The Sinhala Speech Dataset offers an extensive collection of authentic audio recordings from native Sinhala speakers across Sri Lanka. This specialized dataset comprises 156 hours of carefully curated Sinhala speech professionally recorded and annotated for advanced machine learning applications.
Sinhala, spoken by over 17 million people as official language of Sri Lanka with unique Indo-Aryan linguistic heritage, is captured with distinctive phonological features and Sinhala script correspondence essential for developing robust speech recognition systems.
Dataset General Info
| Parameter | Details |
| Size | 156 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 218 MB |
| Number of files | 736 files |
| Gender of speakers | Female: 46%, Male: 54% |
| Age of speakers | 18-30 years: 34%, 31-40 years: 20%, 40-50 years: 16%, 50+ years: 30% |
| Countries | Sri Lanka |
Use Cases
National Digital Infrastructure: Sri Lankan government agencies can utilize the Sinhala Speech Dataset to develop voice-enabled e-government services, national digital platforms, and citizen communication systems in official language. Voice interfaces make government services accessible to Sinhala-speaking majority population, support Digital Sri Lanka initiatives, enable voice-based service delivery across island nation, and facilitate democratic participation in national language. Applications include government portals, healthcare systems, education services, agricultural extension, disaster management for cyclone-prone regions, and tourism platforms serving Sri Lanka’s predominantly Sinhala-speaking population.
Tourism and Cultural Heritage: Sri Lankan tourism industry can leverage this dataset to create voice-guided experiences at UNESCO World Heritage sites, cultural tourism applications, and multilingual hospitality services. Voice technology enhances visitor experiences at Sigiriya, Kandy Temple, and ancient cities while promoting Sinhala language and Buddhist culture, supports vital tourism sector, enables authentic heritage interpretation, and creates immersive cultural experiences. Applications include heritage site audio guides, temple tour systems, tea plantation tours, wildlife safari information, and hospitality platforms serving millions of tourists.
Education and Literacy Programs: Educational institutions can employ this dataset to build Sinhala language learning tools, literacy platforms, and educational content delivery systems. Voice technology supports education in unique Sinhala script, enables literacy programs for rural populations, facilitates distance learning across mountainous terrain, and strengthens Sinhala linguistic competence. Applications include primary education resources, adult literacy tools, Buddhist education platforms, examination systems, and educational content serving Sri Lankan students while preserving distinctive Indo-Aryan linguistic and script heritage.
FAQ
Q: What is included in this dataset?
A: The dataset includes 156 hours of audio recordings with 736 files totaling 218 MB, complete with transcriptions and linguistic annotations.
Q: How diverse is the speaker demographic?
A: Features 46% female and 54% male speakers across age groups: 34% (18-30), 20% (31-40), 16% (40-50), 30% (50+).
How to Use the Speech Dataset
Step 1: Dataset Acquisition – Download the dataset package from the provided link upon purchase.
Step 2: Extract and Organize – Extract to your storage and review the structured folder organization.
Step 3: Environment Setup – Install ML framework dependencies and audio processing libraries.
Step 4: Data Preprocessing – Load audio files and apply preprocessing steps like resampling and feature extraction.
Step 5: Model Training – Split into training/validation/test sets and train your model.
Step 6: Evaluation and Fine-tuning – Evaluate performance and iterate on architecture.
Step 7: Deployment – Export and integrate your trained model into production systems.
For comprehensive documentation, refer to included guides.
