泰国Wav2Vec2，带CommonVoice V8
Thai Wav2Vec2.0 with CommonVoice V8
摘要：
最近，自动语音识别（ASR），一种将音频转换为文本的系统，在机器学习社区引起了广泛的关注。因此，许多公开可用的模型在HuggingFace中发布。然而，这些ASR模型中的大多数都有英语版本;只有少数模型有泰语版本。此外，大多数泰国ASR模型都是闭源的，现有开源模型的性能缺乏鲁棒性。为了解决这个问题，我们训练一个新的ASR模型上预先训练XLSR-Wav 2 Vec模型与泰国CommonVoice语料库V8和训练一个三元组语言模型，以提高我们的ASR模型的性能。我们希望我们的模型将有利于个人和泰国的ASR社区。
英文摘要：
Recently, Automatic Speech Recognition (ASR), a system that converts audio into text, has caught a lot of attention in the machine learning community. Thus, a lot of publicly available models were released in HuggingFace. However, most of these ASR models are available in English; only a minority of the models are available in Thai. Additionally, most of the Thai ASR models are closed-sourced, and the performance of existing open-sourced models lacks robustness. To address this problem, we train a new ASR model on a pre-trained XLSR-Wav2Vec model with the Thai CommonVoice corpus V8 and train a trigram language model to boost the performance of our ASR model. We hope that our models will be beneficial to individuals and the ASR community in Thailand.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
