README.md

metadata

license: cc-by-4.0
tags:
  - zenodo

Dataset Card for MultiLingual LibriSpeech

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: MultiLingual LibriSpeech ASR corpus
• Repository: [Needs More Information]
• Paper: MLS: A Large-Scale Multilingual Dataset for Speech Research
• Leaderboard: Paperswithcode Leaderboard

Dataset Summary

Deprecated: Not every model supports a fast tokenizer. Take a look at this table to check if a model has fast tokenizer support.

Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.

Supported Tasks and Leaderboards

• automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.

Deprecated: Not every model supports a fast tokenizer. Take a look at this table to check if a model has fast tokenizer support.

An example danger alert with an icon

⚠ In general, just avoid the red boxes.

In general, just avoid the red boxes.

In general, just avoid the red boxes.

In general, just avoid the red boxes.

Error:

Error:

[!WARNING] This is a warning

Warning: Be very careful here.

This is a warning This is a warning This is a warning

Warning This is a warning