5 citations · 5 across the 6 of their papers we have counts for
6 papers
Giga-Embeddings: Mixture-of-Experts Encoders for High-Throughput Text Embeddings
Egor Kolodin, Egor Krasnoperov, Evgeniy Kosarev +1
We introduce Giga-Embeddings, a family of text embedding models designed to combine strong retrieval quality with efficient serving. Its largest member is a sparse 10B-parameter Mi…
GigaChat Audio: Time-aware Large Audio Language Model
Aleksandr Kutsakov, Mariia Sadovina, Georgii Gospodinov +4
Temporal grounding in long recordings remains challenging for audio-conditioned LLMs. We present a time-aware audio LLM that answers questions with explicit timestamps over up to 1…
GigaAM Multilingual: Foundation Model for Underrepresented Languages
Andrei Kuzmenko, Alexandr Maximenko, Aleksandr Kutsakov +5
Despite recent scaling successes, multilingual ASR performance remains highly uneven, with long-tail languages suffering from severe data scarcity. This work addresses the challeng…
GigaEmbeddings: Efficient Russian Language Embedding Model
Egor Kolodin, Daria Khomich, Nikita Savushkin +2
We introduce GigaEmbeddings, a novel framework for training high-performance Russian-focused text embeddings through hierarchical instruction tuning of the decoder-only LLM designe…
GigaAM: Efficient Self-Supervised Learner for Speech Recognition
Aleksandr Kutsakov, Alexandr Maximenko, Georgii Gospodinov +2
Self-Supervised Learning (SSL) has demonstrated strong performance in speech processing, particularly in automatic speech recognition. In this paper, we explore an SSL pretraining…
Large Raw Emotional Dataset with Aggregation Mechanism
Vladimir Kondratenko, Artem Sokolov, Nikolay Karpov +3
We present a new data set for speech emotion recognition (SER) tasks called Dusha. The corpus contains approximately 350 hours of data, more than 300 000 audio recordings with Russ…