![]() ![]() Samplewise_normalize_range – Normalize the input by the values in this rangeįor instance, if samplewise_normalize_range=(0,1), then each input will be scaled to values between 0 and 1 Samplewise_std_normalization – Divide processed sample data by its STD Samplewise_center – Center each sample’s processed data about its norm ndarray, class_id : Optional, filename : Optional, batch_index : Optional, batch_class_ids : Optional ], batch_filenames : Optional ] ) -> np. This function should have the following signature:ĭef my_processing_func ( params : ParallelProcessParams, x : np. If this is omitted, then iterator.get_batches_of_transformed_samples() is used Max_batches_pending – This is the number of processed batches to queue.Ī larger number can improving training times at the expense ofįunction that should return the transformed batch. Raw data generator generator#This is useful for debugging as it allows for single-stepping in the generator threads The float is the percentageĪ large number of CPU cores will consume more system memory.ĭebug – If true then use the Python threading library instead of multiprocessing Of CPU cores, or it can be a float < 1.0. This number can be either an integer, which specifies the exact number ![]() Use the silence_class_percentage setting to control the size of this class.Ĭores – The number of CPU cores to use for spawned audio processing batch processes. If _silence_ is added as a class to flow_from_directory(), then the generator will automaticallyĪdd ‘silence’ samples is all zeros with the background noise augmentations added. Use the unknown_class_percentage setting to control the size of this class. ![]() ![]() The other augmentation parameters will be applied to the ‘unknown’ samples. Unused classes in the dataset directory will be randomly selected and used as an ‘unknown’ class. If _unknown_ is added as a class to flow_from_directory(), then the generator will automaticallyĪdd an ‘unknown’ class to the generated batches. If frontend_enabled=True, normalize based on samplewise_center, samplewise_std_normalization, and rescale If supplied, call postprocessing_function() If frontend_enabled=True, pass augmented audio through .audio_feature_generator.AudioFeatureGenerator and return spectrogram as 2D array If supplied, call preprocessing_function() Augment padded audio based on randomly generated transform parameters from part c) Pad zeros before and after trimmed audio based on sample_length_seconds and offset_rangeį. Trim silence from raw audio sample based on trim_threshold_dbĮ. Generate random transform parameters based on parameters from step 1)ĭ. If supplied, call noaug_preprocessing_function()Ĭ. If get_batch_function() is given, then call this function an skip the rest of these stepsī. During fitting, batches of samples are concurrently processed using the following sequence:Ī0.The return value of flow_from_directory() is a ‘generator’ which should be given to a model fit() method This allows for efficient use of multi-core systemsĪs future batch samples can be concurrently processed while processed batches may beĬlass instantiated with parameters (see below)įlow_from_directory() called which lists each classes’ samples in the specified directory This class as a similar functionality to the Keras ImageDataGeneratorĮxcept, instead of processing image files it processes audio files.Īdditionally, batch samples are asynchronously processed using the Python ParallelAudioDataGenerator ( cores = 0.25, debug = False, max_batches_pending = 4, get_batch_function = None, noaug_preprocessing_function = None, preprocessing_function = None, postprocessing_function = None, samplewise_center = False, samplewise_std_normalization = False, samplewise_normalize_range = None, rescale = None, validation_split = 0.0, validation_augmentation_enabled = True, dtype = 'float32', frontend_dtype = None, trim_threshold_db = 20, noise_colors = None, noise_color_range = None, speed_range = None, pitch_range = None, vtlp_range = None, loudness_range = None, bg_noise_range = None, bg_noise_dir = None, offset_range = (0.0, 1.0), unknown_class_percentage = 1.0, silence_class_percentage = 0.6, disable_random_transforms = False, frontend_settings = None, frontend_enabled = True, sample_shape = None, disable_gpu_in_subprocesses = True, add_channel_dimension = True ) ¶ ParallelAudioDataGenerator ¶ class .parallel_generator. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |