Integrating speech and text capabilities into large language models (LLMs) is a challenging task and we present Large Rank Adaptation (LaRA) for effective cross-modal integration of speech and text in the LLM framework. Unlike conventional LoRA, our method requires significantly larger ranks comparable to the pretrained weights to accommodate the complexities of speech-text cross-modality learning. The approach utilizes HuBERT to convert speech into discrete tokens and fine-tunes the pretrained LLM to adapt to cross-modal inputs and outputs. The work employs a Hi-Fi GAN vocoder to synthesize speech waveforms from the generated speech units. The initial studies use the Librispeech corpus to teach the model the relationships between speech and text, and Daily Talk, which involves dialog conversations, to adapt for interaction. The proposed work demonstrates adaptation for spoken and text conversations. However, the proposed framework can be easily extended to other cross-modal applications.

This paper presents the implementation of Machine Translation (MT) between Lambani, a low-resource Indian tribal language, and English, a high-resource universal language. Lambani is spoken by nomadic tribes of the Indian state of Karnataka and there are similarities between Lambani and various other Indian languages. To implement the English-Lambani MT system, we followed the transfer learning approach with English-Kannada as the parent MT model. The implementation and performance of the English-Lambani MT system are discussed in this paper. Since Lambani has been influenced by various other languages, we explored the possibility of getting better MT performance by using parent models associated with related Indian languages. Specifically, we experimented with English-Gujarati and English-Marathi as additional parent models. We compare the performance of three different English-Lambani MT systems derived from three parent language models, and the observations are presented in the paper. Additionally, we will also explore the effect of freezing the encoder layer and decoder layer and the change in performance from both of them.