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data/audio_test.tar.gz

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data/audio_train.tar.gz

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data/audio_validation.tar.gz

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data/metadata_test.csv.gz

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data/metadata_train.csv.gz

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data/metadata_validation.csv.gz

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dv-presidential-speech.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import csv
+import os
+import datasets
+
+
+_CITATION = """\
+@misc{Sofwath_2023, 
+    title = "Dhivehi Presidential Speech Dataset", 
+    url = "https://huggingface.co/datasets/dash8x/presidential_speech", 
+    journal = "Hugging Face",
+    author = "Sofwath", 
+    year = "2018", 
+    month = jul
+} 
+"""
+
+_DESCRIPTION = """\
+Dhivehi Presidential Speech is a Dhivehi speech dataset created from data extracted and 
+processed by [Sofwath](https://github.com/Sofwath) as part of a collection of Dhivehi 
+datasets found [here](https://github.com/Sofwath/DhivehiDatasets).
+
+The dataset contains around 2.5 hrs (1 GB) of speech collected from Maldives President's Office
+consisting of 7 speeches given by President Yaameen Abdhul Gayyoom. 
+"""
+
+_HOMEPAGE = 'https://github.com/Sofwath/DhivehiDatasets'
+
+_LICENSE = 'CC BY-NC-SA 4.0'
+
+# Source data: 'https://drive.google.com/file/d/1vhMXoB2L23i4HfAGX7EYa4L-sfE4ThU5/view?usp=sharing'
+_DATA_URL = 'data'
+
+_PROMPTS_URLS = {
+    'train': 'data/metadata_train.tsv.gz',
+    'test': 'data/metadata_test.tsv.gz',
+    'validation': 'data/metadata_validation.tsv.gz',
+}
+
+
+class DhivehiPresidentialSpeech(datasets.GeneratorBasedBuilder):
+    """Dhivehi Presidential Speech is a free Dhivehi speech corpus consisting of around 2.5 hours of
+    recorded speech prepared for Dhivehi Automatic Speech Recognition task."""
+
+    VERSION = datasets.Version('1.0.0')
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    'path': datasets.Value('string'),
+                    'audio': datasets.Audio(sampling_rate=16_000),
+                    'sentence': datasets.Value('string'),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        dl_manager.download_config.ignore_url_params = True
+        audio_path = {}
+        local_extracted_archive = {}
+        metadata_path = {}
+
+        split_type = {
+            'train': datasets.Split.TRAIN, 
+            'test': datasets.Split.TEST,
+            'validation': datasets.Split.VALIDATION,
+        }
+
+        for split in split_type:
+            audio_path[split] = dl_manager.download(f'{_DATA_URL}/audio_{split}.tar.gz')
+            local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
+            metadata_path[split] = dl_manager.download_and_extract(f'{_DATA_URL}/metadata_{split}.csv.gz')
+
+        path_to_clips = 'dv-presidential-speech'
+
+        return [
+            datasets.SplitGenerator(
+                name=split_type[split],
+                gen_kwargs={
+                    'local_extracted_archive': local_extracted_archive[split],
+                    'audio_files': dl_manager.iter_archive(audio_path[split]),
+                    'metadata_path': dl_manager.download_and_extract(metadata_path[split]),
+                    'path_to_clips': f'{path_to_clips}-{split}/waves',
+                },
+            ) for split in split_type
+        ]
+
+    def _generate_examples(
+        self,
+        local_extracted_archive,
+        audio_files,
+        metadata_path,
+        path_to_clips,
+    ):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(metadata_path, 'r', encoding='utf-8') as f:
+            reader = csv.reader(f)
+            row_dict = {}
+
+            for row in reader:
+                row_dict['path'] = row[0]
+                row_dict['sentence'] = row[1]
+
+                # if data is incomplete, fill with empty values
+                for field in data_fields:
+                    if field not in row_dict:
+                        row_dict[field] = ''
+
+                metadata[row_dict['path']] = row_dict
+
+        id_ = 0
+        for path, f in audio_files:
+            file_name = os.path.splitext(os.path.basename(path))[0]
+            os.path.join(path_to_clips, row[0])
+
+            if file_name in metadata:
+                result = dict(metadata[file_name])
+                # set the audio feature and the path to the extracted file
+                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
+                result['audio'] = {'path': path, 'bytes': f.read()}
+                result['path'] = path
+                yield id_, result
+                id_ += 1