Papers › Espresso: A Fast End-to-end Neural Speech Recognition Toolkit
Espresso: A Fast End-to-end Neural Speech Recognition Toolkit
Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, Sanjeev Khudanpur
We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit fairseq. Espresso supports distributed training across GPUs and computing nodes, and features various decoding approaches commonly employed in ASR, including look-ahead word-based language model fusion, for which a fast, parallelized decoder is implemented. Espresso achieves state-of-the-art ASR performance on the WSJ, LibriSpeech, and Switchboard data sets among other end-to-end systems without data augmentation, and is 4--11x faster for decoding than similar systems (e.g. ESPnet).
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bbe1a698e337d4d4 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Recognition | Hub5'00 CallHome | Espresso | Word Error Rate (WER) | 19.1 | #1 of 1 | Archive leaderboard | report |
| Speech Recognition | Hub5'00 SwitchBoard | Espresso | Eval2000 | 9.2 | #5 of 5 | Archive leaderboard | report |
| Speech Recognition | LibriSpeech test-clean | Espresso | Word Error Rate (WER) | 2.8 | #44 of 64 | Archive leaderboard | report |
| Speech Recognition | LibriSpeech test-other | Espresso | Word Error Rate (WER) | 8.7 | #44 of 53 | Archive leaderboard | report |
| Speech Recognition | WSJ eval92 | Espresso | Word Error Rate (WER) | 3.4 | #10 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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