Faster-Whisper and Whisper.cpp for Local Transcription Quiz

Answers and explanations appear as you take the quiz.

1. Which inference engine is used by Faster-Whisper according to its project description?
   • PyTorch
   • whisper.cpp
   • CTranslate2
   • ONNX Runtime
2. How is whisper.cpp described by its project maintainers?
   • A Python implementation using CTranslate2
   • A C and C++ port of Whisper
   • A Python client that requires a hosted transcription service
   • A model checkpoint with no inference runtime
3. What can quantization trade for reduced memory or faster inference?
   • Only disk space, with no effect on output quality
   • Word error rate is independent of model precision
   • Quantization guarantees the same transcript as full precision
   • Some transcription quality
4. Why are raw speed comparisons between runtimes often misleading?
   • Model, device, decoding settings and workload may differ
   • The model size and precision are matched
   • The same input duration and device are used
   • Decoding and batching settings are equivalent
5. What does word error rate require for a meaningful evaluation?
   • Reference transcripts with unstated text normalization
   • Only elapsed inference time
   • Reference transcripts and stated normalization conventions
   • The number of model parameters without any transcript
6. Does local inference automatically guarantee privacy compliance?
   • Yes; local execution removes every consent obligation
   • No; storage, logs, access and consent still matter
   • Yes; local model weights guarantee all stored recordings are protected
   • Yes; downloading a model once prevents all later content logging
7. A deployment transcribes long, noisy meetings in a particular language. Which benchmark best supports decisions about that deployment?
   • Short clean clips in a different language
   • Silence-only clips used to isolate runtime overhead
   • Studio speech without the overlaps or noise expected in use
   • Representative recordings from intended conditions
8. When should a human review a locally generated transcript?
   • When errors could affect records, decisions or quotations
   • Only when audio was also uploaded to a hosted service
   • Only when the runtime reports an execution failure
   • Only when a nondefault checkpoint was selected