To optimize OpenWispr's local transcription speed, the following steps can be taken: first select a lighter Whisper model (e.g. tiny or base) in the control panel, which are small (tiny is only 39MB) and fast to process, and suitable for scenarios that do not require high accuracy. If the performance of the device allows (more than 8GB of RAM is recommended), you can gradually upgrade to small or medium models to balance speed and quality. Secondly, make sure hardware acceleration is enabled: the program will automatically detect the Python environment, and it is recommended to install Python 3.11+ version for better performance support. In addition, closing other applications that consume CPU resources can significantly improve processing speed. For long-term use, consider an SSD hard disk to store model files for faster loading.
This answer comes from the articleOpenWispr: Privacy-First Speech-to-Text Desktop ApplicationThe
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