Orate's Speech-to-Text (STT) feature is based on professional models such as AssemblyAI's 'best' and meets industrial-grade standards for speech recognition accuracy and processing speed. The feature is implemented through the transcribe() method, which can process common audio files including wav format and output structured text results.
In terms of technical indicators, Orate's integrated STT service excels in speech recognition in complex environments and processing of specialized terminology. For example, in multi-person conversation scenes or audio with high background noise, it can still maintain a high transcription accuracy rate. For transcription of audio materials in specialized fields such as law and medicine, more accurate results can be obtained by selecting professional models.
Compared with self-developed speech recognition systems, the solution using mature commercial models through Orate can significantly reduce the error rate while saving the time cost of development and tuning, making it an efficient choice for enterprises to rapidly deploy speech recognition capabilities.
This answer comes from the articleOrate: A Unified API for Integrating Well-Known Speech Generation, Speech Transcription and Voice Change ModelsThe
