Convert dataset to Parquet (#6)

- Convert dataset to Parquet (7beff1e5a421d047a9f28229ac681d4db8f67167)
- Delete loading script (20892cd408129da347dd6bfaf2537600e13fba00)
- Delete legacy dataset_infos.json (24f1589abffbae2ad06f93222411899539330c7e)
- Delete loading script auxiliary file (889cd98c9b44f9b44f956f8a9b450b14aacac4a2)
- Delete data file (03f53271ad80517a94b09af72335a7bf029410f3)
- Delete data file (26a0749bb4f8376b111bd4124c9daddbf3c72e37)


Co-authored-by: Albert Villanova <albertvillanova@users.noreply.huggingface.co>

.gitattributes

@@ -2,3 +2,12 @@ yong1288/train-00000-of-00002.parquet filter=lfs diff=lfs merge=lfs -text
 yong1288/train-00001-of-00002.parquet filter=lfs diff=lfs merge=lfs -text
 yong1288/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 yong1288/validation-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/train-00000-of-00007.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/train-00001-of-00007.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/train-00002-of-00007.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/train-00003-of-00007.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/train-00004-of-00007.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/train-00005-of-00007.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/train-00006-of-00007.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
+japh1234/validation-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -21,18 +21,19 @@ pretty_name: Pangloss
 language_bcp47:
 - x-japh1234
 - x-yong1288
-language_details: "jya consists of japh1234 (Glottolog code); nru consists of yong1288 (Glottolog code)"
+language_details: jya consists of japh1234 (Glottolog code); nru consists of yong1288
+  (Glottolog code)
 tags:
 - speech-recognition
 configs:
 - config_name: japh1234
   data_files:
   - split: train
-    path: japh1234/train.csv
+    path: japh1234/train-*
   - split: test
-    path: japh1234/test.csv
+    path: japh1234/test-*
   - split: validation
-    path: japh1234/validation.csv
+    path: japh1234/validation-*
 - config_name: yong1288
   data_files:
   - split: train
@@ -42,7 +43,37 @@ configs:
   - split: validation
     path: yong1288/validation-*
 dataset_info:
-  config_name: yong1288
+- config_name: japh1234
+  features:
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: sentence
+    dtype: string
+  - name: doctype
+    dtype: string
+  - name: speaker
+    dtype: string
+  - name: translation:fr
+    dtype: string
+  - name: translation:zh
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3289610572.247
+    num_examples: 28677
+  - name: test
+    num_bytes: 406912809.016
+    num_examples: 3584
+  - name: validation
+    num_bytes: 413971210.98
+    num_examples: 3585
+  download_size: 4036606025
+  dataset_size: 4110494592.243
+- config_name: yong1288
   features:
   - name: path
     dtype: string

dataset_infos.json

@@ -1,162 +0,0 @@
-{
-    "japh1234": {
-        "description": "These datasets are extracts from the Pangloss collection and have\nbeen preprocessed for ASR experiments in Na and Japhug.\n",
-        "citation": {
-            "yong1288": "\n@misc{michaud_alexis_2021_5336698,\nauthor       = {Michaud, Alexis and\n                Galliot, Benjamin and\n                Guillaume, S\u00e9verine},\ntitle        = {{Yongning Na for Natural Language Processing: a\n                single-speaker audio corpus with transcriptions}},\nmonth        = aug,\nyear         = 2021,\npublisher    = {Zenodo},\nversion      = {1.0},\ndoi          = {10.5281/zenodo.5336698},\nurl          = {https://doi.org/10.5281/zenodo.5336698}\n    }\n    ",
-            "japh1234": "@misc{jacques_guillaume_2021_5521112,\nauthor       = {Jacques, Guillaume and\n                Galliot, Benjamin and\n                Guillaume, S\u00e9verine},\ntitle        = {{Japhug for Natural Language Processing: a single-\n                speaker audio corpus with transcriptions}},\nmonth        = sep,\nyear         = 2021,\npublisher    = {Zenodo},\nversion      = {1.0},\ndoi          = {10.5281/zenodo.5521112},\nurl          = {https://doi.org/10.5281/zenodo.5521112}\n    }\n"
-        },
-        "homepage": "https://pangloss.cnrs.fr/",
-        "license": "https://creativecommons.org/licenses/by-nc-sa/4.0/fr/legalcode",
-        "features": {
-            "path": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "audio": {
-                "sampling_rate": 16000,
-                "_type": "Audio"
-            },
-            "sentence": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "doctype": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "translation:fr": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "translation:zh": {
-                "dtype": "string",
-                "_type": "Value"
-            }
-        },
-        "task_templates": [
-            {
-                "task": "automatic-speech-recognition",
-                "transcription_column": "forme"
-            }
-        ],
-        "builder_name": "pangloss",
-        "dataset_name": "pangloss",
-        "config_name": "japh1234",
-        "version": {
-            "version_str": "1.0.0",
-            "major": 1,
-            "minor": 0,
-            "patch": 0
-        },
-        "splits": {
-            "train": {
-                "name": "train",
-                "num_bytes": 9894596,
-                "num_examples": 28677,
-                "dataset_name": "pangloss"
-            },
-            "test": {
-                "name": "test",
-                "num_bytes": 1235918,
-                "num_examples": 3584,
-                "dataset_name": "pangloss"
-            },
-            "validation": {
-                "name": "validation",
-                "num_bytes": 1239396,
-                "num_examples": 3585,
-                "dataset_name": "pangloss"
-            }
-        },
-        "download_checksums": {
-            "https://mycore.core-cloud.net/index.php/s/kuQCxmyVcUFWroV/download": {
-                "num_bytes": 3035269521,
-                "checksum": "e01ec3d1ef88e6ae9e6834fb7ac803cfc8f484ba6a33dfdab528fa5cefd36ea9"
-            }
-        },
-        "download_size": 3035269521,
-        "dataset_size": 12369910,
-        "size_in_bytes": 3047639431
-    },
-    "yong1288": {
-        "description": "These datasets are extracts from the Pangloss collection and have\nbeen preprocessed for ASR experiments in Na and Japhug.\n",
-        "citation": {
-            "yong1288": "\n@misc{michaud_alexis_2021_5336698,\nauthor       = {Michaud, Alexis and\n                Galliot, Benjamin and\n                Guillaume, S\u00e9verine},\ntitle        = {{Yongning Na for Natural Language Processing: a\n                single-speaker audio corpus with transcriptions}},\nmonth        = aug,\nyear         = 2021,\npublisher    = {Zenodo},\nversion      = {1.0},\ndoi          = {10.5281/zenodo.5336698},\nurl          = {https://doi.org/10.5281/zenodo.5336698}\n    }\n    ",
-            "japh1234": "@misc{jacques_guillaume_2021_5521112,\nauthor       = {Jacques, Guillaume and\n                Galliot, Benjamin and\n                Guillaume, S\u00e9verine},\ntitle        = {{Japhug for Natural Language Processing: a single-\n                speaker audio corpus with transcriptions}},\nmonth        = sep,\nyear         = 2021,\npublisher    = {Zenodo},\nversion      = {1.0},\ndoi          = {10.5281/zenodo.5521112},\nurl          = {https://doi.org/10.5281/zenodo.5521112}\n    }\n"
-        },
-        "homepage": "https://pangloss.cnrs.fr/",
-        "license": "https://creativecommons.org/licenses/by-nc-sa/4.0/fr/legalcode",
-        "features": {
-            "path": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "audio": {
-                "sampling_rate": 16000,
-                "_type": "Audio"
-            },
-            "sentence": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "doctype": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "speaker": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "translation:fr": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "translation:en": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "translation:zh": {
-                "dtype": "string",
-                "_type": "Value"
-            }
-        },
-        "task_templates": [
-            {
-                "task": "automatic-speech-recognition",
-                "transcription_column": "forme"
-            }
-        ],
-        "builder_name": "pangloss",
-        "dataset_name": "pangloss",
-        "config_name": "yong1288",
-        "version": {
-            "version_str": "1.0.0",
-            "major": 1,
-            "minor": 0,
-            "patch": 0
-        },
-        "splits": {
-            "train": {
-                "name": "train",
-                "num_bytes": 794594554.85,
-                "num_examples": 8630,
-                "dataset_name": null
-            },
-            "test": {
-                "name": "test",
-                "num_bytes": 103631590.762,
-                "num_examples": 1078,
-                "dataset_name": null
-            },
-            "validation": {
-                "name": "validation",
-                "num_bytes": 97018775.543,
-                "num_examples": 1079,
-                "dataset_name": null
-            }
-        },
-        "download_size": 968237292,
-        "dataset_size": 995244921.155,
-        "size_in_bytes": 1963482213.155
-    }
-}

dummy/japh1234/1.0.0/dummy_data.zip → japh1234/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:423e32e561006f062e3f17a89e7b5a4df1bd3a7f43960b14aaa2f7fd39a6b8b2
-size 3180
+oid sha256:c9548fa8007bd1c18a8601af8be8689826b14dfe71743aee1a1adf0da9ef719f
+size 402264115

dummy/yong1288/1.0.0/dummy_data.zip → japh1234/train-00000-of-00007.parquet

@@ -1,3 +1,3 @@
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-size 3108
+oid sha256:171a70669352221884202b940320630821fe34dd99d04e6b8a7233c40d1b1ca1
+size 455481747

japh1234/train-00001-of-00007.parquet

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+size 459655999

japh1234/train-00002-of-00007.parquet

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japh1234/train-00003-of-00007.parquet

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japh1234/train-00004-of-00007.parquet

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+size 467704151

japh1234/train-00005-of-00007.parquet

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+size 464824929

japh1234/train-00006-of-00007.parquet

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+size 459263887

japh1234/validation-00000-of-00001.parquet

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pangloss.py

@@ -1,202 +0,0 @@
-# Copyright 2020 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.
-"""Pangloss datasets for Yongning Na (yong1288) and Japhug (japh1234)"""
-
-import csv
-import json
-import os
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-_CITATION = {
-    "yong1288": """
-@misc{michaud_alexis_2021_5336698,
-author       = {Michaud, Alexis and
-                Galliot, Benjamin and
-                Guillaume, Séverine},
-title        = {{Yongning Na for Natural Language Processing: a
-                single-speaker audio corpus with transcriptions}},
-month        = aug,
-year         = 2021,
-publisher    = {Zenodo},
-version      = {1.0},
-doi          = {10.5281/zenodo.5336698},
-url          = {https://doi.org/10.5281/zenodo.5336698}
-    }
-    """,
-    "japh1234": """\
-@misc{jacques_guillaume_2021_5521112,
-author       = {Jacques, Guillaume and
-                Galliot, Benjamin and
-                Guillaume, Séverine},
-title        = {{Japhug for Natural Language Processing: a single-
-                speaker audio corpus with transcriptions}},
-month        = sep,
-year         = 2021,
-publisher    = {Zenodo},
-version      = {1.0},
-doi          = {10.5281/zenodo.5521112},
-url          = {https://doi.org/10.5281/zenodo.5521112}
-    }
-"""
-}
-
-_DESCRIPTION = """\
-These datasets are extracts from the Pangloss collection and have
-been preprocessed for ASR experiments in Na and Japhug.
-"""
-
-_HOMEPAGE = "https://pangloss.cnrs.fr/"
-
-_LICENSE = "https://creativecommons.org/licenses/by-nc-sa/4.0/fr/legalcode"
-
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-
-_VERSION = datasets.Version("1.0.0")
-
-_LANGUAGES = {
-    "yong1288": {
-        "url": "https://mycore.core-cloud.net/index.php/s/vaGMeRf4Iij8MWR/download",
-        "homepage": "https://zenodo.org/record/5336698",
-        "description": "Yongning Na dataset",
-        "translations": ["fr", "en", "zh"]
-    },
-    "japh1234": {
-        "url": "https://mycore.core-cloud.net/index.php/s/kuQCxmyVcUFWroV/download",
-        "homepage": "https://zenodo.org/record/5521112",
-        "description": "Japhug dataset",
-        "translations": ["fr", "zh"]
-    }
-}
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class PanglossDataset(datasets.GeneratorBasedBuilder):
-    """The Pangloss datasets are extracts from Pangloss Collections that can be used for ASR experiments in these languages."""
-    field_translations = {
-        "chemin_audio": "path",
-        "nature": "doctype",
-        "forme": "sentence",
-        "locuteur": "speaker",
-        "traduction:fr": "translation:fr",
-        "traduction:en": "translation:en",
-        "traduction:zh": "translation:zh"
-    }
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=language_name, version=_VERSION, description=language_data["description"])
-        for language_name, language_data in _LANGUAGES.items()
-    ]
-
-    #DEFAULT_CONFIG_NAME = "na"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        features = datasets.Features(
-            {
-                "path": datasets.Value("string"),
-                "audio": datasets.features.Audio(sampling_rate=16_000),
-                "sentence": datasets.Value("string"),
-                "doctype": datasets.Value("string"),
-                "speaker": datasets.Value("string"),
-                **{f"translation:{language_code}": datasets.Value("string") for language_code in _LANGUAGES[self.config.name]["translations"]}
-            }
-        )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="forme")],
-
-        )
-
-    def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _LANGUAGES[self.config.name]["url"]
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, self.config.name, "train.csv"),
-                    "split": "train"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, self.config.name, "test.csv"),
-                    "split": "test"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, self.config.name, "validation.csv"),
-                    "split": "validation"
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as file_descriptor:
-            reader = csv.DictReader(file_descriptor)
-            for key, row in enumerate(reader):
-                translated_fieldnames = [self.field_translations[fieldname] for fieldname in reader.fieldnames if fieldname in self.field_translations.keys()]
-                data = dict(zip(translated_fieldnames, row.values()))
-                data["audio"] = os.path.join(os.path.dirname(filepath), data["path"])
-                # Yields examples as (key, example) tuples
-                yield key, data
-
-
-if __name__ == "__main__":
-    # for language in _LANGUAGES.keys():
-    datasets.load_dataset("datasets/pangloss/pangloss.py", "japh1234")
-
-# datasets-cli test datasets/pangloss --save_infos --all_configs
-# datasets-cli dummy_data datasets/pangloss --auto_generate
-

test.py

@@ -1,3 +0,0 @@
-from datasets import load_dataset
-
-dataset = load_dataset("Lacito/pangloss", "japh1234")