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We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech (TTS) models capable of generating speech that is virtually indistinguishable from human speech.
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LibriSpeech: an ASR corpus based on public domain audio books
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Arsha Nagrani, Joon Son Chung, and Andrew Zisserman · 2017
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Minimum word error rate training for attention-based sequence-to-sequence models
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AutoVC: Zero-shot voice style transfer with only autoencoder loss
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FastSpeech: Fast, robust and controllable text to speech
Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu · 2019
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Common Voice: A massively-multilingual speech corpus
Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, and Gregor Weber · 2019
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Singing voice synthesis using deep autoregressive neural networks for acoustic modeling
Yuan-Hao Yi, Yang Ai, Zhen-Hua Ling, and Li-Rong Dai · 2019
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FastSpeech 2: Fast and high-quality end-to-end text to speech
Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu · 2020
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Jian Cong, Shan Yang, Lei Xie, and Dan Su · 2021
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Basis-MelGAN: Efficient neural vocoder based on audio decomposition
Zhengxi Liu and Yanmin Qian · 2021
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DiDiSpeech: A large scale mandarin speech corpus
Tingwei Guo, Cheng Wen, Dongwei Jiang, Ne Luo, Ruixiong Zhang, Shuaijiang Zhao, Wubo Li, Cheng Gong, Wei Zou, Kun Han, et al · 2021
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LiteSing: Towards fast, lightweight and expressive singing voice synthesis
Xiaobin Zhuang, Tao Jiang, Szu-Yu Chou, Bin Wu, Peng Hu, and Simon Lui · 2021
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Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yelysei Bondarenko, Mart Van Baalen, and Tijmen Blankevoort · 2021
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Any-to-many voice conversion with location-relative sequence-to-sequence modeling
Songxiang Liu, Yuewen Cao, Disong Wang, Xixin Wu, Xunying Liu, and Helen Meng · 2021
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Diffusion-based voice conversion with fast maximum likelihood sampling scheme
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Kudinov, and Jiansheng Wei · 2021
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NaturalSpeech: End-to-end text to speech synthesis with human-level quality, 2022
Xu Tan, Jiawei Chen, Haohe Liu, Jian Cong, Chen Zhang, Yanqing Liu, Xi Wang, Yichong Leng, Yuanhao Yi, Lei He, Frank Soong, Tao Qin, Sheng Zhao, and Tie-Yan Liu · 2022
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Yuanhao Yi, Lei He, Shifeng Pan, Xi Wang, and Yujia Xiao · 2022
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BigVGAN: A universal neural vocoder with large-scale training
Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, and Sungroh Yoon · 2022
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AudioLM: A language modeling approach to audio generation, 2022
Borsos Zalán, Marinier Raphaël, Vincent Damien, Kharitonov Eugene, Pietquin Olivier, Sharifi Matt, Teboul Olivier, Tagliasacchi Marco, and Zeghidour Neil · 2022
AWQ: Activation-aware weight quantization for llm compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Efficient memory management for large language model serving with pagedattention
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Tri Dao · 2023
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Large-scale self-supervised speech representation learning for automatic speaker verification
Zhengyang Chen, Sanyuan Chen, Yu Wu, Yao Qian, Chengyi Wang, Shujie Liu, Yanmin Qian, and Michael Zeng · 2022
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Controllable and lossless non-autoregressive end-to-end text-to-speech
Zhengxi Liu, Qiao Tian, Chenxu Hu, Xudong Liu, Menglin Wu, Yuping Wang, Hang Zhao, and Yuxuan Wang · 2022
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WeNet 2.0: More productive end-to-end speech recognition toolkit
Binbin Zhang, Di Wu, Zhendong Peng, Xingchen Song, Zhuoyuan Yao, Hang Lv, Lei Xie, Chao Yang, Fuping Pan, and Jianwei Niu · 2022
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Squeezeformer: An efficient transformer for automatic speech recognition
Sehoon Kim, Amir Gholami, Albert Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W Mahoney, and Kurt Keutzer · 2022
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FlashAttention: Fast and memory-efficient exact attention with IO-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Zero-shot accent conversion using pseudo siamese disentanglement network
Dongya Jia, Qiao Tian, Kainan Peng, Jiaxin Li, Yuanzhe Chen, Mingbo Ma, Yuping Wang, and Yuxuan Wang · 2022
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Streaming voice conversion via intermediate bottleneck features and non-streaming teacher guidance
Yuanzhe Chen, Ming Tu, Tang Li, Xin Li, Qiuqiang Kong, Jiaxin Li, Zhichao Wang, Qiao Tian, Yuping Wang, and Yuxuan Wang · 2023
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Sang-Hoon Lee, Ha-Yeong Choi, Seung-Bin Kim, and Seong-Whan Lee · 2023
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Diffusion model alignment using direct preference optimization
Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik · 2023
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Language model beats diffusion - tokenizer is key to visual generation
Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al · 2023
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NaturalSpeech 3: Zero-shot speech synthesis with factorized codec and diffusion models, 2024
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BASE TTS: Lessons from building a billion-parameter text-to-speech model on 100k hours of data
Mateusz Łajszczak, Guillermo Cámbara, Yang Li, Fatih Beyhan, Arent van Korlaar, Fan Yang, Arnaud Joly, Álvaro Martín-Cortinas, Ammar Abbas, Adam Michalski, et al · 2024
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Voicebox: Text-guided multilingual universal speech generation at scale
Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, et al · 2024
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UniCATS: A unified context-aware text-to-speech framework with contextual VQ-diffusion and vocoding
Chenpeng Du, Yiwei Guo, Feiyu Shen, Zhijun Liu, Zheng Liang, Xie Chen, Shuai Wang, Hui Zhang, and Kai Yu · 2024
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High-fidelity audio compression with improved RVQGAN
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ELLA-V: Stable neural codec language modeling with alignment-guided sequence reordering
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Scaling rectified flow transformers for high-resolution image synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al · 2024
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VoiceShop: A unified speech-to-speech framework for identity-preserving zero-shot voice editing
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DDDM-VC: Decoupled denoising diffusion models with disentangled representation and prior mixup for verified robust voice conversion
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Direct preference optimization: Your language model is secretly a reward model
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SpeechAlign: Aligning speech generation to human preferences
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Back to basics: Revisiting REINFORCE style optimization for learning from human feedback in LLMs
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