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# coding=utf-8
# Copyright 2023 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.
""" NENA Speech Dataset"""
import csv
import os
import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
from .dialects import DIALECTS
from .release_stats import STATS
_HOMEPAGE = "https://nena.ames.cam.ac.uk/"
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
_BASE_URL = "https://huggingface.co/datasets/mnazari/nena_speech_1_0_test/resolve/main/"
_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.dialect = kwargs.pop("dialect", None)
self.release_date = kwargs.pop("release_date", None)
self.num_examples = kwargs.pop("num_examples", 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"NENA Speech dataset in the {self.dialect} dialect 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_examples} examples and has a size of {self.size_human}."
)
super(NENASpeechConfig, self).__init__(
name=name,
version=datasets.Version(version),
description=description,
**kwargs,
)
class NENASpeech(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
BUILDER_CONFIGS = sorted([
NENASpeechConfig(
name=dialect,
version=STATS["version"],
dialect=DIALECTS[dialect],
release_date=STATS["date"],
num_examples=dialect_stats["totalExamples"],
num_speakers=dialect_stats["speakers"],
# validated_hr=float(dialect_stats["validHrs"]) if dialect_stats["validHrs"] else None,
# total_hr=float(dialect_stats["totalHrs"]) if dialect_stats["totalHrs"] else None,
# size_bytes=int(dialect_stats["size"]) if dialect_stats["size"] else None,
)
for dialect, dialect_stats in STATS["dialects"].items()
], key=lambda config: config.num_examples)
def _info(self):
total_dialects = len(STATS["dialects"])
total_duration = STATS["durationLabelled"] / 60
description = (
"NENA Speech is a multimodal dataset to help teach machines how real people speak "
"the Northeastern Neo-Aramaic dialects. The dataset currently consists of "
f"{total_duration} validated minutes of speech in {total_dialects} dialects, but "
"more examples are actively being crowdsourced."
)
features = datasets.Features(
{
"transcription": datasets.Value("string"),
"translation": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=48_000),
"locale": datasets.Value("string"),
"proficiency": datasets.Value("string"),
"age": datasets.Value("string"),
"crowdsourced": datasets.Value("bool"),
"unlabelled": datasets.Value("bool"),
"interrupted": datasets.Value("bool"),
"client_id": datasets.Value("string"),
"path": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=description,
# citation=_CITATION,
homepage=_HOMEPAGE,
license=_LICENSE,
features=features,
supervised_keys=None,
)
def _split_generators(self, dl_manager):
dialect = self.config.name
audio_urls = {}
splits = ("train", "dev", "test")
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),
"archive": dl_manager.iter_archive(archive_paths.get(split)),
"meta_path": meta_paths[split],
},
),
)
return split_generators
def _generate_examples(self, local_extracted_archive_paths, archive, 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..."):
for field in data_fields:
if field not in row:
row[field] = ""
metadata[row["path"]] = row
for path, file in archive:
_, filename = os.path.split(path)
if filename in metadata:
result = dict(metadata[filename])
path = os.path.join(local_extracted_archive_paths, path) if local_extracted_archive_paths else path
result["audio"] = {"path": path, "bytes": file.read()}
result["path"] = path
yield path, result
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