mHuBERT-147: A Compact Multilingual HuBERT Model
<|reference_start|>mHuBERT-147: A Compact Multilingual HuBERT Model: We present mHuBERT-147, the first general-purpose massively multilingual HuBERT speech representation model trained on 90K hours of clean, open-license data. To scale up the multi-iteration HuBERT approach, we use faiss-based clustering, achieving 5.2x faster label assignment than the original method. We also apply a new multilingual batching up-sampling strategy, leveraging both language and dataset diversity. After 3 training iterations, our compact 95M parameter mHuBERT-147 outperforms larger models trained on substantially more data. We rank second and first on the ML-SUPERB 10min and 1h leaderboards, with SOTA scores for 3 tasks. Across ASR/LID tasks, our model consistently surpasses XLS-R (300M params; 436K hours) and demonstrates strong competitiveness against the much larger MMS (1B params; 491K hours). Our findings indicate that mHuBERT-147 is a promising model for multilingual speech tasks, offering an unprecedented balance between high performance and parameter efficiency.<|reference_end|>
@article{boito2024mhubert-147:,
    title={mHuBERT-147: A Compact Multilingual HuBERT Model},
    author={Marcely Zanon Boito, Vivek Iyer, Nikolaos Lagos, Laurent Besacier,
  Ioan Calapodescu},
    journal={arXiv preprint arXiv:2406.06371},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06371},
    primaryClass={cs.CL cs.SD eess.AS}
}