Mltu

Changed
- Changed `mltu.augmentors` to work only with `Image` objects

Added
- Created `Image` object in `mltu.annotations.image` to handle image annotations

Changed
- changes `OnnxInferenceModel` in `mltu.torch.inferenceModels` to load custom metadata from saved ONNX model
- improved `mltu.dataProvider` to remove bad samples from dataset on epoch end

Added:
- added `mltu.torch.losses`, used to create PyTorch losses, that may be used in training and validation
- added CTC loss to `mltu.torch.losses` that can be used for training CTC based models
- added `Model2onnx` and `Tensorboard` callbacks to `mltu.torch.callbacks`, used to create PyTorch callbacks, that may be used in training and validation
- added `CERMetric` and `WERMetric` to `mltu.torch.metrics`, used to create PyTorch metrics, that may be used in training and validation
- created 08 pytorch tutorial, that shows how to use `mltu.torch` to train CTC based models

Changed
- In all tutorials removed stow dependency and replaced with os package, to make it easier to use on Windows 11

Added:
- added `mltu.torch`, that contains PyTorch utilities for training machine learning models
- added `mltu.torch.dataProvider`, used to create PyTorch data loaders for training and validation
- added `mltu.torch.models`, used to create PyTorch models, that wrapps whole model pipeline (training, validation, metrics, callbacks, etc.)
- added `mltu.torch.callbacks`, used to create PyTorch callbacks, that may be used in training and validation
- added `mltu.torch.metrics`, used to create PyTorch metrics, that may be used in training and validation
- added `07_pytorch_tutorial` tutorial

Changed
- detaching TensorFlow from mltu, now mltu is only a collection of utilities for training machine learning models

Added:
- added 06_pytorch_introduction tutorial
- added `mltu.tensorflow` and `mltu.torch` into built package

Changed
- added `mltu.utils` into built package

Changed
-
Added:
- added 05_sound_to_text tutorial
- added `WavReader` to `mltu/preprocessors`, used to read wav files and convert them to numpy arrays