SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning

Prabhat Pandey; Rupak Vignesh Swaminathan; K V Vijay Girish; Arunasish Sen; Jian Xie; Grant P. Strimel; Andreas Schwarz

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arXiv · 2504.09081

SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning

Prabhat Pandey·Rupak Vignesh Swaminathan·K V Vijay Girish·Arunasish Sen·Jian Xie·Grant P. Strimel·Andreas Schwarz

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Abstract

We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50M is built from publicly available speech corpora, which collectively contain 14K hours of speech, and leverages LLMs along with off-the-shelf expert models. The dataset spans five languages, encompassing a diverse range of speech understanding as well as controllable speech generation instructions. Using SIFT-50M, we train SIFT-LLM, which outperforms existing speech-text LLMs on instruction-following benchmarks while achieving competitive performance on foundational speech tasks. To support further research, we also introduce EvalSIFT, a benchmark dataset specifically designed to evaluate the instruction-following capabilities of speech-text LLMs.

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Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish, Arunasish Sen, Jian Xie, Grant P. Strimel, Andreas Schwarz. 2025-04-12. SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning. https://arxiv.org/abs/2504.09081

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