retry

common_voice.py

@@ -1,290 +0,0 @@
-# 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.
-""" Common Voice Dataset"""
-
-
-import os
-
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-
-_DATA_URL = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/{}.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.
-"""
-
-_HOMEPAGE = "https://commonvoice.mozilla.org/bn/datasets"
-
-_LICENSE = "https://github.com/common-voice/common-voice/blob/main/LICENSE"
-
-_LANGUAGES = {
-    "bn": {
-        "Language": "Bengali",
-        "Date": "2022-04-27",
-        "Size": "8 GB",
-        "Version": "bn_399h_2022-04-27",
-        "Validated_Hr_Total": 56,
-        "Overall_Hr_Total": 399,
-        "Number_Of_Voice": 19863,
-    },
-}
-
-
-class CommonVoiceConfig(datasets.BuilderConfig):
-    """BuilderConfig for CommonVoice."""
-
-    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"Common Voice 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(CommonVoiceConfig, self).__init__(
-            name=name,
-            version=datasets.Version("6.1.0", ""),
-            description=description,
-            **kwargs,
-        )
-
-
-class CommonVoice(datasets.GeneratorBasedBuilder):
-
-    DEFAULT_WRITER_BATCH_SIZE = 1000
-    BUILDER_CONFIGS = [
-        CommonVoiceConfig(
-            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(
-            {
-                "client_id": datasets.Value("string"),
-                "path": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=48_000),
-                "sentence": datasets.Value("string"),
-                "up_votes": datasets.Value("int64"),
-                "down_votes": datasets.Value("int64"),
-                "age": datasets.Value("string"),
-                "gender": datasets.Value("string"),
-                "accent": datasets.Value("string"),
-                "locale": datasets.Value("string"),
-                "segment": datasets.Value("string"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[
-                AutomaticSpeechRecognition(
-                    audio_column="audio", 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_clips = "/".join([path_to_data, "clips"])
-        metadata_filepaths = {
-            split: "/".join([path_to_data, f"{split}.tsv"])
-            for split in ["train", "test", "dev", "other", "validated", "invalidated"]
-        }
-        # (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,
-                },
-            ),
-            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,
-                },
-            ),
-            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["dev"],
-                    "path_to_clips": path_to_clips,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="other",
-                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["other"],
-                    "path_to_clips": path_to_clips,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="validated",
-                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["validated"],
-                    "path_to_clips": path_to_clips,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="invalidated",
-                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["invalidated"],
-                    "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())
-
-        # 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

dataset_infos.json

@@ -1,133 +0,0 @@
-{
-    "bn": {
-        "description": "Common Voice is Mozilla's initiative to help teach machines how real people speak.\nThe dataset currently consists of 7,335 validated hours of speech in 60 languages, but we\u2019re always adding more voices and languages.\n",
-        "citation": "@inproceedings{commonvoice:2020,\n  author = {Samiul Alam, Asif Sushmit, Zaowad Abdullah, Md. Shahrin Nakkhatra, Md. N. Ansary, Syed Mobassir Hossen, Tahsin Reasat, L. and Tyers, F. M. and Weber, G.},\n  title = {Common Voice: A Massively-Multilingual Speech Corpus},\n  booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},\n  pages = {4211--4215},\n  year = 2020\n}\n",
-        "homepage": "https://commonvoice.mozilla.org/bn/datasets",
-        "license": "https://github.com/common-voice/common-voice/blob/main/LICENSE",
-        "features": {
-            "client_id": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            },
-            "path": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            },
-            "audio": {
-                "sampling_rate": 48000,
-                "mono": true,
-                "decode": true,
-                "id": null,
-                "_type": "Audio"
-            },
-            "sentence": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            },
-            "up_votes": {
-                "dtype": "int64",
-                "id": null,
-                "_type": "Value"
-            },
-            "down_votes": {
-                "dtype": "int64",
-                "id": null,
-                "_type": "Value"
-            },
-            "age": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            },
-            "gender": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            },
-            "accent": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            },
-            "locale": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            },
-            "segment": {
-                "dtype": "string",
-                "id": null,
-                "_type": "Value"
-            }
-        },
-        "post_processed": null,
-        "supervised_keys": null,
-        "task_templates": [
-            {
-                "task": "automatic-speech-recognition",
-                "audio_file_path_column": "path",
-                "transcription_column": "sentence"
-            }
-        ],
-        "builder_name": "common_voice",
-        "config_name": "en",
-        "version": {
-            "version_str": "6.1.0",
-            "description": "",
-            "major": 6,
-            "minor": 1,
-            "patch": 0
-        },
-        "splits": {
-            "train": {
-                "name": "train",
-                "num_bytes": 26088826658,
-                "num_examples": 564337,
-                "dataset_name": "common_voice"
-            },
-            "test": {
-                "name": "test",
-                "num_bytes": 758718688,
-                "num_examples": 16164,
-                "dataset_name": "common_voice"
-            },
-            "validation": {
-                "name": "validation",
-                "num_bytes": 795638801,
-                "num_examples": 16164,
-                "dataset_name": "common_voice"
-            },
-            "other": {
-                "name": "other",
-                "num_bytes": 5796244022,
-                "num_examples": 169895,
-                "dataset_name": "common_voice"
-            },
-            "validated": {
-                "name": "validated",
-                "num_bytes": 48425872575,
-                "num_examples": 1224864,
-                "dataset_name": "common_voice"
-            },
-            "invalidated": {
-                "name": "invalidated",
-                "num_bytes": 9122973965,
-                "num_examples": 189562,
-                "dataset_name": "common_voice"
-            }
-        },
-        "download_checksums": {
-            "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/en.tar.gz": {
-                "num_bytes": 60613063630,
-                "checksum": "0f8fdfc4fe715738be94ee49c4fb63d5f1608d2e6a43a2bed80f6cb871171c36"
-            }
-        },
-        "download_size": 60613063630,
-        "post_processing_size": null,
-        "dataset_size": 90988274709,
-        "size_in_bytes": 151601338339
-    }
-}