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We present Espresso, an open-source, modular, extensible end-to-end neuralautomatic speech recognition (ASR) toolkit based on the deep learning libraryPyTorch and the popular neural machine translation toolkit fairseq. Espressosupports distributed training across GPUs and computing nodes, and featuresvarious decoding approaches commonly employed in ASR, including look-aheadword-based language model fusion, for which a fast, parallelized decoder isimplemented. Espresso achieves state-of-the-art ASR performance on the WSJ,LibriSpeech, and Switchboard data sets among other end-to-end systems withoutdata augmentation, and is 4--11x faster for decoding than similar systems (e.g.ESPnet).

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit