Papers › The PyTorch-Kaldi Speech Recognition Toolkit

The PyTorch-Kaldi Speech Recognition Toolkit

19 Nov 2018arXiv:1811.07453archive 2025-07-28

Mirco Ravanelli, Titouan Parcollet, Yoshua Bengio

The availability of open-source software is playing a remarkable role in the popularization of speech recognition and deep learning. Kaldi, for instance, is nowadays an established framework used to develop state-of-the-art speech recognizers. PyTorch is used to build neural networks with the Python language and has recently spawn tremendous interest within the machine learning community thanks to its simplicity and flexibility. The PyTorch-Kaldi project aims to bridge the gap between these popular toolkits, trying to inherit the efficiency of Kaldi and the flexibility of PyTorch. PyTorch-Kaldi is not only a simple interface between these software, but it embeds several useful features for developing modern speech recognizers. For instance, the code is specifically designed to naturally plug-in user-defined acoustic models. As an alternative, users can exploit several pre-implemented neural networks that can be customized using intuitive configuration files. PyTorch-Kaldi supports multiple feature and label streams as well as combinations of neural networks, enabling the use of complex neural architectures. The toolkit is publicly-released along with a rich documentation and is designed to properly work locally or on HPC clusters. Experiments, that are conducted on several datasets and tasks, show that PyTorch-Kaldi can effectively be used to develop modern state-of-the-art speech recognizers.

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mravanelli/pytorch-kaldi officialmentioned in papermentioned on GitHubpytorch report
Baileyswu/pytorch-hmm-vae mentioned on GitHubpytorch report
Dahee96/Seq2seq- mentioned on GitHubpytorch report
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ayyucedemirbas/gcommands_12_classes mentioned on GitHubpytorch report
walterheymans/pytorch-kaldi-gan mentioned on GitHubpytorch report
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xpz123/pytorch-kaldi mentioned on GitHubpytorch report
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Tasks

Distant Speech RecognitionNoisy Speech RecognitionSpeech Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Distant Speech Recognition DIRHA English WSJ Li-GRU Word Error Rate (WER) 23.9 #1 of 3 Archive leaderboard report
Noisy Speech Recognition CHiME real Li-GRU Percentage error 14.6 #3 of 5 Archive leaderboard report
Speech Recognition LibriSpeech test-clean Li-GRU Word Error Rate (WER) 6.2 #60 of 64 Archive leaderboard report
Speech Recognition TIMIT LiGRU + Dropout + BatchNorm + Monophone Reg Percentage error 14.2 #3 of 22 Archive leaderboard report
Speech Recognition TIMIT LSTM + Dropout + BatchNorm + Monophone Reg Percentage error 14.5 #4 of 22 Archive leaderboard report
Speech Recognition TIMIT GRU + Dropout + BatchNorm + Monophone Reg Percentage error 14.9 #6 of 22 Archive leaderboard report
Speech Recognition TIMIT RNN + Dropout + BatchNorm + Monophone Reg Percentage error 15.9 #8 of 22 Archive leaderboard report
Speech Recognition TIMIT LSTM Percentage error 16.0 #9 of 22 Archive leaderboard report
Speech Recognition TIMIT Li-GRU Percentage error 16.3 #10 of 22 Archive leaderboard report
Speech Recognition TIMIT RNN Percentage error 16.5 #12 of 22 Archive leaderboard report
Speech Recognition TIMIT GRU Percentage error 16.6 #13 of 22 Archive leaderboard report

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