VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware 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_ | 1866916543498027008 |
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| author | Jung, Jaemin Ahn, Junseok Jung, Chaeyoung Nguyen, Tan Dat Jang, Youngjoon Chung, Joon Son |
| author_facet | Jung, Jaemin Ahn, Junseok Jung, Chaeyoung Nguyen, Tan Dat Jang, Youngjoon Chung, Joon Son |
| contents | We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for intelligible speech, achieving this alignment in noisy conditions remains a significant and underexplored challenge in the field. To address this, we present a novel audio generation pipeline named VoiceDiT. This pipeline includes three key components: (1) the creation of a large-scale synthetic speech dataset for pre-training and a refined real-world speech dataset for fine-tuning, (2) the Dual-DiT, a model designed to efficiently preserve aligned speech information while accurately reflecting environmental conditions, and (3) a diffusion-based Image-to-Audio Translator that allows the model to bridge the gap between audio and image, facilitating the generation of environmental sound that aligns with the multi-modal prompts. Extensive experimental results demonstrate that VoiceDiT outperforms previous models on real-world datasets, showcasing significant improvements in both audio quality and modality integration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis Jung, Jaemin Ahn, Junseok Jung, Chaeyoung Nguyen, Tan Dat Jang, Youngjoon Chung, Joon Son Audio and Speech Processing Sound We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for intelligible speech, achieving this alignment in noisy conditions remains a significant and underexplored challenge in the field. To address this, we present a novel audio generation pipeline named VoiceDiT. This pipeline includes three key components: (1) the creation of a large-scale synthetic speech dataset for pre-training and a refined real-world speech dataset for fine-tuning, (2) the Dual-DiT, a model designed to efficiently preserve aligned speech information while accurately reflecting environmental conditions, and (3) a diffusion-based Image-to-Audio Translator that allows the model to bridge the gap between audio and image, facilitating the generation of environmental sound that aligns with the multi-modal prompts. Extensive experimental results demonstrate that VoiceDiT outperforms previous models on real-world datasets, showcasing significant improvements in both audio quality and modality integration. |
| title | VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2412.19259 |