To create a solid foundation for highly-accurate models, Deepgram suggests closely observing the following best practices. Doing so guarantees that new training data is consistent with data on which Deepgram’s off-the-shelf models were trained.
All labeling should be long-form, which means audio files should be labeled in their entirety. Partially-labeled files are unacceptable.
All audio files should be reviewed (and labeled, if necessary) by at least four (4) different, independent users. No user should be permitted to review the same file twice.
Hotpepper builds this best practice into its design, so to see a file all the way through the review process, four different users must be registered in the system.
All labeling should be scrutinized to ensure it adheres to a carefully constructed style guide. You can see Deepgram’s recommended style guide at:
Data loss can occur at any time and for various reasons. Good backups keep downtime to a minimum by saving time wasted on both recovery and recreating finished work.
To avoid losing all transcripts produced by Hotpepper, back up both your dataset directories and the Hotpepper database. Hotpepper uses a SQLite database, so you only need to copy the configured database file to a secure location.
Make sure you aren’t in a rush when transcribing. To ensure that the quality of work is sufficient, you need to allow enough time to complete the task. For a seasoned transcriptionist, a quality transcription usually takes 5-6x as long as the duration of the original audio file. Many transcriptionists may take as long as 8x.