FlashSpeech: Efficient Zero-Shot Speech Synthesis
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913561216811008 |
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| author | Ye, Zhen Ju, Zeqian Liu, Haohe Tan, Xu Chen, Jianyi Lu, Yiwen Sun, Peiwen Pan, Jiahao Bian, Weizhen He, Shulin Xue, Wei Liu, Qifeng Guo, Yike |
| author_facet | Ye, Zhen Ju, Zeqian Liu, Haohe Tan, Xu Chen, Jianyi Lu, Yiwen Sun, Peiwen Pan, Jiahao Bian, Weizhen He, Shulin Xue, Wei Liu, Qifeng Guo, Yike |
| contents | Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using a lower computing budget to achieve quality on par with previous work remains a significant challenge. In this paper, we present FlashSpeech, a large-scale zero-shot speech synthesis system with approximately 5\% of the inference time compared with previous work. FlashSpeech is built on the latent consistency model and applies a novel adversarial consistency training approach that can train from scratch without the need for a pre-trained diffusion model as the teacher. Furthermore, a new prosody generator module enhances the diversity of prosody, making the rhythm of the speech sound more natural. The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation. Our experimental results demonstrate the superior performance of FlashSpeech. Notably, FlashSpeech can be about 20 times faster than other zero-shot speech synthesis systems while maintaining comparable performance in terms of voice quality and similarity. Furthermore, FlashSpeech demonstrates its versatility by efficiently performing tasks like voice conversion, speech editing, and diverse speech sampling. Audio samples can be found in https://flashspeech.github.io/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_14700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FlashSpeech: Efficient Zero-Shot Speech Synthesis Ye, Zhen Ju, Zeqian Liu, Haohe Tan, Xu Chen, Jianyi Lu, Yiwen Sun, Peiwen Pan, Jiahao Bian, Weizhen He, Shulin Xue, Wei Liu, Qifeng Guo, Yike Audio and Speech Processing Artificial Intelligence Computation and Language Machine Learning Sound Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using a lower computing budget to achieve quality on par with previous work remains a significant challenge. In this paper, we present FlashSpeech, a large-scale zero-shot speech synthesis system with approximately 5\% of the inference time compared with previous work. FlashSpeech is built on the latent consistency model and applies a novel adversarial consistency training approach that can train from scratch without the need for a pre-trained diffusion model as the teacher. Furthermore, a new prosody generator module enhances the diversity of prosody, making the rhythm of the speech sound more natural. The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation. Our experimental results demonstrate the superior performance of FlashSpeech. Notably, FlashSpeech can be about 20 times faster than other zero-shot speech synthesis systems while maintaining comparable performance in terms of voice quality and similarity. Furthermore, FlashSpeech demonstrates its versatility by efficiently performing tasks like voice conversion, speech editing, and diverse speech sampling. Audio samples can be found in https://flashspeech.github.io/. |
| title | FlashSpeech: Efficient Zero-Shot Speech Synthesis |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Machine Learning Sound |
| url | https://arxiv.org/abs/2404.14700 |