What are the limitations of Whisper and how to bypass them (long-form, hallucinations)?

Whisper is an automatic speech recognition model from OpenAI. Main limitations and ways to overcome them:

1. Long-form audio recordings

   • Limitation: The model performs better with short segments (a few minutes), and recognition quality may decrease with longer recordings.
   • Workaround: Split long audio into shorter segments using pause-based or time-based segmentation algorithms, then combine the results.

2. Hallucinations

   • Limitation: The model may mistakenly "fill in" words or phrases, especially with poor audio quality or noise.
   • Workaround: Improve audio quality, use noise reduction, and apply post-processing with language models to verify and correct the text.

3. Language and accent limitations

   • Whisper supports many languages, but recognition quality may vary.
   • Workaround: Choose an appropriate language model (small, base, large), and use adaptation for specific languages or accents.

Thus, to improve recognition quality, it is recommended to preprocess audio, segment long files, and perform post-processing of the text.