Soundwave: Less is More for Speech-Text Alignment in LLMs
Existing end-to-end speech large language models (LLMs) usually rely on
large-scale annotated data for training, while data-efficient training has not
been discussed in depth. We focus on two fundamental problems between speech
and text: the representation space gap and sequence length inconsistency. We
propose Soundwave, which utilizes an efficient training strategy and a novel
architecture to address these issues. Results show that Soundwave outperforms
the advanced Qwen2-Audio in speech translation and AIR-Bench speech tasks,
using only one-fiftieth of the training data. Further analysis shows that
Soundwave still retains its intelligence during conversation. The project is
available at https://github.com/FreedomIntelligence/Soundwave.
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Yuhao Zhang
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2025-09-12T03:51:14.248Z
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Yuhao Zhang
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Yoohao
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Zhiheng Liu
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2025-02-19T09:01:05.678Z
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Chihang Lau
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Fan Bu
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2025-02-19T09:42:08.544Z
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Fan Bu
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Ruiyu Zhang
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2025-02-19T09:42:14.866Z
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Ruiyu Zhang
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Benyou Wang
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2025-02-19T09:42:23.845Z
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Wang
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Benyou
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Haizhou Li
Soundwave: Less is More for Speech-Text Alignment in LLMs
Existing end-to-end speech large language models (LLMs) usually rely on
large-scale annotated data for training, while data-efficient training has not
been discussed in depth. We focus on two fundamental problems between speech
and text: the representation space gap and sequence length inconsistency. We
propose Soundwave, which utilizes an efficient training strategy and a novel
architecture to address these issues. Results show that Soundwave outperforms
the advanced Qwen2-Audio in speech translation and AIR-Bench speech tasks,
using only one-fiftieth of the training data. Further analysis shows that
Soundwave still retains its intelligence during conversation. The project is
available at https://github.com/FreedomIntelligence/Soundwave.
Soundwave addresses the representation space gap and sequence length inconsistency in end-to-end speech large language models using an efficient training strategy and novel architecture, outperforming Qwen2-Audio with significantly less data.
large language models large-scale annotated data data-efficient training representation space gap sequence length inconsistency soundwave Qwen2-Audio speech translation AIR-Bench speech tasks