Upload banc-trawsgrifiadau-bangor.py

banc-trawsgrifiadau-bangor.py

@@ -0,0 +1,103 @@
+"""Huggingface Dataset version of Banc Trawsgrifiadau Bangor"""
+
+
+import os
+import datasets
+
+
+# TODO: Add BibTeX citation
+_CITATION = """\
+}
+"""
+_DESCRIPTION = """Huggingface Dataset version of Banc Trawsgrifiadau Bangor"""
+_HOMEPAGE = "https://git.techiaith.bangor.ac.uk/data-porth-technolegau-iaith/banc-trawsgrifiadau-bangor"
+_LICENSE = "Creative Commons Zero v1.0 Universal"
+_URL = "https://huggingface.co/datasets/prvInSpace/banc-trawsgrifiadau-bangor/resolve/main"
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+
+
+class BancTrawsgrifiadauBangor(datasets.GeneratorBasedBuilder):
+    """Huggingface Dataset version of Banc Trawsgrifiadau Bangor"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio_filename": datasets.Value("string"),
+                "audio_filesize": datasets.Value("string"),
+                "transcript": datasets.Value("string"),
+                "duration": datasets.Value("int64"),
+                "audio": datasets.features.Audio(sampling_rate=16_000)
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            # Here we define them above because they are different between the two configurations
+            features=features,
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        # Download the clips
+        data_dir = dl_manager.download_and_extract(
+            f"{_URL}/clips.zip")
+
+        # Generate the splits
+        return [
+            datasets.SplitGenerator(
+                name="clips",
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dl_manager.download(f"{_URL}/clips.tsv"),
+                    "path_to_clips": os.path.join(data_dir, "clips")
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dl_manager.download(f"{_URL}/train.tsv"),
+                    "path_to_clips": os.path.join(data_dir, "clips")
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dl_manager.download(f"{_URL}/test.tsv"),
+                    "path_to_clips": os.path.join(data_dir, "clips")
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, path_to_clips):
+        print(path_to_clips)
+        import csv
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t")
+            for row in reader:
+                path = f'{path_to_clips}/{row["audio_filename"]}'
+
+                # Add the audio data
+                with open(path, "rb") as file:
+                    row['audio'] = {
+                        'path': path, 'bytes': file.read()
+                    }
+                yield path, row