Datasets:
License:
| import csv | |
| import os | |
| import datasets | |
| import pandas as pd | |
| # Metadata | |
| _DESCRIPTION = """\ | |
| Mumospee is a continuously growing, comprehensive, multilingual dataset across different modalities. | |
| This is the small version include no more 1000 rows. | |
| """ | |
| _LICENSE = "Creative Commons Attribution 4.0 International" | |
| # Defining categories or tags for filtering | |
| _LANGUAGES = ["en", "bg", "de", "es", "it", "ar"] # Example languages | |
| _TAGS = ["CoVoST", "GigaSpeech", "peoples_speech", "Librispeech", "LibriTTS", "Emilia", "MOSEL"] | |
| _SPLITS = ["train", "validation", "test"] | |
| # BuilderConfig class for your dataset | |
| class MumospeeDatasetConfig(datasets.BuilderConfig): | |
| def __init__(self, name, *args, **kwargs): | |
| super().__init__(name=name, *args, **kwargs) | |
| self.language = kwargs.get("language", None) # Add language as a key in the config | |
| self.tag = kwargs.get("tag", None) # Add tag as a key in the config | |
| class MumospeeDataset(datasets.GeneratorBasedBuilder): | |
| """Your custom dataset for Hugging Face based on the CSV metadata and audio URLs.""" | |
| VERSION = datasets.Version("1.0.0") | |
| # Define the available configurations (could be subsets like split or language) | |
| BUILDER_CONFIGS = [ | |
| MumospeeDatasetConfig( | |
| name="default", | |
| version=datasets.Version("1.0.0"), | |
| description=_DESCRIPTION) | |
| ] | |
| DEFAULT_CONFIG_NAME = "default" | |
| 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("float32"), | |
| "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 _split_generators(self, dl_manager): | |
| """Split the dataset into train, validation, and test.""" | |
| # Your dataset might have specific splits like "train", "dev", "test" | |
| splits = ["train", "validation", "test"] | |
| csv_path = dl_manager.download_and_extract("dataset.csv") | |
| # Define the splits and pass the language and tag filters to _generate_examples | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"csv_path": csv_path, "split": "train", "language": _LANGUAGES, "tag": _TAGS} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"csv_path": csv_path, "split": "validation", "language": _LANGUAGES, "tag": _TAGS} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={"csv_path": csv_path, "split": "test", "language": _LANGUAGES, "tag": _TAGS} | |
| ), | |
| ] | |
| def _generate_examples(self, csv_file, split, language, tag): | |
| """Generate examples from the CSV data and audio URLs.""" | |
| # Use dl_manager to get the CSV file from the Hugging Face repo | |
| # csv_file = dl_manager.download_and_extract("metadata.csv") | |
| # Read the CSV metadata | |
| data = pd.read_csv(csv_file) | |
| # Filter by primary split | |
| data_split = data[data["split"] == split] | |
| if language: | |
| data_split = data_split[data_split["language"] == language] | |
| if tag: | |
| data_split = data_split[data_split["tag"] == tag] | |
| for i, row in data_split.iterrows(): | |
| # Construct the full audio path or URL | |
| audio_url = row["url"] | |
| # Prepare the data entry for this example | |
| yield i, { | |
| "path": row["path"], | |
| "audio": audio_url, | |
| "duration": float(row["duration"]), | |
| "language": row["language"], | |
| "transcript": row["transcript"], | |
| "tag": row["tag"], | |
| "split": row["split"], | |
| "license": row["license"] | |
| } | |
| # with open(csv_file, mode='r', encoding='utf-8') as f: | |
| # reader = csv.DictReader(f) | |
| # for row in reader: | |
| # # Filter by tag or language here if necessary | |
| # if row["language"] not in _LANGUAGES: | |
| # continue # Skip entries with unsupported languages | |
| # if row["tag"] not in _TAGS: | |
| # continue # Skip entries with unsupported tags | |
| # # Construct the full audio path or URL | |
| # audio_url = row["url"] | |
| # # Prepare the data entry for this example | |
| # yield row["path"], { | |
| # "audio": audio_url, | |
| # "duration": float(row["duration"]), | |
| # "language": row["language"], | |
| # "transcript": row["transcript"], | |
| # "tag": row["tag"], | |
| # "split": row["split"], | |
| # "license": row["license"] | |
| # } | |
| def _download_audio(self, audio_url): | |
| """Download audio from a URL if needed (you could also implement streaming).""" | |
| # This is an example function for downloading audio if it's needed | |
| # You can integrate this within your data processing pipeline if required | |
| pass | |