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.

本文提出了两种创新方法，用于为印地语等低资源语言构建定制的 Common Voice 数据集。第一种方法利用 Suno 开发的基于 Transformer 的文本到音频模型 Bark，结合 Meta 的 enCodec 和预训练的 HuBert 模型来增强 Bark 的性能。第二种方法采用检索式语音转换（RVC），并使用 Ozen 工具包进行数据准备。这两种方法为 ASR 技术的发展做出了贡献，并提供了有价值的见解，以解决构建低资源语言定制 Common Voice 数据集的挑战。此外，它们为实现高质量个性化语音生成提供了途径，适用于各种应用场景。