| import os | |
| import pandas as pd | |
| from datasets import Dataset, DatasetDict, Features, Value, Audio | |
| def load_dataset_script(data_dir): | |
| """ | |
| Load dataset script for custom audio-transcription dataset. | |
| :param data_dir: Directory where the data and metadata.csv are stored. | |
| :return: A Hugging Face Dataset object. | |
| """ | |
| # Load metadata.csv | |
| metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv")) | |
| # Create lists for audio files and transcriptions | |
| audio_files = [] | |
| transcriptions = [] | |
| # Iterate through the metadata and populate the lists | |
| for _, row in metadata.iterrows(): | |
| audio_files.append({'path': os.path.join(data_dir, row['file_name'])}) | |
| transcriptions.append(row['transcription']) | |
| # Define features of the dataset | |
| features = Features({ | |
| 'audio': Audio(sampling_rate=16_000), # Adjust the sampling rate as needed | |
| 'sentence': Value('string') | |
| }) | |
| # Create a dataset | |
| dataset = Dataset.from_dict({ | |
| 'audio': audio_files, | |
| 'sentence': transcriptions | |
| }, features=features) | |
| # You can split the dataset here if needed, or return as a single dataset | |
| return DatasetDict({'train': dataset}) | |
| # Example usage | |
| if __name__ == "__main__": | |
| data_directory = "path/to/your/data" | |
| dataset = load_dataset_script(data_directory) | |
| print(dataset) | |