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- W4313484417 abstract "With the development of hardware for machine learning, newer models often come at the cost of both increased sizes and computational complexity. In effort to improve the efficiency for these models, we apply and investigate recent quantization techniques on speech representation learning models. The quantization techniques were evaluated on the SUPERB benchmark. On the ASR task, with aggressive quantization to 1 bit, we achieved 86.32% storage reduction (184.42 -> 25.23), 88% estimated runtime reduction (1.00 -> 0.12) with increased word error rate (7.06 -> 15.96). In comparison with DistillHuBERT which also aims for model compression, the 2-bit configuration yielded slightly smaller storage (35.84 vs. 46.98), better word error rate (12.68 vs. 13.37) and more efficient estimated runtime (0.15 vs. 0.73)." @default.
- W4313484417 created "2023-01-06" @default.
- W4313484417 creator A5027043104 @default.
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- W4313484417 date "2022-12-14" @default.
- W4313484417 modified "2023-09-30" @default.
- W4313484417 title "Efficient Speech Representation Learning with Low-Bit Quantization" @default.
- W4313484417 doi "https://doi.org/10.48550/arxiv.2301.00652" @default.
- W4313484417 hasPublicationYear "2022" @default.
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