Delete runyakore_data.py

runyakore_data.py

@@ -1,187 +0,0 @@
-# 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{tericvoices:2024,
-  author = {Bateesa, T. and Wairagala, EP. },
-  title = {Teric Lab: A Massively-Multilingual Speech Corpus},
-}
-"""
-
-_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/Tobius/tericvoices_v2/resolve/main/"
-
-_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar"
-
-_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
-
-_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 = (
-            "Teric Lab is an East African AI startup levereaging AI to ease communication in local African local langauges"
-            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(
-            {
-                "path": datasets.Value("string"),
-                "audio": datasets.features.Audio(sampling_rate=16_000),
-                "transcription": datasets.Value("string"),
-                "sample_rate": datasets.Value("int64"),
-                "speaker_id": 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):
-        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","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,
-            "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") as f:
-            reader = csv.DictReader(f, delimiter="\t", 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