The Telugu Speech Dataset is a comprehensive collection of high-quality audio recordings featuring native Telugu speakers from Andhra Pradesh and Telangana, India. This professionally curated dataset contains 141 hours of authentic Telugu speech data, meticulously annotated and structured for machine learning applications. As one of the most widely spoken Dravidian languages with over 80 million speakers, Telugu is captured with its distinctive phonological features and rich linguistic heritage essential for developing accurate speech recognition systems.
With balanced representation across gender and age groups, the dataset provides researchers and developers with a robust foundation for building Telugu language models, voice assistants, and conversational AI systems serving two major Indian states. The audio files are delivered in MP3/WAV format with consistent quality standards, making them immediately ready for integration into your ML pipeline for regional language technology development.
Dataset General Info
| Parameter | Details |
| Size | 141 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 397 MB |
| Number of files | 660 files |
| Gender of speakers | Female: 45%, Male: 55% |
| Age of speakers | 18-30 years: 27%, 31-40 years: 25%, 40-50 years: 21%, 50+ years: 27% |
| Countries | India (Andhra Pradesh, Telangana) |
Use Cases
Regional E-Governance and Digital Services: State government agencies in Andhra Pradesh and Telangana can utilize the Telugu Speech Dataset to build voice-enabled citizen service platforms, information systems, and digital governance tools. Voice interfaces for services like e-Seva centers, ration card systems, and welfare scheme applications improve accessibility for Telugu-speaking populations, particularly benefiting rural citizens and those with limited digital literacy in implementing state-level digital transformation initiatives.
Entertainment and Media Industry: The thriving Telugu film industry, second largest in India, can leverage this dataset to develop automatic subtitle generation systems, voice dubbing tools, and content discovery platforms for regional entertainment. OTT platforms and streaming services benefit from Telugu speech recognition for content indexing and recommendation, while podcast transcription services support the growing digital content ecosystem serving 80 million Telugu speakers across two states and diaspora communities.
Financial Technology and Banking: Banks, fintech companies, and microfinance institutions operating in Andhra Pradesh and Telangana can employ this dataset to create voice-authenticated banking applications, mobile payment systems, and financial advisory services in Telugu. Voice-based transaction systems and interactive voice response for customer support make financial services more accessible to Telugu-speaking customers, supporting financial inclusion efforts in both urban tech hubs like Hyderabad and rural agricultural regions.
FAQ
Q: What is included in the Telugu Speech Dataset?
A: The Telugu Speech Dataset includes 141 hours of audio recordings from native Telugu speakers in Andhra Pradesh and Telangana. The dataset contains 660 files in MP3/WAV format, totaling approximately 397 MB. Each recording is professionally annotated with transcriptions in Telugu script, speaker metadata including age, gender, and regional origin, along with quality markers to ensure optimal performance for machine learning applications targeting Telugu-speaking populations in these two major Indian states.
Q: How does the dataset handle Telugu’s unique script and phonology?
A: Telugu uses a distinct Brahmic script and features unique phonological characteristics including retroflex consonants and specific vowel combinations. The dataset includes transcriptions in Telugu script with proper orthography, detailed phonetic annotations marking distinctive sounds, and linguistic metadata. This comprehensive annotation ensures trained models can accurately recognize Telugu speech patterns and correctly map audio to written Telugu text.
Q: What regional variations are captured in the dataset?
A: The dataset captures Telugu speakers from both Andhra Pradesh and Telangana, representing various regional accents including Coastal Andhra, Rayalaseema, and Telangana dialects. With 660 files from diverse speakers across both states, the dataset ensures models can understand Telugu speakers regardless of their specific regional background, important for applications serving the entire Telugu-speaking population.
Q: Why is Telugu speech technology important for India’s tech sector?
A: Telugu is the third most spoken language in India with over 80 million speakers, and Hyderabad is a major technology hub. Telugu speech technology enables voice interfaces for regional users, supports digital India initiatives, and creates opportunities for tech companies to serve large Telugu-speaking markets. The dataset addresses the underrepresentation of regional Indian languages in AI technology.
Q: What machine learning tasks is this dataset suitable for?
A: The Telugu Speech Dataset is designed for automatic speech recognition, speaker identification, voice biometrics, sentiment analysis, natural language understanding, acoustic modeling, and conversational AI development. The professionally annotated transcriptions in Telugu script and diverse speaker pool make it ideal for training supervised learning models for regional language technology applications.
Q: How diverse is the speaker demographic?
A: The dataset features balanced representation with 45% female and 55% male speakers. Age distribution includes 27% speakers aged 18-30, 25% aged 31-40, 21% aged 40-50, and 27% aged 50+, ensuring models perform well across different demographic groups in Telugu-speaking regions.
Q: Is this dataset suitable for commercial applications?
A: Yes, the Telugu Speech Dataset is licensed for both research and commercial use. It can be integrated into commercial products including voice assistants, customer service automation for Telugu markets, mobile applications, regional e-governance solutions, and other business applications serving Andhra Pradesh and Telangana markets.
Q: What documentation and support are provided?
A: Comprehensive documentation includes dataset structure guides, Telugu script handling instructions, code examples for popular ML frameworks, preprocessing scripts, and best practices for training Telugu ASR models. Technical support covers integration assistance, linguistic annotation questions, and optimization strategies for Telugu speech recognition systems.
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.
