changed joha.py

joha.py

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+"""SQUAD: The Stanford Question Answering Dataset."""
+
+import os
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+_DATA_URL = "https://huggingface.co/datasets/aymanelmar/joha/resolve/main/joha.tar.gz"
+
+_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 = """\
+Common Voice is Mozilla's initiative to help teach machines how real people speak.
+The dataset currently consists of 7,335 validated hours of speech in 60 languages, but we’re always adding more voices and languages.
+"""
+
+
+
+class johaDataset(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+    
+        
+        features = datasets.Features(
+            {
+                "file_name": datasets.Value("string"),
+                "words": datasets.Value("string"),
+                "duration": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=48_000),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            citation=_CITATION,
+            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="words")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Download the TAR archive that contains the audio files:
+        archive_path = dl_manager.download(_DATA_URL)
+
+        # First we locate the data using the path within the archive:
+        path_to_data = ""
+        path_to_clips = "/".join([path_to_data, "data"])
+        metadata_filepaths = {
+            split: "/".join([path_to_data, f"{split}.tsv"])
+            for split in ["train", "test", "validation"]
+        }
+        # (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) if not dl_manager.is_streaming else None
+
+        # 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 + "/train",
+                },
+            ),
+            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 + "/test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                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["validation"],
+                    "path_to_clips": path_to_clips+ "/validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, local_extracted_archive, archive_iterator, metadata_filepath, path_to_clips):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+
+        # audio is not a header of the csv files
+        data_fields.remove("audio")
+        path_idx = data_fields.index("path")
+
+        all_field_values = {}
+        metadata_found = False
+        # Here we iterate over all the files within the TAR archive:
+        for path, f in archive_iterator:
+            # 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]
+
+                    # 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
+                    path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
+                    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["path"] = path if local_extracted_archive else None
+
+                    yield path, result