Okay !!! I understand that you are looking to build an intelligent interview web application using a neural network powered by a TensorFlow model. While the candidate is being interviewed, the video is saved and sent to the server for further processing. Once the interview is completed, a Cron Job picks up all the videos saved with the status pending for processing and processes them to perform an analysis.
After carefully reviewing the requirements and the issues you are facing, I would like to propose the following solutions:
1. Speed up the video processing: We can leverage cloud computing solutions such as AWS Lambda or Google Cloud Functions. These services can help distribute the processing load across multiple instances, which can significantly reduce processing time.
2. For Improve accuracy: speech-to-text conversion, we can use pre-trained models and fine-tune them with domain-specific data. We can also consider using a combination of different speech-to-text models to improve accuracy.
3. Minimize data loss issue: To minimize data loss issues with the audio model, we can implement a backup system where the audio data is stored in redundant locations to ensure that there is no loss of data. Additionally, we can introduce error detection and correction algorithms to ensure the integrity of the data.
Overall, I believe that these solutions will significantly improve the performance and accuracy of the system.
Regards
Neha K.