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Wekws: A Production First Small-Footprint End-to-End Keyword Spotting Toolkit 48

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Wekws: A Production First Small-Footprint End-to-End Keyword...

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Jie Xu, Menglong Hou, Jingyong Zhang, Binbin Zhang, Xiao-Lei Xie, Lei Pan, Fuping Northwestern Polytechnical University School of Marine Science and Technology Xi'an China WeNet Open Source Community China Horizon Robotics Beijing China School of Computer Science Xi'an China

ISBN: (纸本)9781728163277

Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices. Recently, end-to-end (E2E) methods have be-come the most popular approach for on-device KWS tasks. However, there is still a gap between the research and deployment of E2E KWS methods. In this paper, we introduce WeKws, a production-quality, easy-to-build, and convenient-to-be-applied E2E KWS toolkit. WeKws contains the implementations of several state-of-the-art backbone networks, making it achieve highly competitive results on three publicly available datasets. To make WeKws a pure E2E toolkit, we utilize a refined max-pooling loss to make the model learn the ending position of the keyword by itself, which significantly simplifies the training pipeline and makes WeKws very efficient to be applied in real-world scenarios. The toolkit is publicly available at https://***/wenet-e2e/wekws. © 2023 IEEE.

关键词： Computer vision

LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech 48

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LightGrad: Lightweight Diffusion Probabilistic Model for Tex...

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Jie Song, Xingchen Peng, Zhendong Zhang, Binbin Pan, Fuping Wu, Zhiyong Tsinghua University Shenzhen International Graduate School Shenzhen China Horizon Robotics Beijing China WeNet Open Source Community The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781728163277

Recent advances in neural text-to-speech (TTS) models bring thousands of TTS applications into daily life, where models are deployed in cloud to provide services for customs. Among these models are diffusion probabilistic models (DPMs), which can be stably trained and are more parameter-efficient compared with other generative models. As transmitting data between customs and the cloud introduces high latency and the risk of exposing private data, deploying TTS models on edge devices is preferred. When implementing DPMs onto edge devices, there are two practical problems. First, current DPMs are not lightweight enough for resource-constrained devices. Second, DPMs require many denoising steps in inference, which increases latency. In this work, we present LightGrad, a lightweight DPM for TTS. LightGrad is equipped with a lightweight U-Net diffusion decoder and a training-free fast sampling technique, reducing both model parameters and inference latency. Streaming inference is also implemented in LightGrad to reduce latency further. Compared with Grad-TTS, LightGrad achieves 62.2% reduction in paramters, 65.7% reduction in latency, while preserving comparable speech quality on both Chinese Mandarin and English in 4 denoising steps1. © 2023 IEEE.

关键词： Diffusion

Unleashing Artificial Cognition: Integrating Multiple AI Systems

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arXiv 2024年

作者： Adnan, Muntasir Gamage, Buddhi Xu, Zhiwei Herath, Damith Kuhn, Carlos C.N. Open Source Institute Faculty of Science and Technology University of Canberra Australia Collaborative Robotics Lab Faculty of Science and Technology University of Canberra Australia

In this study, we present an innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence. The introduced open-source AI system seamlessly integrates a Chess engine with a language model, enabling it to predict moves and provide strategic explanations. Leveraging a vector database to achieve retrievable answer generation, our AI system elucidates its decision-making process, bridging the gap between raw computation and human-like understanding. Our choice of Chess as the demonstration environment underscores the versatility of our approach. Beyond Chess, our system holds promise for diverse applications, from medical diagnostics to financial forecasting. Our AI system is available at https://***/TheopenSI/***. © 2024, CC BY.

关键词： Structured Query Language

Fast-U2++: Fast and Accurate End-to-End Speech Recognition in Joint CTC/Attention Frames

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Fast-U2++: Fast and Accurate End-to-End Speech Recognition i...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengdong Liang Xiao-Lei Zhang BinBin Zhang Di Wu Shengqiang Li Xingchen Song Zhendong Peng Fuping Pan School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Horizon Robotics Beijing China WeNet Open Source Community

关键词： Degradation Error analysis Speech recognition Signal processing Acoustics Speech processing

LIGHTGRAD: LIGHTWEIGHT DIFFUSION PROBABILISTIC MODEL FOR TEXT-TO-SPEECH

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arXiv 2023年

作者： Chen, Jie Song, Xingchen Peng, Zhendong Zhang, Binbin Pan, Fuping Wu, Zhiyong Shenzhen International Graduate School Tsinghua University Shenzhen China Horizon Robotics Beijing China WeNet Open Source Community China The Chinese University of Hong Kong Hong Kong

关键词： Diffusion

LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech

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LightGrad: Lightweight Diffusion Probabilistic Model for Tex...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jie Chen Xingchen Song Zhendong Peng Binbin Zhang Fuping Pan Zhiyong Wu Shenzhen International Graduate School Tsinghua University Shenzhen China Horizon Robotics Beijing China WeNet Open Source Community The Chinese University of Hong Kong Hong Kong SAR China

关键词： Noise reduction Signal processing Probabilistic logic Data models Acoustics Decoding Speech processing

WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit

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arXiv 2022年

作者： Zhang, Binbin Wu, Di Peng, Zhendong Song, Xingchen Yao, Zhuoyuan Lv, Hang Xie, Lei Yang, Chao Pan, Fuping Niu, Jianwei Horizon Robotics Beijing China School of Computer Science Northwestern Polytechnical University Xi'An China WeNet Open Source Community China

Recently, we made available WeNet [1], a production-oriented end-to-end speech recognition toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address the streaming and non-streaming decoding modes in a single model. To further improve ASR performance and facilitate various production requirements, in this paper, we present WeNet 2.0 with four important updates. (1) We propose U2++, a unified two-pass framework with bidirectional attention decoders, which includes the future contextual information by a right-to-left attention decoder to improve the representative ability of the shared encoder and the performance during the rescoring stage. (2) We introduce an n-gram based language model and a WFST-based decoder into WeNet 2.0, promoting the use of rich text data in production scenarios. (3) We design a unified contextual biasing framework, which leverages user-specific context (e.g., contact lists) to provide rapid adaptation ability for production and improves ASR accuracy in both with-LM and without-LM scenarios. (4) We design a unified IO to support large-scale data for effective model training. In summary, the brand-new WeNet 2.0 achieves up to 10% relative recognition performance improvement over the original WeNet on various corpora and makes available several important production-oriented features. © 2022, CC BY.

关键词： Speech recognition

FAST-U2++: FAST AND ACCURATE END-TO-END SPEECH RECOGNITION IN JOINT CTC/ATTENTION FRAMES

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arXiv 2022年

作者： Liang, Chengdong Zhang, Xiao-Lei Zhang, BinBin Wu, Di Li, Shengqiang Song, Xingchen Peng, Zhendong Pan, Fuping School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Horizon Robotics Beijing China WeNet Open Source Community China

Recently, the unified streaming and non-streaming two-pass (U2/U2++) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy and latency. In this paper, we present fast-U2++, an enhanced version of U2++ to further reduce partial latency. The core idea of fast-U2++ is to output partial results of the bottom layers in its encoder with a small chunk, while using a large chunk in the top layers of its encoder to compensate the performance degradation caused by the small chunk. Moreover, we use knowledge distillation method to reduce the token emission latency. We present extensive experiments on Aishell-1 dataset. Experiments and ablation studies show that compared to U2++, fast-U2++ reduces model latency from 320ms to 80ms, and achieves a character error rate (CER) of 5.06% with a streaming setup. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing

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arXiv 2025年

作者： Wang, Haoyang Xu, Jingao Luo, Xinyu Chen, Xuecheng Zhang, Ting Duan, Ruiyang Liu, Yunhao Chen, Xinlei Shenzhen International Graduate School Tsinghua University China Carnegie Mellon University United States Meituan Academy of Robotics Shenzhen China School of Software Tsinghua University China Pengcheng Laboratory Shenzhen China RISC-V International Open Source Laboratory Shenzhen China

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, the lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace the traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within the ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Extensive real-world experiments in landing scenarios from a leading drone delivery company demonstrate that mmE-Loc outperforms state-of-the-art methods in both localization accuracy and latency. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones