About Machine Learning Model RNN-T

Recurrent Neural Network Transducer (RNN-T) is a framework specifically developed for automatic speech recognition (ASR). Its growing popularity, particularly in real-time ASR systems, is attributed to its ability to provide high accuracy while offering naturally streaming recognition capabilities. The RNN-T framework is distinct due to its transducer loss function, which, although effective, can be slow to compute and memory-intensive. This presents challenges, especially when dealing with large vocabulary sizes, such as in Chinese character-based ASR systems. The framework's appeal in the industry is due to its natural streaming ability and the fact that it doesn't require the full context to predict the next token, setting it apart from other models like attention-based models and connectionist temporal classification (CTC) models.

Model Card for RNN-T

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