{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adapter-based-extension-of-multi-speaker-text","title":"Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers","arxiv_id":"2211.00585","date":"2022-11-01","proceeding":null,"authors":["Cheng-Ping Hsieh","Subhankar Ghosh","Boris Ginsburg"],"abstract":"Fine-tuning is a popular method for adapting text-to-speech (TTS) models to new speakers. However this approach has some challenges. Usually fine-tuning requires several hours of high quality speech per speaker. There is also that fine-tuning will negatively affect the quality of speech synthesis for previously learnt speakers. In this paper we propose an alternative approach for TTS adaptation based on using parameter-efficient adapter modules. In the proposed approach, a few small adapter modules are added to the original network. The original weights are frozen, and only the adapters are fine-tuned on speech for new speaker. The parameter-efficient fine-tuning approach will produce a new model with high level of parameter sharing with original model. Our experiments on LibriTTS, HiFi-TTS and VCTK datasets validate the effectiveness of adapter-based method through objective and subjective metrics.","url_abs":"https://arxiv.org/abs/2211.00585v1","url_pdf":"https://arxiv.org/pdf/2211.00585v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adapter-based-extension-of-multi-speaker-text","repo_url":"https://github.com/NVIDIA/NeMo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"},{"task_slug":"text-to-speech","task_name":"Text to Speech"},{"task_slug":"parameter-efficient-fine-tuning","task_name":"parameter-efficient fine-tuning"},{"task_slug":"text-to-speech-1","task_name":"text-to-speech"}],"methods":[{"method_slug":"adapter","method_name":"Adapter"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}