating the training procedure:
