An independent research project focused on developing a high-quality automatic speech recognition system for the Armenian language.
Armenian ASR is an ongoing research initiative focused on advancing Armenian speech recognition technology. Operating independently of commercial pressure, the project aims to develop high-fidelity acoustic processing models configured for Armenian speech patterns, dialects, and phonology.
This project is currently in the active research and training preparation stage. We do not claim to have a finished model or state-of-the-art benchmark performances. Our singular objective is to build a high-performance Armenian speech recognition model that is robust, open, and accessible.
A curated speech collection published on Hugging Face and prepared for automatic speech recognition research. Every figure below is verifiable on the dataset card.
Voice-activity detection applied during preparation. Audio is stored as Snappy-compressed Parquet and streams directly through the datasets library, so it can be inspected without downloading all 885 GB.
Tracking our milestones from initial collection to community release.
Gathering diverse spoken Armenian audio sources including audiobooks, media transcripts, and crowd-sourced recordings.
Cleaning, voice-activity segmentation, and quality auditing of audio samples, packaged as Parquet shards ready for self-supervised pre-training.
Setting up transformer-based architectures and self-supervised speech models configured for Armenian phonology.
Testing model accuracy using Word Error Rate (WER) metrics against benchmarks, dialects, and noisy recordings.
Deploying model weights, inference recipes, and open-source validation scripts for research collaboration.
The fundamental objectives guiding our research and engineering decisions.
Developing robust acoustic models that accurately transcribe spoken Armenian, accommodating diverse age groups, accents, and recording conditions.
Applying state-of-the-art deep learning architectures, transformer networks, and self-supervised learning recipes to speech recognition.
Building foundational tools and open-source models that expand the digital presence and technical capabilities of the Armenian language.
Sharing pre-trained models, curated datasets, and research findings openly to foster collaboration among global AI researchers.
Making advanced voice-controlled technology, transcriptions, and digital assistants accessible to all Armenian speakers worldwide.
Using machine learning to document, transcribe, and preserve spoken dialects, oral histories, and linguistic heritage of the Armenian language.
The repository currently holds the project overview and roadmap. Training scripts, model checkpoints and evaluation recipes are scheduled for publication once the initial research phase concludes — the dataset above is the artifact available today.