The Pytorch-kaldi Speech Recognition Toolkit

Abstract

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.

Multi-Task Self-Supervised Learning for Robust Speech Recognition

“…This work uses hybrid HMM-DNN speech recognizers. TIMIT and DIRHA experiments are performed with the PyTorch-Kaldi toolkit [35] using a six-layer multi-layer perceptron and a light GRU [36], respectively. The performance reported on TIMIT is the average of the phone error rates (PER%) obtained by running each experiment three times with different seeds.…”

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

“…The active development of open-source software toolkits plays a significant role in the rapid progress of ASR research, instances include the Kaldi (Povey et al, 2011) and ESPnet (Watanabe et al, 2018). In this work, we demonstrate that state-of-the-art SNN acoustic models can be easily developed in PyTorch and integrated into the PyTorch-Kaldi Speech Recognition Toolkit (Ravanelli et al, 2019). This software toolkit integrates the efficiency of Kaldi and the flexibility of PyTorch, therefore, it can support the rapid development of SNN-based ASR systems.…”

“…All ASR experiments are performed using the PyTorch-Kaldi ASR toolkit (Ravanelli et al, 2019). This recently introduced toolkit inherits the flexibility of PyTorch toolkit (Paszke et al, 2017) for ANN-based acoustic model development and the efficiency of Kaldi ASR toolkit (Povey et al, 2011).…”

Real to H-Space Encoder for Speech Recognition

“…The learning rate is halved every-time the loss on the validation set is below a certain threshold fixed to 0.001 to avoid overfitting. Finally, models are implemented with the Pytorch-Kaldi toolkit [18]. While the effectiveness of QLSTM over LSTM has been demonstrated, an LSTM network trained in the same conditions and based on [5] is considered as a baseline.…”