feat: csript

speech-emotion-recognition-dataset.py

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+import datasets
+import PIL.Image
+import PIL.ImageOps
+import numpy as np
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {generated-usa-passeports-dataset},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+Data generation in machine learning involves creating or manipulating data
+to train and evaluate machine learning models. The purpose of data generation
+is to provide diverse and representative examples that cover a wide range of
+scenarios, ensuring the model's robustness and generalization.
+Data augmentation techniques involve applying various transformations to
+existing data samples to create new ones. These transformations include:
+random rotations, translations, scaling, flips, and more. Augmentation helps
+in increasing the dataset size, introducing natural variations, and improving
+model performance by making it more invariant to specific transformations.
+The dataset contains **GENERATED** USA passports, which are replicas of
+official passports but with randomly generated details, such as name, date of
+birth etc. The primary intention of generating these fake passports is to
+demonstrate the structure and content of a typical passport document and to
+train the neural network to identify this type of document.
+Generated passports can assist in conducting research without accessing or
+compromising real user data that is often sensitive and subject to privacy
+regulations. Synthetic data generation allows researchers to develop and
+refine models using simulated passport data without risking privacy leaks.
+"""
+_NAME = 'generated-usa-passeports-dataset'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = "cc-by-nc-nd-4.0"
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+def exif_transpose(img):
+    if not img:
+        return img
+
+    exif_orientation_tag = 274
+
+    # Check for EXIF data (only present on some files)
+    if hasattr(img, "_getexif") and isinstance(
+            img._getexif(), dict) and exif_orientation_tag in img._getexif():
+        exif_data = img._getexif()
+        orientation = exif_data[exif_orientation_tag]
+
+        # Handle EXIF Orientation
+        if orientation == 1:
+            # Normal image - nothing to do!
+            pass
+        elif orientation == 2:
+            # Mirrored left to right
+            img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)
+        elif orientation == 3:
+            # Rotated 180 degrees
+            img = img.rotate(180)
+        elif orientation == 4:
+            # Mirrored top to bottom
+            img = img.rotate(180).transpose(PIL.Image.FLIP_LEFT_RIGHT)
+        elif orientation == 5:
+            # Mirrored along top-left diagonal
+            img = img.rotate(-90,
+                             expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
+        elif orientation == 6:
+            # Rotated 90 degrees
+            img = img.rotate(-90, expand=True)
+        elif orientation == 7:
+            # Mirrored along top-right diagonal
+            img = img.rotate(90,
+                             expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
+        elif orientation == 8:
+            # Rotated 270 degrees
+            img = img.rotate(90, expand=True)
+
+    return img
+
+
+def load_image_file(file, mode='RGB'):
+    # Load the image with PIL
+    img = PIL.Image.open(file)
+
+    if hasattr(PIL.ImageOps, 'exif_transpose'):
+        # Very recent versions of PIL can do exit transpose internally
+        img = PIL.ImageOps.exif_transpose(img)
+    else:
+        # Otherwise, do the exif transpose ourselves
+        img = exif_transpose(img)
+
+    img = img.convert(mode)
+
+    return np.array(img)
+
+
+class GeneratedUsaPasseportsDataset(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'original': datasets.Image(),
+                'us_pass_augmentated_1': datasets.Image(),
+                'us_pass_augmentated_2': datasets.Image(),
+                'us_pass_augmentated_3': datasets.Image()
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE)
+
+    def _split_generators(self, dl_manager):
+        original = dl_manager.download_and_extract(f"{_DATA}original.zip")
+        augmentation = dl_manager.download_and_extract(
+            f"{_DATA}augmentation.zip")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        original = dl_manager.iter_files(original)
+        augmentation = dl_manager.iter_files(augmentation)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "original": original,
+                                        'augmentation': augmentation,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, original, augmentation, annotations):
+        original = list(original)
+        augmentation = list(augmentation)
+        augmentation = [
+            augmentation[i:i + 3] for i in range(0, len(augmentation), 3)
+        ]
+
+        for idx, (org, aug) in enumerate(zip(original, augmentation)):
+            yield idx, {
+                'original': load_image_file(org),
+                'us_pass_augmentated_1': load_image_file(aug[0]),
+                'us_pass_augmentated_2': load_image_file(aug[1]),
+                'us_pass_augmentated_3': load_image_file(aug[2])
+            }