File size: 5,317 Bytes
3428b9a 8434d7c 3428b9a 8434d7c 3428b9a 8434d7c cbe737e d4dfb7d cbe737e 8434d7c 9554ded 8434d7c 9554ded 8434d7c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
""" NENA Speech Dataset"""
import csv
import os
import json
import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
# _CITATION = """\
# """
# _HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
# _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
# TODO: change this
_BASE_URL = "./"
_AUDIO_URL = _BASE_URL + "audio/{dialect}/{split}.tar"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{dialect}/{split}.tsv"
import datasets
class NENASpeechConfig(datasets.BuilderConfig):
"""BuilderConfig for NENASpeech."""
def __init__(self, name, version, **kwargs):
self.language = kwargs.pop("language", None)
description = (
f"This is a test. "
)
super(NENASpeechConfig, self).__init__(
name=name,
version=datasets.Version(version),
description=description,
**kwargs,
)
class NENASpeech(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
BUILDER_CONFIGS = [
NENASpeechConfig(
name='curmi',
version='1.0.4',
language='assyrian',
),
NENASpeechConfig(
name='jurmi',
version='1.0.4',
language='assyrian',
)
# for lang, lang_stats in STATS["locales"].items()
]
def _info(self):
# total_languages = len(STATS["locales"])
# total_valid_hours = STATS["totalValidHrs"]
description = (
"description from _info"
# "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(
{
"transcription": datasets.Value("string"),
"translation": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=48_000),
"path": datasets.Value("string"),
"age": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=description,
# citation=_CITATION,
# homepage=_HOMEPAGE,
# license=_LICENSE,
features=features,
supervised_keys=None,
version=self.config.version,
)
def _split_generators(self, dl_manager):
dialect = self.config.name
audio_urls = {}
splits = ("train", "dev", "test", "other", "invalidated")
for split in splits:
audio_urls[split] = _AUDIO_URL.format(dialect=dialect, split=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(dialect=dialect, 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
|