Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.18895 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912217609273344 |
|---|---|
| author | Xu, Jingjing Beck, Eugen Yang, Zijian Schlüter, Ralf |
| author_facet | Xu, Jingjing Beck, Eugen Yang, Zijian Schlüter, Ralf |
| contents | ASR systems are deployed across diverse environments, each with specific hardware constraints. We use supernet training to jointly train multiple encoders of varying sizes, enabling dynamic model size adjustment to fit hardware constraints without redundant training. Moreover, we introduce a novel method called OrthoSoftmax, which applies multiple orthogonal softmax functions to efficiently identify optimal subnets within the supernet, avoiding resource-intensive search. This approach also enables more flexible and precise subnet selection by allowing selection based on various criteria and levels of granularity. Our results with CTC on Librispeech and TED-LIUM-v2 show that FLOPs-aware component-wise selection achieves the best overall performance. With the same number of training updates from one single job, WERs for all model sizes are comparable to or slightly better than those of individually trained models. Furthermore, we analyze patterns in the selected components and reveal interesting insights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18895 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression Xu, Jingjing Beck, Eugen Yang, Zijian Schlüter, Ralf Computation and Language ASR systems are deployed across diverse environments, each with specific hardware constraints. We use supernet training to jointly train multiple encoders of varying sizes, enabling dynamic model size adjustment to fit hardware constraints without redundant training. Moreover, we introduce a novel method called OrthoSoftmax, which applies multiple orthogonal softmax functions to efficiently identify optimal subnets within the supernet, avoiding resource-intensive search. This approach also enables more flexible and precise subnet selection by allowing selection based on various criteria and levels of granularity. Our results with CTC on Librispeech and TED-LIUM-v2 show that FLOPs-aware component-wise selection achieves the best overall performance. With the same number of training updates from one single job, WERs for all model sizes are comparable to or slightly better than those of individually trained models. Furthermore, we analyze patterns in the selected components and reveal interesting insights. |
| title | Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.18895 |