AI4D Baamtu Datamation - Automatic Speech Recognition in WOLOF
$2,000 USD
Can you create an automatic speech recognition model for Wolof for use in public transport?
253 data scientists enrolled, 34 on the leaderboard
TransportationAutomatic Speech RecognitionNLPAudio
Senegal
12 February—23 May
Ends in 13 days

There are 6683 audio files in the train set and 1590 in the test set. You will use these files to train your model and submit your translations.

The goal of this competition is to build an ASR model that will help illiterate people use existing apps to find which bus they can take to reach their destination, without having to know how to read or write.

Files available for download:

  • clips.zip - contains all the audio files
  • Train.csv - contains the audio IDs and the transcription. This is the dataset that you will use to train your model.
  • Test.csv - contains the audio IDs and some information about the audio file. It does not include the transcription. You will use the model you trained on Train.csv to make your translation predictions for the test files.
  • SampleSubmission.csv - shows the submission format for this competition, with the ID column indicating the test audio files. The “transcription” column contains your predictions. The order of the rows does not matter, but the names of the ID must be correct.

Variable Definitions

  • ID: the path to get the audio which is just the name of the audio file without the “.mp3” (i.e the id discussed above)
  • transcription: the transcription of the audio corresponding to the current observation (Available only in the training file)
  • up_votes: Number of users that accepted the recorded audio as being valid for the corresponding text
  • down_votes: Number of users that declined the recorded audio as being valid for the corresponding text
  • age: The Age of the user who did the recording
  • gender: The Gender of the recorder