# 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" _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 ["train", "test_freq", "test_rare", "valid_freq", "valid_rare"]] 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 = ("train", "test_freq", "test_rare", "valid_freq", "valid_rare") 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, archiv_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=datasets.Split.TEST, 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=datasets.Split.TEST, 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=datasets.Split.VALIDATION, 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=datasets.Split.VALIDATION, 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=datasets.Split.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 # data = json.loads(line.strip()) # meta_csv = csv.DictReader(csvfile) # for line in meta_csv: 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} } def _flatten_nested_dict(nested_dict): return { key: [inner_list_element for inner_list in value_to_lists.values() for inner_list_element in inner_list] for key, value_to_lists in nested_dict.items() }