adding data files

.gitattributes

@@ -14,3 +14,5 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+*.tsv* filter=lfs diff=lfs merge=lfs -text
+*tsv* filter=lfs diff=lfs merge=lfs -text

large-sinhala-asr-dataset.py

@@ -0,0 +1,159 @@
+import os
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_DATA_URL = ".tar.gz"
+
+_CITATION = """\
+ @inproceedings{kjartansson-etal-sltu2018,
+    title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese,  Sinhala, Nepali, and Bangladeshi Bengali}},
+    author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
+    booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
+    year  = {2018},
+    address = {Gurugram, India},
+    month = aug,
+    pages = {52--55},
+    URL   = {http://dx.doi.org/10.21437/SLTU.2018-11}
+  }
+"""
+
+_DESCRIPTION = """\
+This data set contains transcribed audio data for Sinhala. The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.
+The data set has been manually quality checked, but there might still be errors.
+
+See LICENSE.txt file for license information.
+
+Copyright 2016, 2017, 2018 Google, Inc.
+"""
+
+_HOMEPAGE = "https://www.openslr.org/52/"
+
+_LICENSE = "https://www.openslr.org/resources/52/LICENSE"
+
+_LANGUAGES = {
+    "si": {
+        "Language": "Sinhala",
+        "Date": "2020-12-11",
+        "Size": "39 MB",
+        "Version": "si_1h_2020-12-11",
+        "Validated_Hr_Total": 0.05,
+        "Overall_Hr_Total": 1,
+        "Number_Of_Voice": 14,
+    },
+}
+
+
+class LargeASRConfig(datasets.BuilderConfig):
+    """BuilderConfig for LargeASR."""
+
+    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"Large Sinhala 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(LargeASRConfig, self).__init__(
+            name=name, version=datasets.Version("1.0.0", ""), description=description, **kwargs
+        )
+
+
+class LargeASR(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        LargeASRConfig(
+            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(
+            {
+                "filename": datasets.Value("string"),
+                "x": datasets.Value("string"),
+                "sentence": datasets.Value("string"),
+                "full": datasets.Value("string"),
+                "file": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[
+                AutomaticSpeechRecognition(audio_file_path_column="file", transcription_column="sentence")
+            ],
+        )
+
+    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, "cv-corpus-6.1-2020-12-11", 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,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, path_to_clips):
+        """Yields examples."""
+        data_fields = list(self._info().features.keys())
+        path_idx = data_fields.index("file")
+
+        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 wav 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)}

test.tsv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f128900942286c77805da55efd4c6bdda0948146ead46b0c40f506ea4ac8c89
+size 3256626

train.tsv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f128900942286c77805da55efd4c6bdda0948146ead46b0c40f506ea4ac8c89
+size 3256626