Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion

Anand Kamble, Aniket Tathe, Suyash Kumbharkar, Atharva Bhandare, Anirban C. Mitra
11/24/2023
cs.SDcs.CLeess.AS

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Abstract

This paper proposes two innovative methodologies to construct customized Common Voice datasets for low-resource languages like Hindi. The first methodology leverages Bark, a transformer-based text-to-audio model developed by Suno, and incorporates Meta's enCodec and a pre-trained HuBert model to enhance Bark's performance. The second methodology employs Retrieval-Based Voice Conversion (RVC) and uses the Ozen toolkit for data preparation. Both methodologies contribute to the advancement of ASR technology and offer valuable insights into addressing the challenges of constructing customized Common Voice datasets for under-resourced languages. Furthermore, they provide a pathway to achieving high-quality, personalized voice generation for a range of applications.

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Cite this paper

@article{kamble2023custom,
  title  = {Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion},
  author = {Anand Kamble and Aniket Tathe and Suyash Kumbharkar and Atharva Bhandare and Anirban C. Mitra},
  year   = {2023},
  eprint = {2311.14836},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.14836v3}
}

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