The Yoruba Speech Dataset is a comprehensive collection of high-quality audio recordings featuring native Yoruba speakers from Nigeria, Benin, and Togo. This professionally curated dataset contains 107 hours of authentic Yoruba speech data, meticulously annotated and structured for machine learning applications.
Yoruba, a Niger-Congo language spoken by over 40 million people with rich cultural heritage and distinctive tonal system, is captured with its phonological features essential for developing accurate speech recognition systems. With balanced representation across gender and age groups, the dataset provides researchers and developers with essential resources for building Yoruba language models, voice assistants, and conversational AI systems serving one of West Africa’s major linguistic and cultural communities. The audio files are delivered in MP3/WAV format with consistent quality standards, making them immediately ready for integration into ML pipelines focused on African languages and West African regional development.
Dataset General Info
| Parameter | Details |
| Size | 107 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 311 MB |
| Number of files | 537 files |
| Gender of speakers | Female: 52%, Male: 48% |
| Age of speakers | 18-30 years: 31%, 31-40 years: 27%, 40-50 years: 24%, 50+ years: 18% |
| Countries | Nigeria, Benin, Togo |
Use Cases
Cultural Heritage and Identity: Cultural organizations and academic institutions can utilize the Yoruba Speech Dataset to develop digital archives of Yoruba oral literature, traditional music documentation, and cultural heritage platforms. Voice technology preserves Yoruba rich cultural traditions including oriki praise poetry and traditional knowledge systems, supports intergenerational cultural transmission, and maintains Yoruba identity across Nigeria, Benin, and Togo through accessible digital cultural preservation.
Education and Literacy Programs: Educational institutions can leverage this dataset to create Yoruba language learning applications, literacy tools, and educational content delivery systems. Voice-based learning supports education in Yoruba medium schools across southwestern Nigeria, enables mother-tongue education, and makes digital learning resources accessible to Yoruba-speaking students, supporting literacy and educational development through native language technology.
Regional Commerce and Media: Businesses and media companies across Yorubaland can employ this dataset to develop voice-enabled customer service platforms, entertainment content transcription, and regional commerce applications. Voice interfaces in Yoruba support local businesses, enable Yoruba media industry growth through transcription and content discovery tools, and facilitate commercial activities serving over 40 million Yoruba speakers across West African markets.
FAQ
Q: What is included in the Yoruba Speech Dataset?
A: The Yoruba Speech Dataset includes 107 hours of audio recordings from native Yoruba speakers across Nigeria, Benin, and Togo. Contains 537 files in MP3/WAV format totaling approximately 311 MB, with transcriptions, speaker demographics, and tonal annotations.
Q: How does the dataset handle Yoruba’s tonal system?
A: Yoruba is tonal language where pitch distinguishes word meanings. The dataset includes tonal annotations marking high, mid, and low tones, essential for accurate speech recognition. This ensures trained models can correctly interpret Yoruba speech with characteristic tonal patterns.
Q: Why is Yoruba culturally significant?
A: Yoruba has exceptionally rich cultural heritage including oral literature, traditional religion, and artistic traditions. Voice technology preserves this heritage, supports cultural transmission, and maintains Yoruba identity across Nigeria, Benin, and Togo.
Q: Can this dataset support cross-border applications?
A: Yes, Yoruba is spoken across three countries. The dataset captures this diversity with 537 recordings from different regions, enabling applications serving entire Yoruba-speaking population regardless of national boundaries.
Q: What is the demographic breakdown?
A: Dataset features 52% female and 48% male speakers with ages: 31% (18-30), 27% (31-40), 24% (40-50), 18% (50+).
Q: What applications benefit from Yoruba technology?
A: Applications include cultural heritage platforms, educational technology for Yoruba schools, regional commerce tools, media transcription, customer service automation, and cultural preservation systems serving over 40 million speakers.
Q: How does this support Nigerian linguistic diversity?
A: Nigeria has over 500 languages. This dataset promotes linguistic inclusion by enabling technology for Yoruba, one of Nigeria’s three major languages, ensuring technological development benefits all linguistic communities.
Q: What technical support is provided?
A: Comprehensive documentation includes tonal annotation guides, integration instructions, preprocessing pipelines, and best practices for Yoruba speech recognition system development.
How to Use the Speech Dataset
Step 1: Dataset Acquisition
Download the dataset package from the provided link. Upon purchase, you will receive access credentials and download instructions via email. The dataset is delivered as a compressed archive file containing all audio files, transcriptions, and metadata.
Step 2: Extract and Organize
Extract the downloaded archive to your local storage or cloud environment. The dataset follows a structured folder organization with separate directories for audio files, transcriptions, metadata, and documentation. Review the README file for detailed information about file structure and naming conventions.
Step 3: Environment Setup
Install required dependencies for your chosen ML framework such as TensorFlow, PyTorch, Kaldi, or others. Ensure you have necessary audio processing libraries installed including librosa, soundfile, pydub, and scipy. Set up your Python environment with the provided requirements.txt file for seamless integration.
Step 4: Data Preprocessing
Load the audio files using the provided sample scripts. Apply necessary preprocessing steps such as resampling, normalization, and feature extraction including MFCCs, spectrograms, or mel-frequency features. Use the included metadata to filter and organize data based on speaker demographics, recording quality, or other criteria relevant to your application.
Step 5: Model Training
Split the dataset into training, validation, and test sets using the provided speaker-independent split recommendations to avoid data leakage. Configure your model architecture for the specific task whether speech recognition, speaker identification, or other applications. Train your model using the transcriptions and audio pairs, monitoring performance on the validation set.
Step 6: Evaluation and Fine-tuning
Evaluate model performance on the test set using standard metrics such as Word Error Rate for speech recognition or accuracy for classification tasks. Analyze errors and iterate on model architecture, hyperparameters, or preprocessing steps. Use the diverse speaker demographics to assess model fairness and performance across different groups.
Step 7: Deployment
Once satisfactory performance is achieved, export your trained model for deployment. Integrate the model into your application or service infrastructure. Continue monitoring real-world performance and use the dataset for ongoing model updates and improvements as needed.
For detailed code examples, integration guides, and troubleshooting tips, refer to the comprehensive documentation included with the dataset.
