The Tosk Albanian Speech Dataset is a premium collection of authentic speech recordings representing the southern dialect of Albanian, which forms the basis of modern standard Albanian. This comprehensive dataset captures speakers from southern Albania, Greece, and the historic Arbëreshë communities in Italy, providing unique linguistic coverage of Tosk Albanian across its geographical range.

Professionally recorded and meticulously annotated, this dataset is invaluable for developing speech recognition systems, linguistic research, and AI applications targeting Tosk Albanian speakers.

Available in MP3 and WAV formats, the dataset features diverse speakers across multiple age groups and genders, ensuring robust representation for machine learning applications. With high audio quality, detailed transcriptions, and demographic diversity, this dataset provides researchers and developers with essential resources for building sophisticated language technologies that serve Tosk Albanian-speaking communities across the Mediterranean region.

Tosk Albanian Dataset General Info

Field	Details
Size	167 hours
Format	MP3/WAV
Tasks	Speech recognition, AI training, linguistic analysis, acoustic modeling, standard Albanian ASR, dialectology research
File Size	365 MB
Number of Files	792 files
Gender of Speakers	Male: 53%, Female: 47%
Age of Speakers	18-30 years old: 36%, 31-40 years old: 28%, 41-50 years old: 21%, 50+ years old: 15%
Countries	Albania (southern regions), Greece, Italy (Arbëreshë communities)

Use Cases

Standard Albanian Speech Technologies: Since Tosk forms the basis of standard Albanian, this dataset is essential for developing mainstream Albanian speech recognition systems, voice assistants, and language processing applications that serve the broader Albanian-speaking market across Albania, Greece, and the Albanian diaspora worldwide.

Cultural Heritage Preservation: The inclusion of Arbëreshë speakers from Italy provides rare linguistic data from these historic Albanian-speaking communities. Researchers and cultural organizations can use this dataset to document and preserve Arbëreshë dialects, create educational resources, and develop technologies supporting language maintenance in diaspora communities.

Cross-Platform Voice Applications: Businesses operating in the Albanian market can leverage this dataset to build voice-enabled customer service systems, mobile applications, smart home integrations, and automated phone systems that accurately understand Tosk Albanian speakers from different regions, improving user experience and accessibility.

FAQ

Q: What is Tosk Albanian and why is it significant?

A: Tosk is the southern dialect of Albanian and forms the foundation of standard Albanian used in official communications, media, and education throughout Albania. This dataset captures authentic Tosk speech patterns, making it essential for developing Albanian language technologies that align with the standard language.

Q: What makes this dataset unique with Arbëreshë speakers from Italy?

A: The inclusion of Arbëreshë speakers provides rare recordings from Albanian-speaking communities in Italy whose ancestors migrated centuries ago. This offers unique linguistic features and historical pronunciations valuable for dialectology research, heritage preservation, and understanding Albanian language evolution.

Q: How does this dataset differ from the Gheg Albanian dataset?

A: While Gheg represents northern Albanian dialects, Tosk captures southern Albanian speech which became the basis for standard Albanian. Tosk has different phonological features, vocabulary, and grammar. For applications targeting standard Albanian or southern regions, this Tosk dataset is more appropriate.

Q: What regions are covered in this dataset?

A: The dataset includes speakers from southern Albania, Albanian communities in Greece, and Arbëreshë communities in Italy, providing comprehensive geographic coverage of Tosk Albanian across its historical and contemporary speaking regions.

Q: Is this dataset suitable for building standard Albanian speech recognition?

A: Yes, absolutely. Since standard Albanian is based on Tosk, this dataset is ideal for developing speech recognition systems that work with official Albanian, making it suitable for government applications, education, media transcription, and commercial products targeting the Albanian market.

Q: What demographic diversity is represented?

A: The dataset features balanced demographics with Male (53%) and Female (47%) speakers across age groups from 18 to 50+ years old, ensuring your models perform accurately across different demographic segments of the Tosk-speaking population.

Q: How much speech data is included?

A: The dataset contains 167 hours of Tosk Albanian speech distributed across 792 audio files, providing substantial training data for developing accurate and robust speech recognition and natural language processing systems.

Q: What are the technical specifications and formats?

A: Audio files are available in both MP3 and WAV formats with a total size of 365 MB. All recordings are professionally produced with high audio quality, clear speech, and minimal background noise, optimized for machine learning training.

How to Use the Speech Dataset

Step 1: Access the Dataset

Register and obtain access to the Tosk Albanian Speech Dataset through our platform. Once approved, download the complete package including audio files, transcription texts, speaker metadata, and comprehensive documentation explaining the dataset structure and content.

Step 2: Verify Dataset Contents

After downloading, verify the integrity of all files and review the dataset structure. The package includes organized folders for audio files, corresponding transcription files, metadata spreadsheets with speaker demographics, and a detailed README document explaining naming conventions and data organization.

Step 3: Prepare Development Environment

Set up your machine learning workspace with required software and libraries. Install Python (version 3.7 or higher), deep learning frameworks such as TensorFlow or PyTorch, and audio processing libraries including Librosa, Soundfile, or Kaldi. Ensure adequate storage space (minimum 2GB) and computing power.

Step 4: Explore the Data

Conduct initial data exploration to understand audio quality, speaker diversity, and linguistic features. Listen to sample recordings from different regions (Albania, Greece, Italy), examine transcription accuracy, and review speaker metadata to understand demographic distribution.

Step 5: Implement Preprocessing

Develop your audio preprocessing pipeline. This typically includes loading audio files, resampling to consistent sample rates (e.g., 16kHz), normalizing volume levels, removing silence, and extracting acoustic features such as mel-frequency cepstral coefficients (MFCCs) or mel-spectrograms.

Step 6: Split the Dataset

Divide the dataset into training, validation, and test subsets using appropriate ratios (commonly 70-15-15 or 80-10-10). Ensure stratified sampling to maintain demographic balance across splits. Implement speaker-independent splitting to prevent the model from overfitting to specific voices.

Step 7: Apply Data Augmentation

Enhance dataset diversity through augmentation techniques including speed perturbation (0.9x to 1.1x), pitch shifting, time stretching, adding ambient noise, and applying room reverberation. These techniques improve model robustness and generalization to real-world conditions.

Step 8: Select Model Architecture

Choose an appropriate model architecture for your specific task. Options include traditional HMM-DNN systems, end-to-end models like DeepSpeech or Listen-Attend-Spell, transformer-based architectures, or pre-trained models like Wav2Vec 2.0 or Whisper that can be fine-tuned on Tosk Albanian data.

Step 9: Configure Training Parameters

Set up training configurations including batch size, learning rate, optimizer (Adam, SGD), loss function (CTC loss, cross-entropy), and regularization techniques. Implement learning rate scheduling and early stopping mechanisms to optimize training efficiency.

Step 10: Train Your Model

Execute the training process while monitoring performance metrics such as training loss, validation loss, Word Error Rate (WER), and Character Error Rate (CER). Use GPU acceleration when available to speed up training. Save model checkpoints regularly to preserve progress.

Step 11: Evaluate Performance

Conduct thorough evaluation on the test set using standard speech recognition metrics. Analyze error patterns across different speaker demographics, regions (Albania, Greece, Italy), and phonetic contexts. Compare performance against baseline models and published benchmarks.

Step 12: Optimize and Iterate

Based on evaluation results, refine your model through hyperparameter tuning, architecture adjustments, or ensemble methods. Consider implementing language models or pronunciation dictionaries specific to Tosk Albanian to improve recognition accuracy.

Step 13: Prepare for Deployment

Package your trained model for deployment in production environments. This may involve model compression, quantization for edge devices, API development for cloud deployment, or integration with existing applications. Implement proper error handling and logging.

Step 14: Deploy and Monitor

Deploy your Tosk Albanian speech recognition system to your target platform (mobile app, web service, IoT device). Implement monitoring systems to track performance, user feedback, and edge cases. Continuously collect real-world data to further improve your model through iterative updates.