In this project, the main focus has been on preparing a clean dataset and training models for automatic recognition of Persian speech. Three methods for creating the dataset have been investigated.
One of these methods has been the use of open-text audio and text resources to train the model and create a data cleaning pipeline. In this regard, the CommonVoice-V16 dataset was used and a pipeline was designed to clean the data.
Text-to-speech models have also been evaluated for dataset construction. Among the existing models, the Pertts model performed well, but it was only trained for one speaker.
Also, another method for creating a dataset of open-text audio and text sources has been investigated, and in this method, correct matching between texts and sounds is very important.
At this stage, the training of different models is also considered and the Whisper model is trained on the cleaned CommonVoice-V16 dataset. The Word error rate (WER) on this dataset has reached about 2.5% for Persian language, which indicates significant improvements in the field of speech recognition using clean datasets and advanced models. This project shows how to achieve significant improvements in automatic speech recognition systems by using appropriate data sources and advanced neural network methods.