Shinji Watanabe, Patrick Fernandes, Yifan Peng, Siddharth Dalmia, Dan Berrebbi, Juan Pino, Xiaohui Zhang, Jiatong Shi, Brian Yan, Peter Polák, Hirofumi Inaguma, Yun Tang, and 4 more
We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| espnet/espnet | canonical | 2 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_multitask_egs_opts | Ran | espnet/espnet/egs2/TEMPLATE/asr1/steps/libs/nnet3/train/common.py code served (permissive licence) · get_code("5d7679f03bf6c54b") |
| get_successful_models | Ran | espnet/espnet/egs2/TEMPLATE/asr1/steps/libs/nnet3/train/common.py code served (permissive licence) · get_code("22803b933df066d4") |
| rvad | Ran | this paper's copy was not recorded; identical code first harvested from espnet/espnet pointer only · get_code("a960291efd1b0f20") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offline speech-to-text translation (ST), 2) simultaneous speech-to-text translation (SST), and 3) offline speech-to-speech translation (S2ST) -each task is supported with a wide variety of approaches, differentiating ESPnet-ST-v2 from other open source spoken language translation toolkits. This toolkit offers state-ofthe-art architectures such as transducers, hybrid CTC/attention, multi-decoders with searchable intermediates, time-synchronous blockwise CTC/attention, Translatotron models, and direct discrete unit models. In this paper, we describe the overall design, example models for each task, and performance benchmarking behind ESPnet-ST-v2, which is publicly available at https://github.com/espnet/espnet. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2304.04596")
get_code_for_paper("2304.04596")
have("2304.04596")
Connect an agent — have() is free.