Description

This script extracts spectrograms from .wav files. It allows you to specify various parameters such as duration, window size, hop ratio, high and low pass filters, overlap, resampling frequency, and CPU usage to optimize the process.

Usage

To run the script, use the following command:

python get_spectrogram.py <path> <directory> [options]

Arguments

Positional Arguments

• path: Path to the folder or file that contains the recordings.
• directory: Directory where the extracted spectrograms will be stored.

Optional Arguments

--duration (int): Duration for each spectrogram. Default is 8.
--window (int): Window size for the Fourier Transform. Default is 1024.
--hop (float): Ratio of hop in window. 50%% corresponds to 0.5. Default is 0.5.
--high (int): High Pass Filter value in Hz. Default is 10.
--low (int): Low Pass Filter value in Hz. Default is None.
--overlap (int): Overlap in seconds between two spectrograms. Default is 0.
--rf (int): Resampling Frequency of the signal. If not provided, the original frequency sampling of the recording will be used. Default is None.
--cpu (int): Number of CPUs to use for processing to speed up the process. Provide 2 or more. Default is 1.
--test (flag): If provided, sets the test flag to 1, otherwise it is None.

Description

This script converts annotations from the LabelMe format to a YOLO compatible format. It allows you to specify the path to the LabelMe annotations and the directory where the converted YOLO annotations will be stored.

Usage

To run the script, use the following command:

python labelme2yolo.py <path_to_data> <directory>

Arguments

Positional Arguments

• path_to_data: Path to the folder that contains the LabelMe annotations.
• directory: Directory where the YOLO annotations will be stored.

Description

This script converts annotations from the YOLO format (stored in .txt files) to JSON files. It allows you to specify the path to the folder containing the .txt files, the path to the folder containing the images, and optionally the directory where the modified JSON files will be stored.

Usage

To run the script, use the following command:

python yolo2labelme.py <path_to_txt> <path_to_img> [options]

Arguments

Positional Arguments

• path_to_txt: Path to the folder containing the .txt files.
• path_to_img: Path to the folder containing the .jpg images.

Optional Arguments

-d, --directory (str): Directory where the modified JSON files will be stored. If not provided, the directory will be the same as path_to_txt.

Description

This script splits data into training, validation, and optionally test sets based on a specified ratio. It allows you to specify the path to the folder containing the .txt files, the directory where the spectrogram and .txt files will be stored, and an optional test flag to include a test split.

Usage

To run the script, use the following command:

python get_train_val.py <path_to_data> <directory> [options]

Arguments

Positional Arguments

• path_to_data: Path to the folder that contains the .txt files (should end with labels/).
• directory: Directory where the spectrogram and .txt files will be stored (should be different from <path_to_data>).

Description

This script creates .txt and .jpg files for each annotation from a CSV file. It takes in the path to the CSV file containing annotations, the path to the folder containing the recordings, and the directory where the spectrograms and .txt files will be stored. The script includes options for setting the duration and overlap of the spectrograms, frequency resampling, window size, hop ratio, CPU usage, and an optional test flag to include a test split.

Usage

To run the script, use the following command:

python get_train_annot.py <filename_path> <path_to_data> <directory> [options]

Arguments

Positional Arguments

• filename_path: Path/name of the folder/file containing the annotations. If a file, use Raven format and add a Path column with the path to the .wav files.
• path_to_data: Path of the folder that contains the recordings.
• directory: Directory where the spectrograms and .txt files will be stored.

Optional Arguments

--duration (int): Duration for each spectrogram. Default is 8.
--overlap (int): Overlap in seconds between two spectrograms. Default is 2.
--rf (int): Frequency resampling. Default is None.
--window (int): Window size for the Fourier Transform. Default is 1024.
--hop (float): Ratio of hop in window (e.g., 50% = 0.5). Default is 0.5.
--cpu (int): Number of CPUs to speed up the process. Default is 1.
--test (flag): If provided, splits the data into train/test/validation sets with the ratio 1 - Ratio / 2 for test and the same for validation. If not provided, only train and validation splits are created.
--minimum : If True, vmin will be stft.mean(), else stft.min().

Description

This script collects detections from .txt files and returns a complete dataframe. It takes in the path to the folder containing the .txt files, the directory where the dataframe will be stored, and the path to the YOLOv5 custom_data.yaml file. Additionally, it allows specifying the sampling rate and the duration of the spectrogram.

Usage

To run the script, use the following command:

python get_time_freq_detection.py <path_to_data> <directory> <names> [options]

Arguments

Positional Arguments

• path_to_data: Path to the folder that contains the .txt files.
• directory: Directory where the dataframe will be stored.
• names: Path to the YOLOv5 custom_data.yaml file.

Optional Arguments

-s, --sr (int): Sampling rate of the spectrogram. This argument is required.
--duration (int): Duration of the spectrogram. Default is 8.