In recent times, with the growing popularity of artificial intelligence, the field of Automated Speech Recognition (ASR) has seen tremendous progress. It has changed the face of voice-activated technologies and human-computer interaction. With ASR, machines can translate spoken language into text, which is essential for a variety of applications, including virtual assistants and transcription services. Researchers have struggled to find underlying algorithms as more accurate and efficient ASR systems are needed.
In recent research from NVIDIA, a team of researchers has studied the drawbacks of Connectionist Temporal Classification (CTC) models. In ASR pipelines, CTC models have become a leading contender for achieving high accuracy. These models are especially good at handling the subtleties of spoken language because they are very good at interpreting temporal sequences. Although accurate, the conventional CPU-based beam search decoding method has limited the performance of CTC models.
The beam search decoding process is an essential stage in accurately transcribing spoken words. The traditional method, which is the greedy search method, uses the acoustic model to determine which output token is most likely to be selected at each time step. When it comes to managing contextual biases and external data, this approach comes with a number of challenges.
To overcome all these challenges, the team has proposed GPU-accelerated Weighted Finite State Transducer Beam Searching (WFST) decoder as a solution. This approach was introduced with the aim of seamlessly integrating it with current CTC models. With this GPU-accelerated decoder, the performance of the ASR pipeline can be improved, along with throughput, latency, and support for features such as on-the-fly compositing to enhance specific words of expressions. The suggested GPU-accelerated decoder is especially suitable for streaming inference due to its improved pipeline performance and lower latency.
The team evaluated this approach by testing the decoder in online and offline environments. Compared with the state-of-the-art CPU decoder, the GPU-accelerated decoder showed up to seven times higher performance in the offline scenario. The GPU-accelerated decoder achieved eight times lower latency in the online streaming scenario, while maintaining the same or even higher word error rates. These findings show that employing the suggested GPU-accelerated WFST beamsearch decoder with CTC models significantly improves efficiency and accuracy.
In conclusion, this approach can definitely work excellently to overcome the performance limitations of CPU-based beam search decoding in CTC models. The suggested GPU-accelerated decoder is the fastest beam search decoder for CTC models in both online and offline contexts as it improves performance, reduces latency, and supports advanced features. To assist with the integration of the decoder with Python-based machine learning frameworks, the team has made pre-built Python bindings based on DLPack available on GitHub. This work adds to the usability and accessibility of the suggested solution for Python developers with ML frameworks. The code repository can be accessed at https://github.com/nvidia-riva/riva-asrlib-decoder with a CUDA WFST decoder described as a C++ and Python library.
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Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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