Upload filipino_dataset_script.py

filipino_dataset_script.py

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+
+# 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.
+""" Magic Hub Dataset"""
+
+
+import os
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_DATA_URL = "./fil_tsv.tar.gz" #https://huggingface.co/datasets/Khalsuu/filipino_dataset_script/resolve/main/fil.zip
+
+# _CITATION = """\
+# @inproceedings{commonvoice:2020,
+#   author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
+#   title = {Common Voice: A Massively-Multilingual Speech Corpus},
+#   booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
+#   pages = {4211--4215},
+#   year = 2020   
+# }
+# """
+
+_DESCRIPTION = """\
+Magic Hub's initiative to help teach machines how real people speak. They wanted to provide structured data that
+will help enthusiasts and researchers to spend more time on training models rather than cleaning and structuring data.
+"""
+
+_HOMEPAGE = "https://magichub.com/datasets/filipino-scripted-speech-corpus-daily-use-sentence/"
+
+_LICENSE = "https://magichub.com/magic-data-open-source-license/"
+
+_LANGUAGES = {
+    "fil": {
+        "Language": "Filipino",
+        "Date": "2022-4-20",
+        "Size": "414 MB",
+        "Version": "ab_1h_2020-12-11",
+        "Validated_Hr_Total": 4.58,
+        "Overall_Hr_Total": 4.58,
+        "Number_Of_Voice": 10,
+    },
+}
+
+
+class FilipinoVoiceConfig(datasets.BuilderConfig):
+    """BuilderConfig for Filipino Speech."""
+
+    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)
+        self.validated_hr_total = kwargs.pop("val_hrs", None)
+        self.total_hr_total = kwargs.pop("total_hrs", None)
+        self.num_of_voice = kwargs.pop("num_of_voice", None)
+        description = f"Magic Hub speech to text dataset in {self.language} version {self.sub_version} of {self.date_of_snapshot}. The dataset comprises {self.validated_hr_total} of validated transcribed speech data from {self.num_of_voice} speakers. The dataset has a size of {self.size}"
+        super(FilipinoVoiceConfig, self).__init__(
+            name=name, version=datasets.Version("1.0.0", ""), description=description, **kwargs
+        )
+
+
+class FilipinoVoice(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+    BUILDER_CONFIGS = [
+        FilipinoVoiceConfig(
+            name=lang_id,
+            language=_LANGUAGES[lang_id]["Language"],
+            sub_version=_LANGUAGES[lang_id]["Version"],
+            date=_LANGUAGES[lang_id]["Date"],
+            size=_LANGUAGES[lang_id]["Size"],
+            val_hrs=_LANGUAGES[lang_id]["Validated_Hr_Total"],
+            total_hrs=_LANGUAGES[lang_id]["Overall_Hr_Total"],
+            num_of_voice=_LANGUAGES[lang_id]["Number_Of_Voice"],
+        )
+        for lang_id in _LANGUAGES.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "CHANNEL": datasets.Value("string"),
+                "UTTRANS_ID": datasets.Value("string"),
+                "SPEAKER_ID": datasets.Value("string"),
+                "PROMPT": datasets.Value("string"),
+                "TRANSCRIPTION": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            # citation=_CITATION,
+            task_templates=[AutomaticSpeechRecognition(task="automatic-speech-recognition", transcription_column="sentence")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Download the TAR archive that contains the audio files:
+        archive_path = dl_manager.download(_DATA_URL.format(self.config.name))
+
+        # First we locate the data using the path within the archive:
+        # path_to_data = "/".join(["cv-corpus-6.1-2020-12-11", self.config.name])
+        path_to_data = "fil"
+        path_to_clips = "/".join([path_to_data, "clips"])
+        metadata_filepaths = {
+            split: "/".join([path_to_data, f"{split}.txt"])
+            for split in ["train", "test"]
+        }
+
+        # print("Archive path:" + archive_path)
+        # print("Path to clips:" + path_to_clips)
+        # print(metadata_filepaths)
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path)
+
+        # print("Local extracted archive:")
+        # print(local_extracted_archive)
+
+        # To access the audio data from the TAR archives using the download manager,
+        # we have to use the dl_manager.iter_archive method.
+        #
+        # This is because dl_manager.download_and_extract
+        # doesn't work to stream TAR archives in streaming mode.
+        # (we have to stream the files of a TAR archive one by one)
+        #
+        # The iter_archive method returns an iterable of (path_within_archive, file_obj) for every
+        # file in the TAR archive.
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "local_extracted_archive": local_extracted_archive,
+                    "archive_iterator": dl_manager.iter_archive(
+                        archive_path
+                    ),  # use iter_archive here to access the files in the TAR archives
+                    "metadata_filepath": metadata_filepaths["train"],
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_extracted_archive": local_extracted_archive,
+                    "archive_iterator": dl_manager.iter_archive(
+                        archive_path
+                    ),  # use iter_archive here to access the files in the TAR archives
+                    "metadata_filepath": metadata_filepaths["test"],
+                    "path_to_clips": path_to_clips,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, local_extracted_archive, archive_iterator, metadata_filepath, path_to_clips):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+
+        # print(data_fields)
+        # audio is not a header of the csv files
+        data_fields.remove("audio")
+        path_idx = data_fields.index("UTTRANS_ID")
+        # print(path_idx)
+
+        # print("Requested metadata: " + metadata_filepath)
+        # print(archive_iterator)
+        all_field_values = {}
+        metadata_found = False
+        # Here we iterate over all the files within the TAR archive:
+        for path, f in archive_iterator:
+            print(path, ", ", f)
+            # Parse the metadata CSV file
+            if path == metadata_filepath:
+                metadata_found = True
+                lines = f.readlines()
+                headline = lines[0].decode("utf-8")
+                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 line in lines[1:]:
+                    field_values = line.decode("utf-8").strip().split("\t")
+                    # set full path for mp3 audio file
+                    audio_path = "/".join([path_to_clips, field_values[path_idx]])
+                    all_field_values[audio_path] = field_values
+            # Else, read the audio file and yield an example
+            elif path.startswith(path_to_clips):
+                assert metadata_found, "Found audio clips before the metadata TSV file."
+                if not all_field_values:
+                    break
+                if path in all_field_values:
+                    # retrieve the metadata corresponding to this audio file
+                    field_values = all_field_values[path]
+
+                    # print("Found field values")
+                    # print(field_values)
+                    # if data is incomplete, fill with empty values
+                    if len(field_values) < len(data_fields):
+                        field_values += (len(data_fields) - len(field_values)) * ["''"]
+                    result = {key: value for key, value in zip(data_fields, field_values)}
+
+                    # set audio feature
+                    result["audio"] = {"path": path, "bytes": f.read()}
+
+                    # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
+                    result["UTTRANS_ID"] = os.path.join(local_extracted_archive, path) if local_extracted_archive else None
+
+                    yield path, result