The Awadhi Speech Dataset is a comprehensive collection of high-quality audio recordings from native Awadhi speakers in Uttar Pradesh and Madhya Pradesh, India. This professionally curated dataset contains 121 hours of authentic Awadhi speech data, meticulously annotated and structured for machine learning applications. Awadhi, an important Indo-Aryan language spoken by millions in northern and central India with rich literary traditions including works of Tulsidas, is captured with its distinctive phonological features and linguistic characteristics 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 Awadhi language models, voice assistants, and conversational AI systems serving large rural and semi-urban populations. The audio files are delivered in MP3/WAV format with consistent quality standards, making them immediately ready for integration into ML pipelines focused on regional Indian language technology and digital inclusion initiatives.
Dataset General Info
| Parameter | Details |
| Size | 121 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 442 MB |
| Number of files | 810 files |
| Gender of speakers | Female: 49%, Male: 51% |
| Age of speakers | 18-30 years: 25%, 31-40 years: 27%, 40-50 years: 20%, 50+ years: 28% |
| Countries | India (Uttar Pradesh, Madhya Pradesh) |
Use Cases
Cultural Heritage Preservation: Cultural organizations and academic institutions can utilize the Awadhi Speech Dataset to develop digital archives, language documentation projects, and interactive cultural heritage applications. Voice-enabled access to classical Awadhi literature including works of Tulsidas and folk traditions helps preserve this important linguistic heritage, while educational applications support language learning and cultural transmission for younger generations in Uttar Pradesh and Madhya Pradesh regions.
Rural Communication and Information Services: Government agencies and development organizations working in Awadhi-speaking regions can leverage this dataset to build voice-based information hotlines, agricultural advisory systems, and public service delivery platforms. Voice interfaces for schemes like MGNREGA, PDS, and health programs improve accessibility for rural populations with limited literacy, while community radio integration and voice messaging systems deliver development information effectively to remote areas.
Regional Entertainment Content: Regional media producers and digital content creators can employ this dataset to develop transcription services for Awadhi folk music, cultural programs, and regional entertainment content. Voice-enabled content discovery platforms help users find Awadhi language entertainment, while automatic subtitling tools support the growing digital content ecosystem serving Awadhi-speaking audiences across northern and central India through local OTT platforms and social media.
FAQ
Q: What does the Awadhi Speech Dataset include?
A: The Awadhi Speech Dataset contains 121 hours of authentic audio recordings from native Awadhi speakers across Uttar Pradesh and Madhya Pradesh, India. The dataset includes 810 files in MP3/WAV format totaling approximately 442 MB, with transcriptions, speaker demographics, regional information, and linguistic annotations designed for machine learning applications.
Q: Why is Awadhi important for language technology?
A: Awadhi is spoken by millions in northern and central India and has rich literary heritage including classical works of Tulsidas. Despite its cultural significance and large speaker population, Awadhi remains underrepresented in speech technology. This dataset addresses this gap, enabling development of voice interfaces that serve Awadhi-speaking populations and support regional language preservation.
Q: How does the dataset capture Awadhi’s literary heritage?
A: Awadhi has distinguished literary tradition as language of Ramcharitmanas and other classical texts. While the dataset focuses on modern spoken Awadhi, linguistic annotations acknowledge this heritage and capture phonological features that connect contemporary speech to classical literary language, supporting both modern applications and cultural preservation efforts.
Q: What regional variations are represented?
A: The dataset captures Awadhi speakers from various regions of Uttar Pradesh and Madhya Pradesh where Awadhi is spoken, representing dialectal variations across this geographic area. With 810 recordings from diverse speakers, it ensures coverage of regional variations within Awadhi-speaking territories of northern and central India.
Q: What linguistic features distinguish Awadhi from Hindi?
A: While related to Hindi, Awadhi has distinctive phonological, grammatical, and lexical features. The dataset includes linguistic annotations marking Awadhi-specific characteristics including unique verb forms, vocabulary items, and sound patterns. This ensures trained models recognize Awadhi as distinct language rather than Hindi dialect, respecting its linguistic identity.
Q: How can this dataset support rural development?
A: Awadhi-speaking regions include large rural populations. Voice interfaces built with this dataset can deliver agricultural information, government services, health advisories, and educational content in Awadhi, improving access for populations with limited literacy. This supports rural development and digital inclusion initiatives in Uttar Pradesh and Madhya Pradesh.
Q: What is the speaker demographic breakdown?
A: The dataset includes 49% female and 51% male speakers with age distribution of 25% aged 18-30, 27% aged 31-40, 20% aged 40-50, and 28% aged 50+. This balanced representation ensures models perform well across different demographic groups in Awadhi-speaking regions.
Q: What applications are suitable for Awadhi speech technology?
A: Applications include voice-based information systems for rural development, agricultural extension services, regional entertainment content transcription, cultural heritage digitization, community radio integration, voice interfaces for government welfare schemes, and educational tools supporting mother-tongue instruction in Awadhi-speaking areas of Uttar Pradesh and Madhya Pradesh.
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.
