Towards General-Purpose Text-Instruction-Guided Voice Conversion
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913198043561984 |
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| author | Kuan, Chun-Yi Li, Chen An Hsu, Tsu-Yuan Lin, Tse-Yang Chung, Ho-Lam Chang, Kai-Wei Chang, Shuo-yiin Lee, Hung-yi |
| author_facet | Kuan, Chun-Yi Li, Chen An Hsu, Tsu-Yuan Lin, Tse-Yang Chung, Ho-Lam Chang, Kai-Wei Chang, Shuo-yiin Lee, Hung-yi |
| contents | This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted speech, our model adds versatility and specificity to voice conversion. The proposed VC model is a neural codec language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech. It utilizes text instructions as style prompts to modify the prosody and emotional information of the given speech. In contrast to previous approaches, which often rely on employing separate encoders like prosody and content encoders to handle different aspects of the source speech, our model handles various information of speech in an end-to-end manner. Experiments have demonstrated the impressive capabilities of our model in comprehending instructions and delivering reasonable results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_14324 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Towards General-Purpose Text-Instruction-Guided Voice Conversion Kuan, Chun-Yi Li, Chen An Hsu, Tsu-Yuan Lin, Tse-Yang Chung, Ho-Lam Chang, Kai-Wei Chang, Shuo-yiin Lee, Hung-yi Audio and Speech Processing Computation and Language Machine Learning Sound This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted speech, our model adds versatility and specificity to voice conversion. The proposed VC model is a neural codec language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech. It utilizes text instructions as style prompts to modify the prosody and emotional information of the given speech. In contrast to previous approaches, which often rely on employing separate encoders like prosody and content encoders to handle different aspects of the source speech, our model handles various information of speech in an end-to-end manner. Experiments have demonstrated the impressive capabilities of our model in comprehending instructions and delivering reasonable results. |
| title | Towards General-Purpose Text-Instruction-Guided Voice Conversion |
| topic | Audio and Speech Processing Computation and Language Machine Learning Sound |
| url | https://arxiv.org/abs/2309.14324 |