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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
_BASE_URL = "https://huggingface.co/datasets/Seon25/hausa_2_eng_2/resolve/main"
_AUDIO_URL = _BASE_URL + "audio/ha/{split}/ha_{split}_{shard_idx}.tar"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
_N_SHARDS_URL ="n_shards.json"
class Hausa2Eng(datasets.GeneratorBasedBuilder):
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_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 validated hours of speech "
f" in 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,
)
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 |