Update hausa_2_eng_2.py

hausa_2_eng_2.py

@@ -0,0 +1,185 @@
+
+
+
+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
+}
+"""
+
+_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+
+_HOMEPAGE = "https://huggingface.co/datasets/Seon25/hausa_2_eng_2"
+
+_BASE_URL = "https://huggingface.co/datasets/Seon25/hausa_2_eng_2/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 Hausa2EngConfig(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(Hausa2EngConfig, self).__init__(
+            name=name,
+            version=datasets.Version(version),
+            description=description,
+            **kwargs,
+        )
+
+
+
+class Hausa2Eng(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    BUILDER_CONFIGS = [
+        Hausa2EngConfig(
+            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):
+        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", "other", "invalidated")
+        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") 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(".mp3"):
+                    row["path"] += ".mp3"
+                # 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