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Generative Pre-trained Transformer (GPT) models have achieved remarkable performance on various natural language processing tasks, and have shown great potential as backbones for audio-and-text large language models (LLMs).
The design for the wall street journal-based CSR corpus
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San-m: Memory equipped self-attention for end-to-end speech recognition
Zhifu Gao, Shiliang Zhang, Ming Lei, and Ian McLoughlin. 2020 · 2006
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A review of vector quantization techniques
A Vasuki and PT Vanathi. 2006 · 2006
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Yuma Koizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, and Kunio Kashino. 2020 · 2007
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Covost 2 and massively multilingual speech-to-text translation
Changhan Wang, Anne Wu, and Juan Pino. 2020 · 2007
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Iemocap: Interactive emotional dyadic motion capture database
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan. 2008 · 2008
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Slurp: A spoken language understanding resource package
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Crema-d: Crowd-sourced emotional multimodal actors dataset
Houwei Cao, David G Cooper, Michael K Keutmann, Ruben C Gur, Ani Nenkova, and Ragini Verma. 2014 · 2014
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Surrey audio-visual expressed emotion (savee) database
Philip Jackson and SJUoSG Haq. 2014 · 2014
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Librispeech: an asr corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. 2015 · 2015
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Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline
Hui Bu, Jiayu Du, Xingyu Na, Bengu Wu, and Hao Zheng. 2017 · 2017
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A study on data augmentation of reverberant speech for robust speech recognition
Tom Ko, Vijayaditya Peddinti, Daniel Povey, Michael L Seltzer, and Sanjeev Khudanpur. 2017 · 2017
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The emotional voices database: Towards controlling the emotion dimension in voice generation systems
Adaeze Adigwe, Noé Tits, Kevin El Haddad, Sarah Ostadabbas, and Thierry Dutoit. 2018 · 2018
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Aishell-2: Transforming mandarin asr research into industrial scale
Jiayu Du, Xingyu Na, Xuechen Liu, and Hui Bu. 2018 · 2018
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The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english
Steven R Livingstone and Frank A Russo. 2018 · 2018
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Meld: A multimodal multi-party dataset for emotion recognition in conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2018 · 2018
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Audiocaps: Generating captions for audios in the wild
Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, and Gunhee Kim. 2019 · 2019
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Clotho: An audio captioning dataset
Konstantinos Drossos, Samuel Lipping, and Tuomas Virtanen. 2020 · 2020
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Toronto emotional speech set (tess)
M Kathleen Pichora-Fuller and Kate Dupuis. 2020 · 2020
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Seanet: A multi-modal speech enhancement network
Marco Tagliasacchi, Yunpeng Li, Karolis Misiunas, and Dominik Roblek. 2020 · 2020
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Speechbrain: A general-purpose speech toolkit
Mirco Ravanelli, Titouan Parcollet, Peter Plantinga, Aku Rouhe, Samuele Cornell, Loren Lugosch, Cem Subakan, Nauman Dawalatabad, Abdelwahab Heba, Jianyuan Zhong, Ju-Chieh Chou, Sung-Lin Yeh, Szu-Wei Fu, Chien-Feng Liao, Elena Rastorgueva, François Grondin, William Aris, Hwidong Na, Yan Gao, Renato De Mori, and Yoshua Bengio. 2021 · 2021
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Bstc: A large-scale chinese-english speech translation dataset
Ruiqing Zhang, Xiyang Wang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Zhi Li, Haifeng Wang, Ying Chen, and Qinfei Li. 2021 · 2021
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Speecht5: Unified-modal encoder-decoder pre-training for spoken language processing
Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, and Furu Wei. 2022 · 2022
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CMGAN: Conformer-based Metric GAN for Speech Enhancement
Ruizhe Cao, Sherif Abdulatif, and Bin Yang. 2022 · 2022
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Pengi: An audio language model for audio tasks
Soham Deshmukh, Benjamin Elizalde, Rita Singh, and Huaming Wang. 2023 · 2023
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Funcodec: A fundamental, reproducible and integrable open-source toolkit for neural speech codec
Zhihao Du, Shiliang Zhang, Kai Hu, and Siqi Zheng. 2023 · 2023
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Funasr: A fundamental end-to-end speech recognition toolkit
Zhifu Gao, Zerui Li, Jiaming Wang, Haoneng Luo, Xian Shi, Mengzhe Chen, Yabin Li, Lingyun Zuo, Zhihao Du, Zhangyu Xiao, and Shiliang Zhang. 2023 · 2023
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Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models
Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, and Zhou Zhao. 2023a · 2023
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LMCodec: A low bitrate speech codec with causal transformer models
Teerapat Jenrungrot, Michael Chinen, W. Bastiaan Kleijn, and et al. 2023 · 2023
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Alexandre Défossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi. 2022 · 2022
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CLAP: learning audio concepts from natural language supervision
Benjamin Elizalde, Soham Deshmukh, Mahmoud Al Ismail, and Huaming Wang. 2022 · 2022
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FSD50K: an open dataset of human-labeled sound events
Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, and Xavier Serra. 2022 · 2022
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tiktoken: A fast BPE tokeniser for use with OpenAI’s models
Shantanu Jain. 2022 · 2022
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Findings of the 2022 conference on machine translation (wmt22)
Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, et al. 2022 · 2022
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Robust speech recognition via large-scale weak supervision
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. 2022 · 2022
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Learning to generalize to more: Continuous semantic augmentation for neural machine translation
Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Weihua Luo, and Rong Jin. 2022 · 2022
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Wenetspeech: A 10000+ hours multi-domain mandarin corpus for speech recognition
Binbin Zhang, Hang Lv, Pengcheng Guo, Qijie Shao, Chao Yang, Lei Xie, Xin Xu, Hui Bu, Xiaoyu Chen, Chenchen Zeng, et al. 2022 · 2022
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Audiogen: Textually guided audio generation
Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, and Yossi Adi. 2023 · 2023
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Lms with a voice: Spoken language modeling beyond speech tokens
Eliya Nachmani, Alon Levkovitch, Julian Salazar, Chulayuth Asawaroengchai, Soroosh Mariooryad, R. J. Skerry-Ryan, and Michelle Tadmor Ramanovich. 2023 · 2023
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OpenAI. 2023 · 2023
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Robust speech recognition via large-scale weak supervision
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Audiopalm: A large language model that can speak and listen
Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen, Ankur Bapna, Zalán Borsos, Félix de Chaumont Quitry, Peter Chen, Dalia El Badawy, Wei Han, Eugene Kharitonov, Hannah Muckenhirn, Dirk Padfield, James Qin, Danny Rozenberg, Tara N. Sainath, Johan Schalkwyk, Matthew Sharifi, Michelle Tadmor Ramanovich, Marco Tagliasacchi, Alexandru Tudor, Mihajlo Velimirovic, Damien Vincent, Jiahui Yu, Yongqiang Wang, Vicky Zayats, Neil Zeghidour, Yu Zhang, Zhishuai Zhang, Lukas Zilka, and Christian Havnø Frank. 2023 · 2023
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Hugginggpt: Solving AI tasks with chatgpt and its friends in huggingface
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023 · 2023
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SALMONN: towards generic hearing abilities for large language models
Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun Ma, and Chao Zhang. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Audiobox: Unified audio generation with natural language prompts
Apoorv Vyas, Bowen Shi, Matthew Le, Andros Tjandra, Yi-Chiao Wu, Baishan Guo, Jiemin Zhang, Xinyue Zhang, Robert Adkins, William Ngan, Jeff Wang, Ivan Cruz, Bapi Akula, Akinniyi Akinyemi, Brian Ellis, Rashel Moritz, Yael Yungster, Alice Rakotoarison, Liang Tan, Chris Summers, Carleigh Wood, Joshua Lane, Mary Williamson, and Wei-Ning Hsu. 2023 · 2023
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Speechgen: Unlocking the generative power of speech language models with prompts
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3d-speaker: A large-scale multi-device, multi-distance, and multi-dialect corpus for speech representation disentanglement
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Paraformer: Fast and accurate parallel transformer for non-autoregressive end-to-end speech recognition
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