Datasets:

edinburghcstr
/

edacc

Tasks:

Automatic Speech Recognition

Audio Classification

Modalities:

Formats:

Size:

ArXiv:

Libraries:

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sanchit-gandhi commited on Feb 22, 2024

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remove langs

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@@ -46,10 +46,6 @@ wide range of first and second-language varieties of English and a linguistic ba
 - 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://groups.inf.ed.ac.uk/edacc/leaderboard.html and ranks models based on their WER scores on the dev and test sets
 - Audio Classification: the model is presented with an audio file and asked to predict the accent or gender of the speaker. The most common evaluation metric is the percentage accuracy.
-## Languages
-The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
 ## How to use
 The `datasets` library allows you to load and pre-process EdAcc in just 2 lines of code. The dataset can be

 - 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://groups.inf.ed.ac.uk/edacc/leaderboard.html and ranks models based on their WER scores on the dev and test sets
 - Audio Classification: the model is presented with an audio file and asked to predict the accent or gender of the speaker. The most common evaluation metric is the percentage accuracy.
 ## How to use
 The `datasets` library allows you to load and pre-process EdAcc in just 2 lines of code. The dataset can be