First version of the akan_audio dataset

asr_nlpghana.py

@@ -0,0 +1,162 @@
+# coding=utf-8
+# Copyright 2021 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.
+""" NLPGhana Voice Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+#_DATA_URL = "https://zenodo.org/record/4641533/files/ak.tar.gz?download=1"
+#_DATA_URL = 'https://www.dropbox.com/s/o6k13voiy8kdhhk/ak.tar.gz?dl=1' 
+_DATA_URL = "ak.tar.gz"
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+This work is comprised of audio data of Twi, a low resourced language spoken by the Akan people in Ghana.
+This has been adapted by NLPGhana.
+"""
+
+_HOMEPAGE = "https://ghananlp.org/"
+
+_LICENSE = ""
+
+_LANGUAGES = {
+    "ak": {
+        "Language": "Twi",
+        "Date": "2023-07-08",
+        "Size": "753 MB",
+        "Version": "tw_05_2023-07-08",
+    },
+}
+
+
+class NLPGhanaVoiceConfig(datasets.BuilderConfig):
+    """BuilderConfig for NLPGhana."""
+
+    def __init__(self, name, sub_version, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files in the
+            downloaded .tar
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        self.sub_version = sub_version
+        self.language = kwargs.pop("language", None)
+        self.date_of_snapshot = kwargs.pop("date", None)
+        self.size = kwargs.pop("size", None)
+        description = f"NLPGhana speech to text dataset in {self.language} version {self.sub_version} of {self.date_of_snapshot}. The dataset has a size of {self.size}"
+        super(NLPGhanaVoiceConfig, self).__init__(
+            name=name, version=datasets.Version("1.0.5", ""), description=description, **kwargs
+        )
+
+
+class NLPGhanaVoice(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        NLPGhanaVoiceConfig(
+            name=lang_id,
+            language=_LANGUAGES[lang_id]["Language"],
+            sub_version=_LANGUAGES[lang_id]["Version"],
+            date=_LANGUAGES[lang_id]["Date"],
+            size=_LANGUAGES[lang_id]["Size"],
+        )
+        for lang_id in _LANGUAGES.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "user_id": datasets.Value("string"),
+                "path": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "durationMsec": datasets.Value("int64"),
+                "sampleRate": datasets.Value("int64"),
+                "speaker_gender": datasets.Value("string"),
+                "mother_tongue": datasets.Value("string"),
+                "date": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_DATA_URL)
+        abs_path_to_data = os.path.join(dl_path, self.config.name)
+        abs_path_to_clips = os.path.join(abs_path_to_data, "clips")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(abs_path_to_data, "train.tsv"),
+                    "path_to_clips": abs_path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(abs_path_to_data, "test.tsv"),
+                    "path_to_clips": abs_path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(abs_path_to_data, "validation.tsv"),
+                    "path_to_clips": abs_path_to_clips,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, path_to_clips):
+        """ Yields examples. """
+        data_fields = list(self._info().features.keys())
+        path_idx = data_fields.index("path")
+
+        with open(filepath, encoding="utf-8") as f:
+            lines = f.readlines()
+            headline = lines[0]
+
+            column_names = headline.strip().split("\t")
+            assert (
+                column_names == data_fields
+            ), f"The file should have {data_fields} as column names, but has {column_names}"
+
+            for id_, line in enumerate(lines[1:]):
+                field_values = line.strip().split("\t")
+
+                # set absolute path for mp3 audio file
+                field_values[path_idx] = os.path.join(path_to_clips, field_values[path_idx])
+
+                # if data is incomplete, fill with empty values
+                if len(field_values) < len(data_fields):
+                    field_values += (len(data_fields) - len(field_values)) * ["''"]
+
+                yield id_, {key: value for key, value in zip(data_fields, field_values)}