# 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. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import csv import json 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 description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn"t host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", } # _METADATA_URL = "https://huggingface.co/datasets/taejunkim/djmix/resolve/b961f21ab1a22b12e3154229bb40de9c950f8b26/metadata.json" _METADATA_URL = "https://huggingface.co/datasets/taejunkim/djmix/resolve/b48eccb4738ba09b96ea21d0bdb33f29e7be3b3c/metadata.json.gz" # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class DJMixDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don"t want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset("my_dataset", "first_domain") # data = datasets.load_dataset("my_dataset", "second_domain") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="mixes", version=VERSION, description="This part of my dataset covers a first domain"), datasets.BuilderConfig(name="tracks", version=VERSION, description="This part of my dataset covers a second domain"), ] DEFAULT_CONFIG_NAME = "first_domain" # It"s not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "sentence": datasets.Value("string"), "option1": datasets.Value("string"), "answer": datasets.Value("string") # These are the features of your dataset like images, labels ... } ) else: # This is an example to show how to have different features for "first_domain" and "second_domain" features = datasets.Features( { "sentence": datasets.Value("string"), "option2": datasets.Value("string"), "second_domain_answer": datasets.Value("string") # These are the features of your dataset like images, labels ... } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there"s a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They"ll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive metadata_path = dl_manager.download(_METADATA_URL) metadata_path = dl_manager.extract(metadata_path) dl_manager.iter_archive() with open(metadata_path) as f: metadata = json.load(f) download_mixes(metadata, self.cache_dir) audio_urls = [] for mix in metadata: audio_urls.append(mix["audio_url"]) dl_manager.download_custom(mix["audio_url"], youtube_dl_download) print("HI!!!!!!!!!!!!!!!!!!!!!!") urls = _URLS[self.config.name] dl_manager.downloaded_paths dl_manager.download_custom("haha.mp3", custom) data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "train.jsonl"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "dev.jsonl"), "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "test.jsonl"), "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "first_domain": # Yields examples as (key, example) tuples yield key, { "sentence": data["sentence"], "option1": data["option1"], "answer": "" if split == "test" else data["answer"], } else: yield key, { "sentence": data["sentence"], "option2": data["option2"], "second_domain_answer": "" if split == "test" else data["second_domain_answer"], } def youtube_dl_download(src_url, dst_path): from yt_dlp import YoutubeDL params = { "format": "bestaudio", "outtmpl": f"{dst_path}.%(ext)s", "postprocessors": [{ # Extract audio using ffmpeg "key": "FFmpegExtractAudio", "preferredcodec": "mp3", }] } with YoutubeDL(params) as ydl: ydl.download(src_url) os.rename(f"{dst_path}.mp3", dst_path) return dst_path def download_mixes(metadata, cache_dir): from yt_dlp.utils import DownloadError for mix in metadata: audio_path = os.path.join(cache_dir, mix["id"] + ".mp3") # TODO: try/except try: ret = download_audio(mix["audio_url"], audio_path) except DownloadError as e: print(e) print() def download_audio(url, path): if os.path.isfile(path): # TODO: silence? print(f'{path} already exists. Skip downloading.') return from yt_dlp import YoutubeDL params = { 'format': 'bestaudio', 'outtmpl': path, 'postprocessors': [{ # Extract audio using ffmpeg 'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', }] } with YoutubeDL(params) as ydl: os.makedirs(os.path.dirname(path), exist_ok=True) return ydl.download(url) if __name__ == "__main__": builder = DJMixDataset( config_name="mixes", data_dir="/data/djmix" ) builder.download_and_prepare() dir_path = os.path.dirname(os.path.realpath(__file__)) datasets.load_dataset(dir_path)