Lightweight Zero-shot Text-to-Speech with Mixture of Adapters

• URL: http://arxiv.org/abs/2407.01291v1
• Date: Mon, 1 Jul 2024 13:45:31 GMT
• Title: Lightweight Zero-shot Text-to-Speech with Mixture of Adapters
• Authors: Kenichi Fujita, Takanori Ashihara, Marc Delcroix, Yusuke Ijima,
• Abstract summary: We propose a lightweight zero-shot text-to-speech (TTS) method using a mixture of adapters (MoA) Our proposed method incorporates MoA modules into the decoder and the variance adapter of a non-autoregressive TTS model. Our method achieves high-quality speech synthesis with minimal additional parameters.
• Score: 36.29364245236912
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The advancements in zero-shot text-to-speech (TTS) methods, based on large-scale models, have demonstrated high fidelity in reproducing speaker characteristics. However, these models are too large for practical daily use. We propose a lightweight zero-shot TTS method using a mixture of adapters (MoA). Our proposed method incorporates MoA modules into the decoder and the variance adapter of a non-autoregressive TTS model. These modules enhance the ability to adapt a wide variety of speakers in a zero-shot manner by selecting appropriate adapters associated with speaker characteristics on the basis of speaker embeddings. Our method achieves high-quality speech synthesis with minimal additional parameters. Through objective and subjective evaluations, we confirmed that our method achieves better performance than the baseline with less than 40\% of parameters at 1.9 times faster inference speed. Audio samples are available on our demo page (https://ntt-hilab-gensp.github.io/is2024lightweightTTS/).

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