The Kurmanji Kurdish Speech Dataset is a comprehensive collection of high-quality audio recordings from native Kurmanji speakers across Turkey, Syria, Iraq, Iran, and Armenia. This professionally curated dataset contains 161 hours of authentic Kurmanji speech data, meticulously annotated and structured for machine learning applications. Kurmanji, the most widely spoken Kurdish dialect with over 15 million speakers across five countries, 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 Kurmanji language models, voice assistants, and conversational AI systems serving Kurdish-speaking populations across Middle Eastern and Caucasian regions. The audio files are delivered in MP3/WAV format with consistent quality standards, making them immediately ready for integration into ML pipelines focused on Kurdish language technology and supporting one of the region’s major stateless linguistic communities.
Dataset General Info
| Parameter | Details |
| Size | 161 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 246 MB |
| Number of files | 659 files |
| Gender of speakers | Female: 55%, Male: 45% |
| Age of speakers | 18-30 years: 27%, 31-40 years: 30%, 40-50 years: 19%, 50+ years: 24% |
| Countries | Turkey, Syria, Iraq, Iran, Armenia |
Use Cases
Media and Broadcasting Services: Kurdish media organizations across Turkey, Syria, Iraq, Iran, and diaspora communities can utilize the Kurmanji Kurdish Speech Dataset to develop automatic transcription for Kurdish television and radio, voice-enabled content platforms, and news delivery systems. These applications support Kurdish media serving millions of speakers, make cultural content more accessible across divided communities, and preserve Kurmanji linguistic presence in digital media landscape despite political challenges.
Educational Technology and Language Rights: Educational institutions and cultural organizations can leverage this dataset to create Kurmanji language learning applications, digital educational resources, and mother-tongue education tools. Voice technology supports Kurdish language education where permitted, enables heritage language learning in diaspora communities, and helps maintain Kurmanji vitality across generations despite historical restrictions on Kurdish language use in various countries.
Cultural Preservation and Community Connection: Organizations serving Kurdish communities can employ this dataset to develop voice-enabled access to Kurdish literature, oral traditions, and cultural heritage resources. These applications preserve Kurmanji linguistic and cultural identity, support communication across politically divided Kurdish regions, and maintain linguistic continuity for one of the Middle East’s major stateless nations, enabling Kurdish speakers to access technology in their mother tongue.
FAQ
Q: What does the Kurmanji Kurdish Speech Dataset include?
A: The Kurmanji Kurdish Speech Dataset contains 161 hours of authentic audio recordings from native Kurmanji speakers across Turkey, Syria, Iraq, Iran, and Armenia. The dataset includes 659 files in MP3/WAV format totaling approximately 246 MB, with transcriptions, speaker demographics, cross-border information, and linguistic annotations.
Q: Why is Kurmanji technology important for Kurdish communities?
A: Kurmanji is the most widely spoken Kurdish dialect with over 15 million speakers, but Kurdish language faces historical restrictions and underrepresentation in technology. This dataset enables development of Kurdish language technology, supports linguistic rights, and provides Kurdish speakers access to modern technology in their mother tongue despite political challenges.
Q: How does the dataset address Kurmanji’s cross-border nature?
A: Kurdish speakers are divided across five countries without Kurdish nation-state. The dataset captures Kurmanji from Turkey, Syria, Iraq, Iran, and Armenia, representing dialectal variations across these regions. This enables applications serving entire Kurmanji-speaking population regardless of national borders dividing Kurdish communities.
Q: What script systems does Kurmanji use?
A: Kurmanji is written in Latin script in Turkey and Syria, and modified Arabic script (Sorani-influenced) in some contexts. The dataset includes appropriate transcriptions for different contexts, supporting development of applications that can handle multiple writing systems used by Kurdish speakers in different countries.
Q: Can this dataset support cultural preservation?
A: Yes, Kurdish culture faces preservation challenges due to political histories. The dataset supports development of applications that preserve Kurdish oral traditions, literature, music, and cultural practices through voice technology, helping maintain Kurdish cultural identity across divided communities.
Q: What is the demographic distribution?
A: The dataset includes 55% female and 45% male speakers with age distribution of 27% aged 18-30, 30% aged 31-40, 19% aged 40-50, and 24% aged 50+. Cross-border representation from five countries ensures comprehensive coverage.
Q: What applications benefit from Kurmanji technology?
A: Applications include Kurdish media transcription and broadcasting tools, educational resources for Kurdish language learning, cultural preservation platforms, diaspora community services, cross-border communication tools, and voice interfaces enabling Kurdish speakers to access technology in their language despite historical restrictions.
Q: How does this support Kurdish linguistic rights?
A: The dataset contributes to Kurdish linguistic rights by enabling technology development in Kurmanji. It provides tools for education, media, and communication in Kurdish language, supports cultural preservation, and helps ensure Kurdish remains vibrant living language rather than becoming marginalized, despite political challenges facing Kurdish communities.
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.
