djmix.py

# 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)