ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866910603972444160 |
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| author | Someki, Masao Choi, Kwanghee Arora, Siddhant Chen, William Cornell, Samuele Han, Jionghao Peng, Yifan Shi, Jiatong Srivastav, Vaibhav Watanabe, Shinji |
| author_facet | Someki, Masao Choi, Kwanghee Arora, Siddhant Chen, William Cornell, Samuele Han, Jionghao Peng, Yifan Shi, Jiatong Srivastav, Vaibhav Watanabe, Shinji |
| contents | We introduce ESPnet-EZ, an extension of the open-source speech processing toolkit ESPnet, aimed at quick and easy development of speech models. ESPnet-EZ focuses on two major aspects: (i) easy fine-tuning and inference of existing ESPnet models on various tasks and (ii) easy integration with popular deep neural network frameworks such as PyTorch-Lightning, Hugging Face transformers and datasets, and Lhotse. By replacing ESPnet design choices inherited from Kaldi with a Python-only, Bash-free interface, we dramatically reduce the effort required to build, debug, and use a new model. For example, to fine-tune a speech foundation model, ESPnet-EZ, compared to ESPnet, reduces the number of newly written code by 2.7x and the amount of dependent code by 6.7x while dramatically reducing the Bash script dependencies. The codebase of ESPnet-EZ is publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09506 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration Someki, Masao Choi, Kwanghee Arora, Siddhant Chen, William Cornell, Samuele Han, Jionghao Peng, Yifan Shi, Jiatong Srivastav, Vaibhav Watanabe, Shinji Sound Artificial Intelligence Audio and Speech Processing We introduce ESPnet-EZ, an extension of the open-source speech processing toolkit ESPnet, aimed at quick and easy development of speech models. ESPnet-EZ focuses on two major aspects: (i) easy fine-tuning and inference of existing ESPnet models on various tasks and (ii) easy integration with popular deep neural network frameworks such as PyTorch-Lightning, Hugging Face transformers and datasets, and Lhotse. By replacing ESPnet design choices inherited from Kaldi with a Python-only, Bash-free interface, we dramatically reduce the effort required to build, debug, and use a new model. For example, to fine-tune a speech foundation model, ESPnet-EZ, compared to ESPnet, reduces the number of newly written code by 2.7x and the amount of dependent code by 6.7x while dramatically reducing the Bash script dependencies. The codebase of ESPnet-EZ is publicly available. |
| title | ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.09506 |