no code implementations • 22 Feb 2024 • Zechun Liu, Changsheng Zhao, Forrest Iandola, Chen Lai, Yuandong Tian, Igor Fedorov, Yunyang Xiong, Ernie Chang, Yangyang Shi, Raghuraman Krishnamoorthi, Liangzhen Lai, Vikas Chandra
The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0. 7%/0. 8% than MobileLLM 125M/350M.
no code implementations • 20 Feb 2024 • Yang Li, Yuan Shangguan, Yuhao Wang, Liangzhen Lai, Ernie Chang, Changsheng Zhao, Yangyang Shi, Vikas Chandra
This study delves into how weight parameters in speech recognition models influence the overall power consumption of these models.
no code implementations • 8 Jan 2024 • Yang Liu, Li Wan, Yun Li, Yiteng Huang, Ming Sun, James Luan, Yangyang Shi, Xin Lei
Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted.
no code implementations • 1 Nov 2023 • Ernie Chang, Sidd Srinivasan, Mahi Luthra, Pin-Jie Lin, Varun Nagaraja, Forrest Iandola, Zechun Liu, Zhaoheng Ni, Changsheng Zhao, Yangyang Shi, Vikas Chandra
Text-to-audio generation (TTA) produces audio from a text description, learning from pairs of audio samples and hand-annotated text.
no code implementations • 1 Nov 2023 • Ernie Chang, Pin-Jie Lin, Yang Li, Sidd Srinivasan, Gael Le Lan, David Kant, Yangyang Shi, Forrest Iandola, Vikas Chandra
We show that the framework enhanced the audio quality across the set of collected user prompts, which were edited with reference to the training captions as exemplars.
1 code implementation • 27 Oct 2023 • Jeff Hwang, Moto Hira, Caroline Chen, Xiaohui Zhang, Zhaoheng Ni, Guangzhi Sun, Pingchuan Ma, Ruizhe Huang, Vineel Pratap, Yuekai Zhang, Anurag Kumar, Chin-Yun Yu, Chuang Zhu, Chunxi Liu, Jacob Kahn, Mirco Ravanelli, Peng Sun, Shinji Watanabe, Yangyang Shi, Yumeng Tao, Robin Scheibler, Samuele Cornell, Sean Kim, Stavros Petridis
TorchAudio is an open-source audio and speech processing library built for PyTorch.
no code implementations • 19 Sep 2023 • Zhaoheng Ni, Sravya Popuri, Ning Dong, Kohei Saijo, Xiaohui Zhang, Gael Le Lan, Yangyang Shi, Vikas Chandra, Changhan Wang
High-quality and intelligible speech is essential to text-to-speech (TTS) model training, however, obtaining high-quality data for low-resource languages is challenging and expensive.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 19 Sep 2023 • Xinhao Mei, Varun Nagaraja, Gael Le Lan, Zhaoheng Ni, Ernie Chang, Yangyang Shi, Vikas Chandra
A prevalent problem in V2A generation is the misalignment of generated audio with the visible actions in the video.
no code implementations • 15 Sep 2023 • Gael Le Lan, Varun Nagaraja, Ernie Chang, David Kant, Zhaoheng Ni, Yangyang Shi, Forrest Iandola, Vikas Chandra
In language modeling based music generation, a generated waveform is represented by a sequence of hierarchical token stacks that can be decoded either in an auto-regressive manner or in parallel, depending on the codebook patterns.
no code implementations • 15 Sep 2023 • Yangyang Shi, Gael Le Lan, Varun Nagaraja, Zhaoheng Ni, Xinhao Mei, Ernie Chang, Forrest Iandola, Yang Liu, Vikas Chandra
This paper presents an innovative approach to enhance control over audio generation by emphasizing the alignment between audio and text representations during model training.
no code implementations • 14 Sep 2023 • Yang Li, Liangzhen Lai, Yuan Shangguan, Forrest N. Iandola, Zhaoheng Ni, Ernie Chang, Yangyang Shi, Vikas Chandra
Instead, the bottleneck lies in the linear projection layers of multi-head attention and feedforward networks, constituting a substantial portion of the model size and contributing significantly to computation, memory, and power usage.
no code implementations • 25 Aug 2023 • Mei-Yuh Hwang, Yangyang Shi, Ankit Ramchandani, Guan Pang, Praveen Krishnan, Lucas Kabela, Frank Seide, Samyak Datta, Jun Liu
This paper discusses the challenges of optical character recognition (OCR) on natural scenes, which is harder than OCR on documents due to the wild content and various image backgrounds.
1 code implementation • 1 Jul 2023 • Ernie Chang, Muhammad Hassan Rashid, Pin-Jie Lin, Changsheng Zhao, Vera Demberg, Yangyang Shi, Vikas Chandra
Knowing exactly how many data points need to be labeled to achieve a certain model performance is a hugely beneficial step towards reducing the overall budgets for annotation.
1 code implementation • 2 Jun 2023 • Zechun Liu, Barlas Oguz, Aasish Pappu, Yangyang Shi, Raghuraman Krishnamoorthi
For machine translation, we achieved BLEU scores of 21. 7 and 17. 6 on the WMT16 En-Ro benchmark, compared with a full precision mBART model score of 26. 8.
no code implementations • 29 May 2023 • Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra
Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits.
no code implementations • 21 May 2023 • Yassir Fathullah, Chunyang Wu, Yuan Shangguan, Junteng Jia, Wenhan Xiong, Jay Mahadeokar, Chunxi Liu, Yangyang Shi, Ozlem Kalinli, Mike Seltzer, Mark J. F. Gales
State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches.
Ranked #8 on Speech Recognition on LibriSpeech test-clean
no code implementations • 15 Dec 2022 • Ke Li, Jay Mahadeokar, Jinxi Guo, Yangyang Shi, Gil Keren, Ozlem Kalinli, Michael L. Seltzer, Duc Le
Experiments on Librispeech and in-house data show relative WER reductions (WERRs) from 3% to 5% with a slight increase in model size and negligible extra token emission latency compared with fast-slow encoder based transducer.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 9 Nov 2022 • Haichuan Yang, Zhaojun Yang, Li Wan, Biqiao Zhang, Yangyang Shi, Yiteng Huang, Ivaylo Enchev, Limin Tang, Raziel Alvarez, Ming Sun, Xin Lei, Raghuraman Krishnamoorthi, Vikas Chandra
This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting.
no code implementations • 4 Nov 2022 • Florian L. Kreyssig, Yangyang Shi, Jinxi Guo, Leda Sari, Abdelrahman Mohamed, Philip C. Woodland
Furthermore, this paper proposes a variant of MPPT that allows low-footprint streaming models to be trained effectively by computing the MPPT loss on masked and unmasked frames.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 1 Nov 2022 • Yang Liu, Yangyang Shi, Yun Li, Kaustubh Kalgaonkar, Sriram Srinivasan, Xin Lei
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation.
no code implementations • 25 Jul 2022 • Chunxi Liu, Yuan Shangguan, Haichuan Yang, Yangyang Shi, Raghuraman Krishnamoorthi, Ozlem Kalinli
There is growing interest in unifying the streaming and full-context automatic speech recognition (ASR) networks into a single end-to-end ASR model to simplify the model training and deployment for both use cases.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 29 Mar 2022 • Jay Mahadeokar, Yangyang Shi, Ke Li, Duc Le, Jiedan Zhu, Vikas Chandra, Ozlem Kalinli, Michael L Seltzer
Streaming ASR with strict latency constraints is required in many speech recognition applications.
2 code implementations • 28 Oct 2021 • Yao-Yuan Yang, Moto Hira, Zhaoheng Ni, Anjali Chourdia, Artyom Astafurov, Caroline Chen, Ching-Feng Yeh, Christian Puhrsch, David Pollack, Dmitriy Genzel, Donny Greenberg, Edward Z. Yang, Jason Lian, Jay Mahadeokar, Jeff Hwang, Ji Chen, Peter Goldsborough, Prabhat Roy, Sean Narenthiran, Shinji Watanabe, Soumith Chintala, Vincent Quenneville-Bélair, Yangyang Shi
This document describes version 0. 10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain.
no code implementations • 7 Oct 2021 • Dawei Liang, Yangyang Shi, Yun Wang, Nayan Singhal, Alex Xiao, Jonathan Shaw, Edison Thomaz, Ozlem Kalinli, Mike Seltzer
Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life.
no code implementations • 7 Oct 2021 • Yangyang Shi, Chunyang Wu, Dilin Wang, Alex Xiao, Jay Mahadeokar, Xiaohui Zhang, Chunxi Liu, Ke Li, Yuan Shangguan, Varun Nagaraja, Ozlem Kalinli, Mike Seltzer
This paper improves the streaming transformer transducer for speech recognition by using non-causal convolution.
no code implementations • 9 Jul 2021 • Xiaohui Zhang, Vimal Manohar, David Zhang, Frank Zhang, Yangyang Shi, Nayan Singhal, Julian Chan, Fuchun Peng, Yatharth Saraf, Mike Seltzer
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 16 Jun 2021 • Varun Nagaraja, Yangyang Shi, Ganesh Venkatesh, Ozlem Kalinli, Michael L. Seltzer, Vikas Chandra
On-device speech recognition requires training models of different sizes for deploying on devices with various computational budgets.
no code implementations • 6 Apr 2021 • Yuan Shangguan, Rohit Prabhavalkar, Hang Su, Jay Mahadeokar, Yangyang Shi, Jiatong Zhou, Chunyang Wu, Duc Le, Ozlem Kalinli, Christian Fuegen, Michael L. Seltzer
As speech-enabled devices such as smartphones and smart speakers become increasingly ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems that can run directly on-device; end-to-end (E2E) speech recognition models such as recurrent neural network transducers and their variants have recently emerged as prime candidates for this task.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 6 Apr 2021 • Jay Mahadeokar, Yangyang Shi, Yuan Shangguan, Chunyang Wu, Alex Xiao, Hang Su, Duc Le, Ozlem Kalinli, Christian Fuegen, Michael L. Seltzer
In order to achieve flexible and better accuracy and latency trade-offs, the following techniques are used.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 5 Apr 2021 • Duc Le, Mahaveer Jain, Gil Keren, Suyoun Kim, Yangyang Shi, Jay Mahadeokar, Julian Chan, Yuan Shangguan, Christian Fuegen, Ozlem Kalinli, Yatharth Saraf, Michael L. Seltzer
How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area.
no code implementations • 5 Apr 2021 • Yangyang Shi, Varun Nagaraja, Chunyang Wu, Jay Mahadeokar, Duc Le, Rohit Prabhavalkar, Alex Xiao, Ching-Feng Yeh, Julian Chan, Christian Fuegen, Ozlem Kalinli, Michael L. Seltzer
DET gets similar accuracy as a baseline model with better latency on a large in-house data set by assigning a lightweight encoder for the beginning part of one utterance and a full-size encoder for the rest.
no code implementations • 3 Nov 2020 • Ching-Feng Yeh, Yongqiang Wang, Yangyang Shi, Chunyang Wu, Frank Zhang, Julian Chan, Michael L. Seltzer
Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 27 Oct 2020 • Yongqiang Wang, Yangyang Shi, Frank Zhang, Chunyang Wu, Julian Chan, Ching-Feng Yeh, Alex Xiao
We compare the transformer based acoustic models with their LSTM counterparts on industrial scale tasks.
1 code implementation • 21 Oct 2020 • Yangyang Shi, Yongqiang Wang, Chunyang Wu, Ching-Feng Yeh, Julian Chan, Frank Zhang, Duc Le, Mike Seltzer
For a low latency scenario with an average latency of 80 ms, Emformer achieves WER $3. 01\%$ on test-clean and $7. 09\%$ on test-other.
no code implementations • 18 May 2020 • Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 16 May 2020 • Chunyang Wu, Yongqiang Wang, Yangyang Shi, Ching-Feng Yeh, Frank Zhang
The memory bankstores the embedding information for all the processed seg-ments.
no code implementations • 8 Apr 2019 • Yangyang Shi, Mei-Yuh Hwang, Xin Lei, Haoyu Sheng
Using knowledge distillation with trust regularization, we reduce the parameter size to a third of that of the previously published best model while maintaining the state-of-the-art perplexity result on Penn Treebank data.
1 code implementation • 12 Mar 2019 • Yangyang Shi, Mei-Yuh Hwang, Xin Lei
In this paper, we propose to use a high rank projection layer to replace the projection matrix.