added new files

audio.tgz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba58e91bc9a87c57cb226a75a81734811ad274d176d9210e6549184822c4ae76
+size 23521945

languages.py

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+LANGUAGES = {
+    'ace': 'Acehnese',
+    'bal': 'Balinese',
+    'bug': 'Bugisnese',
+    'id': 'Indonesian',
+    'min': 'Minangkabau',
+    'jav': 'Javanese',
+    'sun': 'Sundanese'
+}

release_stats.py

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+STATS = {
+    "name": "",
+    "downloadUrl": "",
+    "version": "1.0",
+    "date": ""
+}

test01.py

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+# 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 urllib
+
+import datasets
+import requests
+from datasets.utils.py_utils import size_str
+from huggingface_hub import HfApi, HfFolder
+
+from .languages import LANGUAGES
+from .release_stats import STATS
+
+_CITATION = """\
+"""
+
+_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
+
+_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+
+class Test01Config(datasets.BuilderConfig):
+    """BuilderConfig for Test01."""
+
+    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(Test01Config, self).__init__(
+            name=name,
+            version=datasets.Version(version),
+            description=description,
+            **kwargs,
+        )
+
+
+class Test01(datasets.GeneratorBasedBuilder):
+    DEFAULT_CONFIG_NAME = "id"
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    BUILDER_CONFIGS = [
+        Test01Config(
+            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"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=description,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            version=self.config.version,
+            # task_templates=[
+            #     AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="sentence")
+            # ],
+        )
+
+    def _get_bundle_url(self, locale, url_template):
+        # path = encodeURIComponent(path)
+        path = url_template.replace("{locale}", locale)
+        path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
+        # use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
+        # response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
+        response = requests.get(f"{_API_URL}/bucket/dataset/{path}", timeout=10.0).json()
+        return response["url"]
+
+    def _log_download(self, locale, bundle_version, auth_token):
+        if isinstance(auth_token, bool):
+            auth_token = HfFolder().get_token()
+        whoami = HfApi().whoami(auth_token)
+        email = whoami["email"] if "email" in whoami else ""
+        payload = {"email": email, "locale": locale, "dataset": bundle_version}
+        requests.post(f"{_API_URL}/{locale}/downloaders", json=payload).json()
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        hf_auth_token = dl_manager.download_config.use_auth_token
+        if hf_auth_token is None:
+            raise ConnectionError(
+                "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
+            )
+
+        bundle_url_template = STATS["bundleURLTemplate"]
+        bundle_version = bundle_url_template.split("/")[0]
+        dl_manager.download_config.ignore_url_params = True
+
+        self._log_download(self.config.name, bundle_version, hf_auth_token)
+        archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
+
+        if self.config.version < datasets.Version("5.0.0"):
+            path_to_data = ""
+        else:
+            path_to_data = "/".join([bundle_version, self.config.name])
+        path_to_clips = "/".join([path_to_data, "clips"]) if path_to_data else "clips"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "local_extracted_archive": local_extracted_archive,
+                    "archive_iterator": dl_manager.iter_archive(archive_path),
+                    "metadata_filepath": "/".join([path_to_data, "train.tsv"]) if path_to_data else "train.tsv",
+                    "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())
+        metadata = {}
+        metadata_found = False
+        for path, f in archive_iterator:
+            if path == metadata_filepath:
+                metadata_found = True
+                lines = (line.decode("utf-8") for line in f)
+                reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for row in reader:
+                    # set absolute path for mp3 audio file
+                    if not row["path"].endswith(".mp3"):
+                        row["path"] += ".mp3"
+                    row["path"] = os.path.join(path_to_clips, row["path"])
+                    # 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
+            elif path.startswith(path_to_clips):
+                assert metadata_found, "Found audio clips before the metadata TSV file."
+                if not metadata:
+                    break
+                if path in metadata:
+                    result = dict(metadata[path])
+                    # set the audio feature and the path to the extracted file
+                    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