The Bhojpuri Speech Dataset is a meticulously curated collection of high-quality audio recordings from native Bhojpuri speakers across India, Nepal, Mauritius, Fiji, Guyana, Trinidad and Tobago, and Suriname. This comprehensive linguistic resource features 81 hours of authentic Bhojpuri speech data, professionally annotated and structured for advanced machine learning applications.
Bhojpuri, an Indo-Aryan language spoken by over 50 million people with significant presence in South Asian and Caribbean diaspora communities, is captured with its distinctive phonological features and rich cultural context crucial for developing accurate speech recognition technologies.
The dataset includes diverse representation across age demographics and balanced gender distribution, ensuring thorough coverage of Bhojpuri linguistic variations and regional dialects spanning three continents. Formatted in MP3/WAV with superior audio quality standards, this dataset empowers researchers and developers working on voice technology, AI training, speech-to-text systems, and computational linguistics projects focused on underrepresented Indo-Aryan languages and diaspora language preservation.
Dataset General Info
| Parameter | Details |
| Size | 81 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 195 MB |
| Number of files | 721 files |
| Gender of speakers | Female: 48%, Male: 52% |
| Age of speakers | 18-30 years: 34%, 31-40 years: 27%, 40-50 years: 21%, 50+ years: 18% |
| Countries | India (Bihar, Uttar Pradesh, Jharkhand), Nepal, Mauritius, Fiji, Guyana, Trinidad and Tobago, Suriname |
Use Cases
Diaspora Community Services: Organizations serving Bhojpuri-speaking diaspora communities in Mauritius, Fiji, Guyana, Trinidad, and Suriname can utilize this dataset to develop voice-enabled community platforms, cultural preservation applications, and heritage language learning tools. These services help maintain linguistic and cultural connections across generations in Caribbean and Pacific diaspora communities, supporting identity preservation and community cohesion among descendants of Indian indentured laborers who carried Bhojpuri language traditions globally.
Rural Development and Agricultural Extension: Government agencies and NGOs working in Bihar, eastern Uttar Pradesh, and Jharkhand can leverage this dataset to create voice-based information systems for rural development programs, agricultural advisory services, and welfare scheme delivery. Voice interfaces make government services accessible to populations with lower literacy rates, while agricultural guidance systems in Bhojpuri support farming communities with timely information on crops, weather, and market prices in underserved regions.
Entertainment and Regional Media: Growing Bhojpuri entertainment industry can employ this dataset to develop content recommendation systems, automatic transcription tools for Bhojpuri films and music, and voice-enabled streaming platforms. Regional OTT services benefit from speech recognition for content discovery, while podcast and radio transcription supports the vibrant Bhojpuri cultural production serving millions across South Asia and diaspora communities maintaining connections through regional media content.
FAQ
Q: What is included in the Bhojpuri Speech Dataset?
A: The Bhojpuri Speech Dataset contains 81 hours of high-quality audio recordings from native Bhojpuri speakers across India (Bihar, Uttar Pradesh, Jharkhand), Nepal, Mauritius, Fiji, Guyana, Trinidad and Tobago, and Suriname. The dataset includes 721 files in MP3/WAV format totaling approximately 195 MB, with transcriptions, speaker demographics, geographic information, and linguistic annotations.
Q: Why is Bhojpuri speech technology important?
A: Bhojpuri is spoken by over 50 million people but remains significantly underrepresented in language technology despite being one of India’s most widely spoken languages. This dataset addresses digital exclusion by enabling speech technology for large populations in Bihar, eastern UP, and global diaspora communities descended from Indian indentured laborers, supporting digital inclusion and cultural preservation.
Q: How does the dataset represent diaspora communities?
A: The dataset includes speakers from Caribbean and Pacific Bhojpuri-speaking communities in Mauritius, Fiji, Guyana, Trinidad and Tobago, and Suriname. These diaspora varieties preserve linguistic features from historical Bhojpuri while developing unique characteristics, providing comprehensive coverage essential for applications serving both South Asian and diaspora Bhojpuri speakers globally.
Q: What linguistic characteristics of Bhojpuri are annotated?
A: Bhojpuri is an Eastern Indo-Aryan language with distinctive phonological and grammatical features differing from standard Hindi. The dataset includes linguistic annotations marking Bhojpuri-specific sounds, verb conjugation patterns, and vocabulary. Detailed transcriptions ensure models accurately recognize Bhojpuri rather than treating it as Hindi dialect, respecting its distinct linguistic identity.
Q: Can this dataset support language preservation efforts?
A: Yes, the Bhojpuri Speech Dataset serves both technology development and language preservation purposes. By documenting diverse speakers across South Asia and diaspora communities in structured digital format, it contributes to maintaining Bhojpuri linguistic heritage while enabling modern technology to support language vitality and intergenerational transmission.
Q: What is the demographic distribution?
A: The dataset features 48% female and 52% male speakers with age distribution of 34% aged 18-30, 27% aged 31-40, 21% aged 40-50, and 18% aged 50+. Geographic diversity spans seven countries across Asia, Caribbean, and South America, ensuring comprehensive representation.
Q: What applications can benefit from this dataset?
A: Applications include voice interfaces for rural development programs in Bihar and eastern UP, agricultural advisory systems, diaspora community platforms for heritage language learning, entertainment and media services for growing Bhojpuri film industry, voice-enabled government services, and communication tools for migrant worker populations.
Q: How does this dataset address digital divide issues?
A: Bhojpuri speakers often face digital exclusion due to lack of language technology support. This dataset enables development of voice interfaces that make digital services accessible regardless of literacy levels, supporting rural populations, migrant workers, and diaspora communities in accessing education, healthcare, government services, and economic opportunities through speech technology.
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.
