ogunlao/yoruba_speech_project

This repo contains 3 hours of audio speech recordings in Yoruba language collected for research purposes. # Yoruba Speech Dataset for Low-Resource Speech Tasks This repo contains over 3 hours of recorded yoruba text from 1 native yoruba male speaker. ## Downloading Go to your terminal and enter; ```git git clone github.com ``` This adds a folder called "yoruba_speech_project" which contain the files to your local directory. ## Statistics - 1079 recordings of a maximum of 20 word length per recording - 3.06 total recording hours - 22 folders each containing 49 recorded audio with their respective metadata. Recordings are divided into folders for easy access. ## Data Collection Text dataset used for recording was collected from Niger Volta LTI. It had a smooth narrative which made the text interesting to read. Recording was done using the Lig-Aikuma Android app. It is an easy-to-use app with good interface for recording and elicitation. It offers 6 modes of usage; - Recording - Respeaking - Translating - Elicitation - Check - Share The elicitation mode was used in data collection where a displayed text is read carefully by the speaker during recording. ## Data Preprocessing Preprocessing involved; - validating data for errors and removing corrupt files - merging folders - splitting data into train, val and test samples Two main dataset directory with subdirectories; - `recordings` contain the unprocessed recorded files and metadata - `data/records` contain split data Raw speech dataset split into; - 1 hour of speech data (split into train and val) - 1 hour of speech as test set - Additional extra 1 hour of speech as extra data - each contained in `data/records/train`, `data/records/val`, `data/records/extra` respectively All preprocessing can be accessed through the notebook `yoruba_speech_preprocessing` and script `yor_processor.py` Note that a python installation is required with some dependencies; - numpy - wave ## Application The dataset can be used majorly for low-resource speech model experiments or …