Utilities¶

Metric functions¶

`predictions_temporal_integration`(Y_predicted)	Integrate temporal dimension.
`evaluate_metrics`(model, data, metrics, **kwargs)	Calculate metrics over files with different length
`sed`(Y_val, Y_predicted[, sequence_time_sec, …])	Calculate metrics for Sound Event Detection
`classification`(Y_val, Y_predicted[, label_list])	Calculate metrics for Audio Classification
`tagging`(Y_val, Y_predicted[, label_list])	Calculate metrics for Audio Tagging
`accuracy`
`ER`
`F1`

`get_fold_val`(fold_test, fold_list)	Get the validation fold given the test fold.
`evaluation_setup`(fold_test, folds, …[, …])	Return a evaluation setup given by the evaluation_mode.

`contiguous_regions`(act)
`evaluation_setup`(fold_test, folds, …[, …])	Return a evaluation setup given by the evaluation_mode.
`event_roll_to_event_list`(event_roll, …)	Convert a event roll matrix to a event list.
`tag_probabilities_to_tag_list`(…[, threshold])	Convert a tag probabilites matrix to a tag list.

`save_json`(path, json_string)	Save a json file in the location given by path.
`load_json`(path)	Load a json file from path.
`save_pickle`(X, path)	Save a pickle object in the location given by path.
`load_pickle`(path)	Load a pickle object from path.
`list_all_files`(path)	List all files in the path including subfolders.
`list_wav_files`(path)	List all wav files in the path including subfolders.
`load_training_log`(weights_folder)	Load the training log files of keras.
`mkdir_if_not_exists`(path[, parents])	Make dir if does not exists.
`download_files_and_unzip`(dataset_folder, …)	Download files from zenodo and decompress them.
`move_all_files_to`(source, destination)	Move all files from source to destination
`move_all_files_to_parent`(parent, child)	Move all files in parent/child to the parent/
`duplicate_folder_structure`(origin_path, …)	Duplicate the folder structure from the origin to the destination.
`example_audio_file`([index])	Get path to an example audio file

`ClassificationCallback`(data[, file_weights, …])	Keras callback to calculate acc after each epoch and save file with the weights if the evaluation improves
`SEDCallback`(data[, file_weights, best_F1, …])	Keras callback to calculate F1 and ER after each epoch and save file with the weights if the evaluation improves.
`TaggingCallback`(data[, file_weights, …])	Keras callback to calculate acc after each epoch and save file with the weights if the evaluation improves
`F1ERCallback`

encode_audio(data, sr) Encode an audio signal for web applications.

progressbar(it[, prefix, size, file]) Iterable progress bar.

get_class_by_name(classes_dict, class_name, …) Get a class given its name.