How Hatek-Lingua AI is designed to process a video.
The planned system combines speech recognition, translation, generated speech, video processing and quality evaluation. Each part contributes to the final translated video.
The product is in development, so this page describes the intended technical architecture rather than a finished production system.
The system starts with the original video and ends with a translated audiovisual version.
The main technical challenge is keeping language meaning, speech timing and visible mouth movement consistent with one another.
Speech recognition and timing
The system identifies spoken words, speaker changes and the timing of each segment. This timing information is needed later when translated speech is generated.
Context-aware translation
The spoken content is translated into the target language while preserving meaning, names, terminology and conversational context as closely as possible.
Target-language speech generation
The translated text is converted into speech. Pronunciation, naturalness, pacing and duration are important because the audio must still fit the video.
Visual speech synchronisation
The video is adjusted so the visible speaker's mouth movement follows the generated target-language speech while the surrounding scene remains stable.
Quality evaluation
The output is checked for translation quality, pronunciation, speech timing, visual consistency and common failure cases before it is accepted.
One workflow for the user
The product goal is to hide the complexity of the individual AI systems behind a simple workflow: upload video, choose language, review and export.
A translated video is only useful when several kinds of quality are acceptable at the same time.
A strong translation with poor pronunciation is not enough. Good audio with unstable lip synchronisation is not enough either. Evaluation therefore has to cover the full audiovisual result.
Does the translated speech preserve the intended message?
Are names, local words and target-language sounds spoken correctly?
Does the translated speech fit the available speaking time naturally?
Does the face remain stable while mouth movement follows the new speech?
GPU acceleration is planned for model training and inference.
The development plan includes NVIDIA GPUs for compute-intensive speech, language and video workloads.
Planned use of NVIDIA technology includes CUDA for accelerated model workloads and TensorRT for optimising inference when models are prepared for deployment. Exact infrastructure choices will depend on benchmark results, model size, latency and cost.
We are open to research, compute, data and evaluation partnerships.
Useful collaboration can include language data, native-language evaluation, AI infrastructure, video testing and research expertise.