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# Copyright 2020 The HuggingFace Datasets Authors and 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.
"""Waxal Wolof Dataset."""
import csv
import os
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
_MODALITIES_COMBINATION = [
["audio", "image", "text"],
["audio", "text"],
["audio", "image"],
["image", "text"],
["audio"],
["image"],
["text"],
]
_URLs = {
"train-transcriptions": "train_transcriptions.csv",
"test-transcriptions": "test_transcriptions.csv",
"image-files": "images.tar.gz",
"captioned-images": "captioned_images.tar.gz",
"audio-files": "audios.tar.gz",
"transcribed-audio": "transcribed_audio.tar.gz"
}
class WaxalConfig(datasets.BuilderConfig):
"""BuilderConfig for Waxal dataset."""
def __init__(self, name, version, modalities, **kwargs):
self.modalities = modalities
self.language = kwargs.pop("language", None)
modalities_str = " to ".join(self.modalities)
description = f"Waxal {modalities_str} in {self.language}"
super(WaxalConfig, self).__init__(
name=name,
version=version,
description=description,
**kwargs,
)
class WaxalWolof(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
WaxalConfig(
name="-".join(modalities),
version=datasets.Version("1.1.0"),
modalities=modalities,
language="wolof",
)
for modalities in _MODALITIES_COMBINATION
]
DEFAULT_CONFIG_NAME = "audio-text"
def _info(self):
features = {}
if "audio" in self.config.modalities:
features["audio"] = datasets.features.Audio()
features["audio_duration"] = datasets.Value("float")
features["participant"] = datasets.Value("int32")
if "image" in self.config.modalities:
features["image"] = datasets.features.Image()
if "text" in self.config.modalities:
features["text_annotation"] = datasets.Value("string")
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
@property
def with_audio(self):
return "audio" in self.config.modalities
@property
def with_image(self):
return "image" in self.config.modalities
@property
def with_text(self):
return "text" in self.config.modalities
def _split_generators(self, dl_manager):
audio_url_key = "transcribed-audio" if self.with_text else "audio-files"
image_url_key = "captioned-images" if self.with_text else "image-files"
audio_files = (
dl_manager.download_and_extract(_URLs[audio_url_key])
if self.with_audio
else None
)
image_files = (
dl_manager.download_and_extract(_URLs[image_url_key])
if self.with_image
else None
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"metadata_path": dl_manager.download(
_URLs["train-transcriptions"]
),
"audio_files": audio_files,
"image_files": image_files,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"metadata_path": dl_manager.download(
_URLs["test-transcriptions"]
),
"audio_files": audio_files,
"image_files": image_files,
},
),
]
def _generate_examples(
self,
metadata_path,
audio_files=None,
path_to_audio="transcribed_audio",
image_files=None,
path_to_images="captioned_images",
):
metadata = {}
with open(metadata_path) as buf:
reader = csv.DictReader(buf)
for row in reader:
del row["prompt"] # TODO(shpotes): remove it in future versions!
if self.with_text:
if not row["transcription"]:
continue
if self.with_image:
row["image_file_path"] = os.path.join(
path_to_images, self.config.language, row["image_file_name"]
)
if self.with_audio:
row["audio_file_path"] = os.path.join(
path_to_audio, row["audio_file_name"]
) # TODO(shpotes): add lang name to the csv path.
metadata[row["idx"]] = row
for idx, sample in metadata.items():
result = {}
if self.with_audio:
result["participant"] = sample["participant"]
result["audio_duration"] = sample["duration"]
audio_path = os.path.join(audio_files, sample["audio_file_path"])
with open(audio_path, "rb") as f:
result["audio"] = {"path": audio_path, "bytes": f.read()}
if self.with_image:
image_path = os.path.join(image_files, sample["image_file_path"])
with open(image_path, "rb") as f:
result["image"] = {"path": image_path, "bytes": f.read()}
if self.with_text:
result["text_annotation"] = sample["transcription"]
yield idx, result
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