:j(ddlmZddlmZmZmZmZmZddlm Z ddl Z ddl m Z ddl m Z ee e fZ ddeded efd Zd eeeeeeefffd efd Zd eeefd efdZy))Path)AnyDictTextOptionalUnion) import_moduleN)Compose)BaseWaveformTransform class_namedefault_module_namereturnc|jd}t|dk(r|d|d}t||}ndj|dd}|d}t t ||S)asLoad class by its name Parameters ---------- class_name : `str` default_module_name : `str`, optional When provided and `class_name` does not contain the absolute path. Defaults to "torch_audiomentations". Returns ------- Klass : `type` Class. Example ------- >>> YourAugmentation = get_class_by_name('your_package.your_module.YourAugmentation') >>> YourAugmentation = get_class_by_name('YourAugmentation', default_module_name='your_package.your_module') >>> from torch_audiomentations import Gain >>> assert Gain == get_class_by_name('Gain') .Nz-Could not infer module name from class name "z%".Please provide default module name.)splitlen ValueErrorjoingetattrr )r r tokensmsg module_names w/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torch_audiomentations/utils/config.pyget_class_by_namers}2  c "F 6{a  &? |L67 S/ !) hhvcr{+ BZ =-z ::configcX |d} t|}|j dt }t |t s td|dvr|dDcgc] }t|c}|d<|d i|S#t$r tdwxYw#t$rtd|dwxYwcc}w) aInstantiate a transform from a configuration dictionary. `from_dict` can be used to instantiate a transform from its class name. For instance, these two pieces of code are equivalent: >>> from torch_audiomentations import Gain >>> transform = Gain(min_gain_in_db=-12.0) >>> transform = from_dict({'transform': 'Gain', ... 'params': {'min_gain_in_db': -12.0}}) Transforms composition is also supported: >>> compose = from_dict( ... {'transform': 'Compose', ... 'params': {'transforms': [{'transform': 'Gain', ... 'params': {'min_gain_in_db': -12.0, ... 'mode': 'per_channel'}}, ... {'transform': 'PolarityInversion'}], ... 'shuffle': True}}) :param config: configuration - a configuration dictionary :returns: A transform. :rtype Transform: transformzRA (currently missing) 'transform' key should be used to define the transform type.z)torch_audiomentations does not implement z transform.paramszPTransform parameters must be provided as {'param_name': param_value} dictionary.)r OneOfSomeOf transforms)KeyErrorrrAttributeErrorgetdict isinstance from_dict)rTransformClassNameTransformClasstransform_paramssub_transform_configs rr+r+9s6 #)+#6  *+=> $ZZ$&9 & - ^  ;;)9(F* $ * +* &  -, --1   `     78J7K; W   * sA3 B B'3B B$file_ymlc ddl}t|d5}|j||j}dddt |S#t$r}tdd}~wwxYw#1swYt SxYw)a`Instantiate a transform from a YAML configuration file. `from_yaml` can be used to instantiate a transform from a YAML file. For instance, these two pieces of code are equivalent: >>> from torch_audiomentations import Gain >>> transform = Gain(min_gain_in_db=-12.0, mode="per_channel") >>> transform = from_yaml("config.yml") where the content of `config.yml` is something like: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # config.yml transform: Gain params: min_gain_in_db: -12.0 mode: per_channel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Transforms composition is also supported: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # config.yml transform: Compose params: shuffle: True transforms: - transform: Gain params: min_gain_in_db: -12.0 mode: per_channel - transform: PolarityInversion ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :param file_yml: configuration file - a path to a YAML file with the following structure: :returns: A transform. :rtype Transform: rNzAPyYAML package is needed by `from_yaml`: please install it first.r)Loader)yaml ImportErroropenload SafeLoaderr+)r0r4efrs r from_yamlr;qs{N  h 61T__56 V    O   6 V s"AA A AAA2)torch_audiomentations)pathlibrtypingrrrrr importlibr r<r /torch_audiomentations.core.transforms_interfacer Transformstrtyperr+r;r%rrrDs33#)Q '0 1 1H';';*-'; ';T5.d4tT$)_'5.95.p1dDj)1i1r