论文标题
Vakyansh：低资源指示语言的ASR工具包
Vakyansh: ASR Toolkit for Low Resource Indic languages
论文作者
论文摘要
我们提出了Vakyansh，这是一种用指示语言识别语音识别的端到端工具包。印度拥有近121种语言和大约125亿扬声器。然而，大多数语言就数据和预算模型而言都是低资源。通过Vakyansh，我们介绍了用于数据创建，模型培训，模型评估和部署的自动数据管道。我们以23种指示语言和Train Wav2Vec 2.0预验证的模型创建14,000小时的语音数据。然后，对这些预审预告添加的模型进行了填充，以创建18个指示语言的最先进的语音识别模型，其次是语言模型和标点符号恢复模型。我们以使命开源所有这些资源，这将激发语音社区使用Indic语言使用我们的ASR模型开发语音的首次应用程序。
We present Vakyansh, an end to end toolkit for Speech Recognition in Indic languages. India is home to almost 121 languages and around 125 crore speakers. Yet most of the languages are low resource in terms of data and pretrained models. Through Vakyansh, we introduce automatic data pipelines for data creation, model training, model evaluation and deployment. We create 14,000 hours of speech data in 23 Indic languages and train wav2vec 2.0 based pretrained models. These pretrained models are then finetuned to create state of the art speech recognition models for 18 Indic languages which are followed by language models and punctuation restoration models. We open source all these resources with a mission that this will inspire the speech community to develop speech first applications using our ASR models in Indic languages.
