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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.
import csv
import os
import json
import datasets
_CITATION = """\
"""
_DESCRIPTION = """\
Multidialog is the first large-sccale multimodal (i.e. audio, visual, and text) dialogue corpus, consisting of approximately 400 hours of audio-visual conversation strems between 6 pairs of conversation partners.
It contina
"""
_HOMEPAGE = "https://multidialog.github.io/"
_LICENSE = "Apache License 2.0"
_SUBSETS = ("train", "test_freq", "test_rare", "valid_freq", "valid_rare")
_BASE_DATA_URL = "https://huggingface.co/datasets/IVLLab/MultiDialog/resolve/main/"
_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "data/{subset}/{subset}_chunks_{archive_id:04}.tar.gz"
_META_URL = _BASE_DATA_URL + "metadata/{subset}/{subset}_metadata_{archive_id:04}.jsonl"
logger = datasets.utils.logging.get_logger(__name__)
class MultidialogConfig(datasets.BuilderConfig):
"""BuilderConfig for Multidialog."""
def __init__(self, name, *args, **kwargs):
"""BuilderConfig for Multidialog
"""
super().__init__(name=name, *args, **kwargs)
self.subsets_to_download = (name,)
class Multidialog(datasets.GeneratorBasedBuilder):
"""
"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [MultidialogConfig(name=subset) for subset in _SUBSETS]
DEFAULT_WRITER_BATCH_SIZE = 128
def _info(self):
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"conv_id": datasets.Value("string"),
"utterance_id": datasets.Value("float32"),
"audio": datasets.Audio(sampling_rate=16_000),
"from": datasets.Value("string"),
"value": datasets.Value("string"),
"emotion": datasets.Value("string"),
"original_full_path": datasets.Value("string"), # relative path to full audio in original data dirs
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
splits = (self.config.name,)
n_archives = {
"train" : [15, 4],
"test_freq": [1, 1],
"test_rare": [1, 1],
"valid_freq": [1, 1],
"valid_rare": [1, 1],
}
# 2. prepare sharded archives with audio files
audio_archives_urls = {
split: [
_AUDIO_ARCHIVE_URL.format(subset=split, archive_id=i)
for i in range(n_archives[split][0])
]
for split in splits
}
audio_archives_paths = dl_manager.download(audio_archives_urls)
# flatten archives paths from
# {"train": {"xs": [path1, path2,], "s": [path3], "m": [path5, path5]}, "dev": {"dev": [path6,...]}, "test": {"test": [...]}}
# to {"train": [path1, path2, path3, path4, path5], "dev": [path6, ...], "test": [...]}
# audio_archives_paths = _flatten_nested_dict(audio_archives_paths)
local_audio_archives_paths = dl_manager.extract(audio_archives_paths) if not dl_manager.is_streaming \
else None
# 3. prepare sharded metadata csv files
meta_urls = {
split: [
_META_URL.format(subset=split, archive_id=i)
for i in range(n_archives[split][1])
]
for split in splits
}
meta_paths = dl_manager.download_and_extract(meta_urls)
# meta_paths = _flatten_nested_dict(meta_paths)
if self.config.name == "test_freq":
return [
datasets.SplitGenerator(
name="test_freq",
gen_kwargs={
"audio_archives_iterators": [
dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["test_freq"]
],
"local_audio_archives_paths": local_audio_archives_paths[
"test_freq"] if local_audio_archives_paths else None,
"meta_paths": meta_paths["test_freq"]
},
),
]
if self.config.name == "test_rare":
return [
datasets.SplitGenerator(
name="test_rare",
gen_kwargs={
"audio_archives_iterators": [
dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["test_rare"]
],
"local_audio_archives_paths": local_audio_archives_paths[
"test_rare"] if local_audio_archives_paths else None,
"meta_paths": meta_paths["test_rare"]
},
),
]
if self.config.name == "valid_freq":
return [
datasets.SplitGenerator(
name="valid_freq",
gen_kwargs={
"audio_archives_iterators": [
dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["valid_freq"]
],
"local_audio_archives_paths": local_audio_archives_paths[
"valid_freq"] if local_audio_archives_paths else None,
"meta_paths": meta_paths["valid_freq"]
},
),
]
if self.config.name == "valid_rare":
return [
datasets.SplitGenerator(
name="valid_rare",
gen_kwargs={
"audio_archives_iterators": [
dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["valid_rare"]
],
"local_audio_archives_paths": local_audio_archives_paths[
"valid_rare"] if local_audio_archives_paths else None,
"meta_paths": meta_paths["valid_rare"]
},
),
]
if self.config.name == "train":
return [
datasets.SplitGenerator(
name="train",
gen_kwargs={
"audio_archives_iterators": [
dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["train"]
],
"local_audio_archives_paths": local_audio_archives_paths[
"train"] if local_audio_archives_paths else None,
"meta_paths": meta_paths["train"]
},
),
]
def _generate_examples(self, audio_archives_iterators, local_audio_archives_paths, meta_paths):
assert len(audio_archives_iterators) == len(meta_paths)
if local_audio_archives_paths:
assert len(audio_archives_iterators) == len(local_audio_archives_paths)
for i, (meta_path, audio_archive_iterator) in enumerate(zip(meta_paths, audio_archives_iterators)):
meta_dict = dict()
with open(meta_path) as jsonl_file:
for line in jsonl_file:
data = json.loads(line.strip())
meta_dict[data["file_name"]] = data
for audio_path_in_archive, audio_file in audio_archive_iterator:
# `audio_path_in_archive` is like "dev_chunks_0000/YOU1000000029_S0000095.wav"
audio_filename = os.path.split(audio_path_in_archive)[1]
audio_id = audio_filename.split(".wav")[0]
audio_meta = meta_dict[audio_path_in_archive]
audio_meta["conv_id"] = audio_meta.pop("conv_id")
audio_meta["utterance_id"] = audio_meta.pop("utterance_id")
audio_meta["from"] = audio_meta.pop("from")
audio_meta["value"] = audio_meta.pop("value")
audio_meta["emotion"] = audio_meta.pop("emotion")
audio_meta["original_full_path"] = audio_meta.pop("audpath")
path = os.path.join(local_audio_archives_paths[i], audio_path_in_archive) if local_audio_archives_paths \
else audio_path_in_archive
yield audio_id, {
"audio": {"path": path , "bytes": audio_file.read()},
**{feature: value for feature, value in audio_meta.items() if feature in self.info.features}
} |