A Curated Crowdsourced Dataset of Luganda and Swahili Speech for Text-to-Speech Synthesis
Description
This dataset contains curated and preprocessed speech recordings in Luganda and Kiswahili for use in text-to-speech (TTS) research (Katumba et al., 2025). The audio and transcripts were sourced from Mozilla Common Voice (Luganda v12.0 and Kiswahili v15.0) and curated for voice consistency and quality. This dataset is designed for training and evaluating end-to-end TTS systems in low-resource African languages. The data is organized into two folders, Luganda and Kiswahili, each containing: wavs.zip: A ZIP archive of .wav audio files from six selected female speakers per language. All audio files have been silence-trimmed, denoised using a causal DEMUCS model, and filtered using WV-MOS to retain only clips with a predicted MOS ≥ 3.5. metadata.csv: A CSV file with two columns: filename and transcript. Each row corresponds to an audio file in the wavs.zip archive and provides the spoken sentence for that clip. This dataset was used in: Katumba, A., Kagumire, S., Nakatumba-Nabende, J., Quinn, J., & Murindanyi, S. (2025). Building Text-to-Speech Models for Low-Resourced Languages From Crowdsourced Data. Applied AI Letters, 6:e117. https://doi.org/10.1002/ail2.117