Datasets:
License:
| import csv | |
| import os | |
| import datasets | |
| import pandas as pd | |
| from datasets import Split | |
| # Metadata | |
| _DESCRIPTION = """\ | |
| Mumospee is a continuously growing, comprehensive, multilingual dataset across different modalities. | |
| This is the small version include no more 1000 rows. | |
| """ | |
| _LICENSE = "cc0-1.0" | |
| _LANGUAGES = ["en", "bg", "de", "ar", "fr"] | |
| _TAGS = ["CoVoST", "GigaSpeech", "PeopleSpeech", "Librispeech", "LibriTTS", "Emilia", "MOSEL"] | |
| _SPLITS = ["train", "validation", "test"] | |
| # BuilderConfig class for your dataset | |
| class MumospeeDatasetConfig(datasets.BuilderConfig): | |
| def __init__(self, name, download_audio=None, language=None, tag=None, **kwargs): | |
| super().__init__(**kwargs) | |
| self.name = name | |
| self.language = language | |
| self.tag = tag | |
| self.download_audio = download_audio | |
| class MumospeeDataset(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| # Define the available configurations (could be subsets like split or language) | |
| BUILDER_CONFIGS = [ | |
| MumospeeDatasetConfig( | |
| version=datasets.Version("1.0.0"), | |
| description=_DESCRIPTION, | |
| name="train", | |
| download_audio=None, | |
| language=None, | |
| tag=None | |
| ), | |
| MumospeeDatasetConfig( | |
| version=datasets.Version("1.0.0"), | |
| description=_DESCRIPTION, | |
| name="test", | |
| download_audio=None, | |
| language=None, | |
| tag=None | |
| ), | |
| MumospeeDatasetConfig( | |
| version=datasets.Version("1.0.0"), | |
| description=_DESCRIPTION, | |
| name="validation", | |
| download_audio=None, | |
| language=None, | |
| tag=None | |
| ) | |
| ] | |
| DEFAULT_CONFIG_NAME = "train" | |
| def _info(self): | |
| # Define the features of your dataset | |
| features = datasets.Features({ | |
| "path": datasets.Value("string"), | |
| "url": datasets.Value("string"), | |
| "type": datasets.Value("string"), | |
| "duration": datasets.Value("string"), | |
| "language": datasets.Value("string"), | |
| "transcript": datasets.Value("string"), | |
| "tag": datasets.Value("string"), | |
| "split": datasets.Value("string"), | |
| "license": datasets.Value("string") | |
| }) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| license=_LICENSE, | |
| ) | |
| def _adapt_args(self, arg, accepted_arg): | |
| """ | |
| Adpat the input and make sure it outs as list | |
| and all the elements within the list are accpeted. | |
| """ | |
| if arg: | |
| if isinstance(arg, str): | |
| adapted_arg = [arg] | |
| else: | |
| adapted_arg = arg | |
| for aa in adapted_arg: | |
| if aa not in accepted_arg: | |
| raise ValueError(f"Invalid input: '{aa}'. Accepted values are: {', '.join(accepted_arg)}.") | |
| else: | |
| adapted_arg = accepted_arg | |
| return adapted_arg | |
| def _split_generators(self, dl_manager): | |
| csv_path = dl_manager.download_and_extract("dataset.csv") | |
| if self.config.name==None: | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager} | |
| ), | |
| ] | |
| else: | |
| return [ | |
| datasets.SplitGenerator( | |
| name = getattr(Split, self.config.name.upper()), | |
| gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager} | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, dl_manager): | |
| data = pd.read_csv(filepath) | |
| name = self.config.name | |
| language = self.config.language | |
| tag = self.config.tag | |
| download_audio = self.config.download_audio | |
| all_splits=[] | |
| # If split is None, generate examples for all splits | |
| if name is None: | |
| all_splits = _SPLITS | |
| else: | |
| all_splits = [name] | |
| print(f"Split input is {name}, so get split of {all_splits}.") | |
| # Split base on name split train, test, validation. | |
| data_split = data[data["split"]==name] | |
| if data_split.empty: | |
| print(f"No data found for split='{name}'. Skipping this split.") | |
| return | |
| # Split based on tags. | |
| if tag is not None: | |
| tag_list = self._adapt_args(tag, _TAGS) | |
| data_split = data_split[data_split["tag"].isin(tag_list)] | |
| else: | |
| print(f"No specific tag provided, including all tags in split='{name}', language='{language or 'all'}'.") | |
| # split based on language. | |
| if language is not None: | |
| language_list = self._adapt_args(language, _LANGUAGES) | |
| data_split = data_split[data_split["language"].isin(language_list)] | |
| else: | |
| print(f"No specific language provided, including all languages in split='{name}', tag='{tag or 'all'}'.") | |
| if data_split.empty: | |
| print(f"No data found for split='{name}', language='{language}', tag='{tag}'. Skip this one.") | |
| return | |
| # Generate examples | |
| for i, row in data_split.iterrows(): | |
| # download the url file | |
| if download_audio: | |
| external_url = row["url"] | |
| dl_manager.download(external_url) | |
| yield i, { | |
| "path": row["path"], | |
| #"local_path": row["local_path"], | |
| "url": row["url"], | |
| "type": row["type"], | |
| "duration": float(row["duration"]), | |
| "language": row["language"], | |
| "transcript": row["transcript"], | |
| "tag": row["tag"], | |
| "split": row["split"], | |
| "license": row["license"] | |
| } | |