Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Chen, Sanyuan"'
Autor:
Meng, Lingwei, Zhou, Long, Liu, Shujie, Chen, Sanyuan, Han, Bing, Hu, Shujie, Liu, Yanqing, Li, Jinyu, Zhao, Sheng, Wu, Xixin, Meng, Helen, Wei, Furu
We present MELLE, a novel continuous-valued tokens based language modeling approach for text to speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram frames directly from text condition, bypassing the need for vector qua
Externí odkaz:
http://arxiv.org/abs/2407.08551
Autor:
Han, Bing, Zhou, Long, Liu, Shujie, Chen, Sanyuan, Meng, Lingwei, Qian, Yanming, Liu, Yanqing, Zhao, Sheng, Li, Jinyu, Wei, Furu
With the help of discrete neural audio codecs, large language models (LLM) have increasingly been recognized as a promising methodology for zero-shot Text-to-Speech (TTS) synthesis. However, sampling based decoding strategies bring astonishing divers
Externí odkaz:
http://arxiv.org/abs/2406.07855
Autor:
Chen, Sanyuan, Liu, Shujie, Zhou, Long, Liu, Yanqing, Tan, Xu, Li, Jinyu, Zhao, Sheng, Qian, Yao, Wei, Furu
This paper introduces VALL-E 2, the latest advancement in neural codec language models that marks a milestone in zero-shot text-to-speech synthesis (TTS), achieving human parity for the first time. Based on its predecessor, VALL-E, the new iteration
Externí odkaz:
http://arxiv.org/abs/2406.05370
Autor:
Hu, Shujie, Zhou, Long, Liu, Shujie, Chen, Sanyuan, Hao, Hongkun, Pan, Jing, Liu, Xunying, Li, Jinyu, Sivasankaran, Sunit, Liu, Linquan, Wei, Furu
The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception and generation. However, effectively integrating listening capabilitie
Externí odkaz:
http://arxiv.org/abs/2404.00656
Autor:
Wang, Xiaofei, Thakker, Manthan, Chen, Zhuo, Kanda, Naoyuki, Eskimez, Sefik Emre, Chen, Sanyuan, Tang, Min, Liu, Shujie, Li, Jinyu, Yoshioka, Takuya
Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models still face limitations in handling diverse audio-text speech generati
Externí odkaz:
http://arxiv.org/abs/2308.06873
Autor:
Zhang, Ziqiang, Zhou, Long, Wang, Chengyi, Chen, Sanyuan, Wu, Yu, Liu, Shujie, Chen, Zhuo, Liu, Yanqing, Wang, Huaming, Li, Jinyu, He, Lei, Zhao, Sheng, Wei, Furu
We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis. Specifically, we extend VALL-E and train a multi-lingual conditional codec language model to predict the acoustic token sequences of the target lang
Externí odkaz:
http://arxiv.org/abs/2303.03926
Autor:
Wang, Chengyi, Chen, Sanyuan, Wu, Yu, Zhang, Ziqiang, Zhou, Long, Liu, Shujie, Chen, Zhuo, Liu, Yanqing, Wang, Huaming, Li, Jinyu, He, Lei, Zhao, Sheng, Wei, Furu
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a condit
Externí odkaz:
http://arxiv.org/abs/2301.02111
The massive growth of self-supervised learning (SSL) has been witnessed in language, vision, speech, and audio domains over the past few years. While discrete label prediction is widely adopted for other modalities, the state-of-the-art audio SSL mod
Externí odkaz:
http://arxiv.org/abs/2212.09058
Self-supervised speech pre-training empowers the model with the contextual structure inherent in the speech signal while self-supervised text pre-training empowers the model with linguistic information. Both of them are beneficial for downstream spee
Externí odkaz:
http://arxiv.org/abs/2211.13443
Autor:
Song, Hyungchan, Chen, Sanyuan, Chen, Zhuo, Wu, Yu, Yoshioka, Takuya, Tang, Min, Shin, Jong Won, Liu, Shujie
There is a surge in interest in self-supervised learning approaches for end-to-end speech encoding in recent years as they have achieved great success. Especially, WavLM showed state-of-the-art performance on various speech processing tasks. To bette
Externí odkaz:
http://arxiv.org/abs/2211.09988