updatinf data files

.ipynb_checkpoints/large-sinhala-asr-dataset-checkpoint.py

@@ -0,0 +1,146 @@
+import os
+import string
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_DATA_URL = "https://www.openslr.org/resources/52/asr_sinhala_{}.zip"
+
+_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 ~185K 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": "2018",
+    },
+}
+
+
+class LargeASRConfig(datasets.BuilderConfig):
+    """BuilderConfig for LargeASR."""
+
+    def __init__(self, name, **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.language = kwargs.pop("language", None)
+        self.date_of_snapshot = kwargs.pop("date", None)
+        description = f"Large Sinhala dataset in {self.language} of {self.date_of_snapshot}."
+        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"],
+            date=_LANGUAGES[lang_id]["Date"],
+        )
+        for lang_id in _LANGUAGES.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "filename": datasets.Value("string"),
+                "x": datasets.Value("string"),
+                "sentence": 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."""
+        data_urls = [_DATA_URL.format(i) for i in (string.digits + string.ascii_lowercase[:6])]
+        dl_path = dl_manager.download_and_extract(data_urls)
+        print(dl_path)
+        abs_path_to_data = os.path.join('./')
+        abs_path_to_clips = os.path.join(dl_path)
+
+        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)}

large-sinhala-asr-dataset.py

@@ -1,10 +1,11 @@
 import os
+import string
 
 import datasets
 from datasets.tasks import AutomaticSpeechRecognition
 
 
-_DATA_URL = ".tar.gz"
+_DATA_URL = "https://www.openslr.org/resources/52/asr_sinhala_{}.zip"
 
 _CITATION = """\
  @inproceedings{kjartansson-etal-sltu2018,
@@ -20,7 +21,7 @@ _CITATION = """\
 """
 
 _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.
+This data set contains ~185K 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.
@@ -35,12 +36,7 @@ _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,
+        "Date": "2018",
     },
 }
 
@@ -48,7 +44,7 @@ _LANGUAGES = {
 class LargeASRConfig(datasets.BuilderConfig):
     """BuilderConfig for LargeASR."""
 
-    def __init__(self, name, sub_version, **kwargs):
+    def __init__(self, name, **kwargs):
         """
         Args:
           data_dir: `string`, the path to the folder containing the files in the
@@ -57,14 +53,9 @@ class LargeASRConfig(datasets.BuilderConfig):
           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}"
+        description = f"Large Sinhala dataset in {self.language} of {self.date_of_snapshot}."
         super(LargeASRConfig, self).__init__(
             name=name, version=datasets.Version("1.0.0", ""), description=description, **kwargs
         )
@@ -76,12 +67,7 @@ class LargeASR(datasets.GeneratorBasedBuilder):
         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()
     ]
@@ -92,7 +78,6 @@ class LargeASR(datasets.GeneratorBasedBuilder):
                 "filename": datasets.Value("string"),
                 "x": datasets.Value("string"),
                 "sentence": datasets.Value("string"),
-                "full": datasets.Value("string"),
                 "file": datasets.Value("string"),
             }
         )
@@ -111,9 +96,11 @@ class LargeASR(datasets.GeneratorBasedBuilder):
 
     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")
+        data_urls = [_DATA_URL.format(i) for i in (string.digits + string.ascii_lowercase[:6])]
+        dl_path = dl_manager.download_and_extract(data_urls)
+        print(dl_path)
+        abs_path_to_data = os.path.join('./')
+        abs_path_to_clips = os.path.join(dl_path)
 
         return [
             datasets.SplitGenerator(

test.tsv

@@ -1,3 +1,3 @@
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-oid sha256:3f128900942286c77805da55efd4c6bdda0948146ead46b0c40f506ea4ac8c89
-size 3256626
+oid sha256:9aff9fa04dbaa71e7605ca1cca8e7550e8d1aac450e4671f972bc6e9c656b3eb
+size 2886835

train.tsv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3f128900942286c77805da55efd4c6bdda0948146ead46b0c40f506ea4ac8c89
-size 3256626
+oid sha256:5d95b9cf6f910d988454486a7feac59f935ee85efb5849d1f442d5feb6ab429f
+size 16324488