meetween/mumospee_emilia

Dataset Summary

This dataset is a modified version of the Emilia corpus, converted into parquet format to facilitate optimized I/O operations in high-performance and distributed computing environments. The Emilia dataset is a comprehensive, multilingual dataset with the following features:

• containing over 101k hours of speech data;
• covering six different languages: English (En), Chinese (Zh), German (De), French (Fr), Japanese (Ja), and Korean (Ko);
• containing diverse speech data with various speaking styles from diverse video platforms and podcasts on the Internet, covering various content genres such as talk shows, interviews, debates, sports commentary, and audiobooks.

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Source Data

• Original Dataset: Emilia
• License: This derived dataset is shared under the same license, with modifications only to format for efficiency.

Modifications

• Data Format: Converted to parquet format to enhance I/O performance for distributed training, reducing latency during data loading and retrieval.
• Efficiency Optimization: Restructured for reduced storage footprint and faster I/O on high-performance clusters by leveraging parquet’s efficient compression and columnar storage.

Dataset Structure

• File Format: Parquet files.
• Languages: 6 languages retained from the original Emilia dataset.
• Audio Sampling Rate: Matches original dataset specifications for high-fidelity speech data.
• Multilingual Speaker Representation: Over 101K hours across multiple languages, preserving Emilia’s speaker diversity and multilingual alignment.

Usage

This dataset is ideal for use in large-scale multilingual speech-to-text translation tasks, especially in distributed and high-performance computing environments. The parquet format enhances usability by minimizing I/O overhead, making it well-suited for high-throughput training.

Attribution

This dataset is based on the original Emilia dataset, with modifications for I/O optimization by converting to parquet format. Please cite the original CoVoST dataset in any publications or projects using this dataset.