Delete loading script

voxpopuli.py

@@ -1,223 +0,0 @@
-from collections import defaultdict
-import os
-import json
-import csv
-
-import datasets
-
-
-_DESCRIPTION = """
-A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
-"""
-
-_CITATION = """
-@inproceedings{wang-etal-2021-voxpopuli,
-    title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, 
-    Semi-Supervised Learning and Interpretation",
-    author = "Wang, Changhan  and
-      Riviere, Morgane  and
-      Lee, Ann  and
-      Wu, Anne  and
-      Talnikar, Chaitanya  and
-      Haziza, Daniel  and
-      Williamson, Mary  and
-      Pino, Juan  and
-      Dupoux, Emmanuel",
-    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics 
-    and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
-    month = aug,
-    year = "2021",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.acl-long.80",
-    doi = "10.18653/v1/2021.acl-long.80",
-    pages = "993--1003",
-}
-"""
-
-_HOMEPAGE = "https://github.com/facebookresearch/voxpopuli"
-
-_LICENSE = "CC0, also see https://www.europarl.europa.eu/legal-notice/en/"
-
-_ASR_LANGUAGES = [
-    "en", "de", "fr", "es", "pl", "it", "ro", "hu", "cs", "nl", "fi", "hr",
-    "sk", "sl", "et", "lt"
-]
-_ASR_ACCENTED_LANGUAGES = [
-    "en_accented"
-]
-
-_LANGUAGES = _ASR_LANGUAGES + _ASR_ACCENTED_LANGUAGES
-
-_BASE_DATA_DIR = "data/"
-
-_N_SHARDS_FILE = _BASE_DATA_DIR + "n_files.json"
-
-_AUDIO_ARCHIVE_PATH = _BASE_DATA_DIR + "{lang}/{split}/{split}_part_{n_shard}.tar.gz"
-
-_METADATA_PATH = _BASE_DATA_DIR + "{lang}/asr_{split}.tsv"
-
-
-class VoxpopuliConfig(datasets.BuilderConfig):
-    """BuilderConfig for VoxPopuli."""
-
-    def __init__(self, name, languages="all", **kwargs):
-        """
-        Args:
-          name: `string` or `List[string]`:
-            name of a config: either one of the supported languages or "multilang" for many languages.
-            By default, "multilang" config includes all languages, including accented ones.
-            To specify a custom set of languages, pass them to the `languages` parameter
-          languages: `List[string]`: if config is "multilang" can be either "all" for all available languages,
-            excluding accented ones (default), or a custom list of languages.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        if name == "multilang":
-            self.languages = _ASR_LANGUAGES if languages == "all" else languages
-            name = "multilang" if languages == "all" else "_".join(languages)
-        else:
-            self.languages = [name]
-
-        super().__init__(name=name, **kwargs)
-
-
-class Voxpopuli(datasets.GeneratorBasedBuilder):
-    """The VoxPopuli dataset."""
-
-    VERSION = datasets.Version("1.3.0")  # TODO: version
-    BUILDER_CONFIGS = [
-        VoxpopuliConfig(
-            name=name,
-            version=datasets.Version("1.3.0"),
-            )
-        for name in _LANGUAGES + ["multilang"]
-    ]
-    DEFAULT_WRITER_BATCH_SIZE = 256
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "audio_id": datasets.Value("string"),
-                "language": datasets.ClassLabel(names=_LANGUAGES),
-                "audio": datasets.Audio(sampling_rate=16_000),
-                "raw_text": datasets.Value("string"),
-                "normalized_text": datasets.Value("string"),
-                "gender": datasets.Value("string"),  # TODO: ClassVar?
-                "speaker_id": datasets.Value("string"),
-                "is_gold_transcript": datasets.Value("bool"),
-                "accent": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE)
-        with open(n_shards_path) as f:
-            n_shards = json.load(f)
-
-        if self.config.name == "en_accented":
-            splits = ["test"]
-        else:
-            splits = ["train", "dev", "test"]
-
-        audio_urls = defaultdict(dict)
-        for split in splits:
-            for lang in self.config.languages:
-                audio_urls[split][lang] = [
-                    _AUDIO_ARCHIVE_PATH.format(lang=lang, split=split, n_shard=i) for i in range(n_shards[lang][split])
-                ]
-
-        meta_urls = defaultdict(dict)
-        for split in splits:
-            for lang in self.config.languages:
-                meta_urls[split][lang] = _METADATA_PATH.format(lang=lang, split=split)
-
-        # dl_manager.download_config.num_proc = len(urls)
-
-        meta_paths = dl_manager.download_and_extract(meta_urls)
-        audio_paths = dl_manager.download(audio_urls)
-
-        local_extracted_audio_paths = (
-            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
-            {
-                split: {lang: [None] * len(audio_paths[split][lang]) for lang in self.config.languages} for split in splits
-            }
-        )
-        if self.config.name == "en_accented":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "audio_archives": {
-                            lang: [dl_manager.iter_archive(archive) for archive in lang_archives]
-                            for lang, lang_archives in audio_paths["test"].items()
-                        },
-                        "local_extracted_archives_paths": local_extracted_audio_paths["test"],
-                        "metadata_paths": meta_paths["test"],
-                    }
-                ),
-            ]
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "audio_archives": {
-                        lang: [dl_manager.iter_archive(archive) for archive in lang_archives]
-                        for lang, lang_archives in audio_paths["train"].items()
-                    },
-                    "local_extracted_archives_paths": local_extracted_audio_paths["train"],
-                    "metadata_paths": meta_paths["train"],
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "audio_archives": {
-                        lang: [dl_manager.iter_archive(archive) for archive in lang_archives]
-                        for lang, lang_archives in audio_paths["dev"].items()
-                    },
-                    "local_extracted_archives_paths": local_extracted_audio_paths["dev"],
-                    "metadata_paths": meta_paths["dev"],
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "audio_archives": {
-                        lang: [dl_manager.iter_archive(archive) for archive in lang_archives]
-                        for lang, lang_archives in audio_paths["test"].items()
-                    },
-                    "local_extracted_archives_paths": local_extracted_audio_paths["test"],
-                    "metadata_paths": meta_paths["test"],
-                }
-            ),
-        ]
-
-    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
-        assert len(metadata_paths) == len(audio_archives) == len(local_extracted_archives_paths)
-        features = ["raw_text", "normalized_text", "speaker_id", "gender", "is_gold_transcript", "accent"]
-
-        for lang in self.config.languages:
-            assert len(audio_archives[lang]) == len(local_extracted_archives_paths[lang])
-
-            meta_path = metadata_paths[lang]
-            with open(meta_path) as f:
-                metadata = {x["id"]: x for x in csv.DictReader(f, delimiter="\t")}
-
-            for audio_archive, local_extracted_archive_path in zip(audio_archives[lang], local_extracted_archives_paths[lang]):
-                for audio_filename, audio_file in audio_archive:
-                    audio_id = audio_filename.split(os.sep)[-1].split(".wav")[0]
-                    path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
-
-                    yield audio_id, {
-                        "audio_id": audio_id,
-                        "language": lang,
-                        **{feature: metadata[audio_id][feature] for feature in features},
-                        "audio": {"path": path, "bytes": audio_file.read()},
-                    }