Update common_voice_17_0.py

common_voice_17_0.py

@@ -1,200 +1,200 @@
-# coding=utf-8
-# Copyright 2022 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 csv
-import os
-import json
-
-import datasets
-from datasets.utils.py_utils import size_str
-from tqdm import tqdm
-
-from .languages import LANGUAGES
-from .release_stats import STATS
-
-
-_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
-}
-"""
-
-_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
-
-_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
-
-# TODO: change "streaming" to "main" after merge!
-_BASE_URL = "https://huggingface.co/datasets/Mohammadawad1/my-dataset-test-v2/resolve/main/"
-
-_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{split}_audio.tar"
-
-_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}_metadata.csv"
-
-_N_SHARDS_URL = _BASE_URL + "n_shards.json"
-
-
-class CommonVoiceConfig(datasets.BuilderConfig):
-    """BuilderConfig for CommonVoice."""
-
-    def __init__(self, name, version, **kwargs):
-        self.language = kwargs.pop("language", None)
-        self.release_date = kwargs.pop("release_date", None)
-        self.num_clips = kwargs.pop("num_clips", None)
-        self.num_speakers = kwargs.pop("num_speakers", None)
-        self.validated_hr = kwargs.pop("validated_hr", None)
-        self.total_hr = kwargs.pop("total_hr", None)
-        self.size_bytes = kwargs.pop("size_bytes", None)
-        self.size_human = size_str(self.size_bytes)
-        description = (
-            f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. "
-            f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
-            f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
-            f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
-        )
-        super(CommonVoiceConfig, self).__init__(
-            name=name,
-            version=datasets.Version(version),
-            description=description,
-            **kwargs,
-        )
-
-
-class CommonVoice(datasets.GeneratorBasedBuilder):
-    DEFAULT_WRITER_BATCH_SIZE = 1000
-
-    BUILDER_CONFIGS = [
-        CommonVoiceConfig(
-            name=lang,
-            version=STATS["version"],
-            language=LANGUAGES[lang],
-            release_date=STATS["date"],
-            num_clips=lang_stats["clips"],
-            num_speakers=lang_stats["users"],
-            validated_hr=float(lang_stats["validHrs"]) if lang_stats["validHrs"] else None,
-            total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
-            size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
-        )
-        for lang, lang_stats in STATS["locales"].items()
-    ]
-
-    def _info(self):
-        total_languages = len(STATS["locales"])
-        total_valid_hours = STATS["totalValidHrs"]
-        description = (
-            "Common Voice is Mozilla's initiative to help teach machines how real people speak. "
-            f"The dataset currently consists of {total_valid_hours} validated hours of speech "
-            f" in {total_languages} languages, but more voices and languages are always added."
-        )
-        features = datasets.Features(
-            {
-                "client_id": datasets.Value("string"),
-                "path": datasets.Value("string"),
-                "audio": datasets.features.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"),
-                "variant": datasets.Value("string"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=description,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            version=self.config.version,
-        )
-
-    def _split_generators(self, dl_manager):
-        print("Using CommonVoice dataset script _split_generators()")
-
-        lang = self.config.name
-        n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
-        with open(n_shards_path, encoding="utf-8") as f:
-            n_shards = json.load(f)
-
-        audio_urls = {}
-        splits = ("train", "dev", "test")
-        for split in splits:
-            audio_urls[split] = [
-                _AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][split])
-            ]
-        archive_paths = dl_manager.download(audio_urls)
-        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
-
-        meta_urls = {split: _TRANSCRIPT_URL.format(lang=lang, split=split) for split in splits}
-        meta_paths = dl_manager.download_and_extract(meta_urls)
-
-        split_generators = []
-        split_names = {
-            "train": datasets.Split.TRAIN,
-            "dev": datasets.Split.VALIDATION,
-            "test": datasets.Split.TEST,
-        }
-        for split in splits:
-            split_generators.append(
-                datasets.SplitGenerator(
-                    name=split_names.get(split, split),
-                    gen_kwargs={
-                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
-                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
-                        "meta_path": meta_paths[split],
-                    },
-                ),
-            )
-
-        return split_generators
-
-    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
-        data_fields = list(self._info().features.keys())
-        metadata = {}
-        with open(meta_path, encoding="utf-8-sig") as f:
-            reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
-            for row in tqdm(reader, desc="Reading metadata..."):
-                if not row["path"].endswith(".wav"):
-                    row["path"] += ".wav"
-                # accent -> accents in CV 8.0
-                if "accents" in row:
-                    row["accent"] = row["accents"]
-                    del row["accents"]
-                # if data is incomplete, fill with empty values
-                for field in data_fields:
-                    if field not in row:
-                        row[field] = ""
-                metadata[row["path"]] = row
-
-        for i, audio_archive in enumerate(archives):
-            for path, file in audio_archive:
-                _, filename = os.path.split(path)
-                if filename in metadata:
-                    result = dict(metadata[filename])
-                    # set the audio feature and the path to the extracted file
-                    path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
-                    result["audio"] = {"path": path, "bytes": file.read()}
-                    result["path"] = path
-                    yield path, result
+# # coding=utf-8
+# # Copyright 2022 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 csv
+# import os
+# import json
+
+# import datasets
+# from datasets.utils.py_utils import size_str
+# from tqdm import tqdm
+
+# from .languages import LANGUAGES
+# from .release_stats import STATS
+
+
+# _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
+# }
+# """
+
+# _HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
+
+# _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+# # TODO: change "streaming" to "main" after merge!
+# _BASE_URL = "https://huggingface.co/datasets/Mohammadawad1/my-dataset-test-v2/resolve/main/"
+
+# _AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{split}_audio.tar"
+
+# _TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}_metadata.csv"
+
+# _N_SHARDS_URL = _BASE_URL + "n_shards.json"
+
+
+# class CommonVoiceConfig(datasets.BuilderConfig):
+#     """BuilderConfig for CommonVoice."""
+
+#     def __init__(self, name, version, **kwargs):
+#         self.language = kwargs.pop("language", None)
+#         self.release_date = kwargs.pop("release_date", None)
+#         self.num_clips = kwargs.pop("num_clips", None)
+#         self.num_speakers = kwargs.pop("num_speakers", None)
+#         self.validated_hr = kwargs.pop("validated_hr", None)
+#         self.total_hr = kwargs.pop("total_hr", None)
+#         self.size_bytes = kwargs.pop("size_bytes", None)
+#         self.size_human = size_str(self.size_bytes)
+#         description = (
+#             f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. "
+#             f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
+#             f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
+#             f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
+#         )
+#         super(CommonVoiceConfig, self).__init__(
+#             name=name,
+#             version=datasets.Version(version),
+#             description=description,
+#             **kwargs,
+#         )
+
+
+# class CommonVoice(datasets.GeneratorBasedBuilder):
+#     DEFAULT_WRITER_BATCH_SIZE = 1000
+
+#     BUILDER_CONFIGS = [
+#         CommonVoiceConfig(
+#             name=lang,
+#             version=STATS["version"],
+#             language=LANGUAGES[lang],
+#             release_date=STATS["date"],
+#             num_clips=lang_stats["clips"],
+#             num_speakers=lang_stats["users"],
+#             validated_hr=float(lang_stats["validHrs"]) if lang_stats["validHrs"] else None,
+#             total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
+#             size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
+#         )
+#         for lang, lang_stats in STATS["locales"].items()
+#     ]
+
+#     def _info(self):
+#         total_languages = len(STATS["locales"])
+#         total_valid_hours = STATS["totalValidHrs"]
+#         description = (
+#             "Common Voice is Mozilla's initiative to help teach machines how real people speak. "
+#             f"The dataset currently consists of {total_valid_hours} validated hours of speech "
+#             f" in {total_languages} languages, but more voices and languages are always added."
+#         )
+#         features = datasets.Features(
+#             {
+#                 "client_id": datasets.Value("string"),
+#                 "path": datasets.Value("string"),
+#                 "audio": datasets.features.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"),
+#                 "variant": datasets.Value("string"),
+#             }
+#         )
+
+#         return datasets.DatasetInfo(
+#             description=description,
+#             features=features,
+#             supervised_keys=None,
+#             homepage=_HOMEPAGE,
+#             license=_LICENSE,
+#             citation=_CITATION,
+#             version=self.config.version,
+#         )
+
+#     def _split_generators(self, dl_manager):
+#         print("Using CommonVoice dataset script _split_generators()")
+
+#         lang = self.config.name
+#         n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
+#         with open(n_shards_path, encoding="utf-8") as f:
+#             n_shards = json.load(f)
+
+#         audio_urls = {}
+#         splits = ("train", "dev", "test")
+#         for split in splits:
+#             audio_urls[split] = [
+#                 _AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][split])
+#             ]
+#         archive_paths = dl_manager.download(audio_urls)
+#         local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+#         meta_urls = {split: _TRANSCRIPT_URL.format(lang=lang, split=split) for split in splits}
+#         meta_paths = dl_manager.download_and_extract(meta_urls)
+
+#         split_generators = []
+#         split_names = {
+#             "train": datasets.Split.TRAIN,
+#             "dev": datasets.Split.VALIDATION,
+#             "test": datasets.Split.TEST,
+#         }
+#         for split in splits:
+#             split_generators.append(
+#                 datasets.SplitGenerator(
+#                     name=split_names.get(split, split),
+#                     gen_kwargs={
+#                         "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
+#                         "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
+#                         "meta_path": meta_paths[split],
+#                     },
+#                 ),
+#             )
+
+#         return split_generators
+
+#     def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
+#         data_fields = list(self._info().features.keys())
+#         metadata = {}
+#         with open(meta_path, encoding="utf-8-sig") as f:
+#             reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
+#             for row in tqdm(reader, desc="Reading metadata..."):
+#                 if not row["path"].endswith(".wav"):
+#                     row["path"] += ".wav"
+#                 # accent -> accents in CV 8.0
+#                 if "accents" in row:
+#                     row["accent"] = row["accents"]
+#                     del row["accents"]
+#                 # if data is incomplete, fill with empty values
+#                 for field in data_fields:
+#                     if field not in row:
+#                         row[field] = ""
+#                 metadata[row["path"]] = row
+
+#         for i, audio_archive in enumerate(archives):
+#             for path, file in audio_archive:
+#                 _, filename = os.path.split(path)
+#                 if filename in metadata:
+#                     result = dict(metadata[filename])
+#                     # set the audio feature and the path to the extracted file
+#                     path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
+#                     result["audio"] = {"path": path, "bytes": file.read()}
+#                     result["path"] = path
+#                     yield path, result