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from abc import ABC, abstractmethod |
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from typing import Dict, Optional |
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import importlib |
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import os |
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import numpy as np |
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class AudioTransform(ABC): |
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@classmethod |
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@abstractmethod |
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def from_config_dict(cls, config: Optional[Dict] = None): |
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pass |
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class CompositeAudioTransform(AudioTransform): |
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def _from_config_dict( |
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cls, |
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transform_type, |
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get_audio_transform, |
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composite_cls, |
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config=None, |
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return_empty=False, |
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): |
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_config = {} if config is None else config |
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_transforms = _config.get(f"{transform_type}_transforms") |
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if _transforms is None: |
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if return_empty: |
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_transforms = [] |
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else: |
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return None |
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transforms = [ |
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get_audio_transform(_t).from_config_dict(_config.get(_t)) |
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for _t in _transforms |
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] |
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return composite_cls(transforms) |
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def __init__(self, transforms): |
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self.transforms = [t for t in transforms if t is not None] |
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def __call__(self, x): |
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for t in self.transforms: |
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x = t(x) |
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return x |
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def __repr__(self): |
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format_string = ( |
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[self.__class__.__name__ + "("] |
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+ [f" {t.__repr__()}" for t in self.transforms] |
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+ [")"] |
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) |
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return "\n".join(format_string) |
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def register_audio_transform(name, cls_type, registry, class_names): |
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def register_audio_transform_cls(cls): |
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if name in registry: |
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raise ValueError(f"Cannot register duplicate transform ({name})") |
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if not issubclass(cls, cls_type): |
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raise ValueError( |
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f"Transform ({name}: {cls.__name__}) must extend " |
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f"{cls_type.__name__}" |
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) |
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if cls.__name__ in class_names: |
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raise ValueError( |
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f"Cannot register audio transform with duplicate " |
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f"class name ({cls.__name__})" |
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) |
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registry[name] = cls |
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class_names.add(cls.__name__) |
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return cls |
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return register_audio_transform_cls |
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def import_transforms(transforms_dir, transform_type): |
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for file in os.listdir(transforms_dir): |
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path = os.path.join(transforms_dir, file) |
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if ( |
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not file.startswith("_") |
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and not file.startswith(".") |
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and (file.endswith(".py") or os.path.isdir(path)) |
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): |
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name = file[: file.find(".py")] if file.endswith(".py") else file |
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importlib.import_module( |
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f"fairseq.data.audio.{transform_type}_transforms." + name |
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) |
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def rand_uniform(a, b): |
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return np.random.uniform() * (b - a) + a |
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