OpenAI whisper adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini idi pataki ti OpenAI's Whisper?
   • Idanimọ ọrọ aifọwọyi: ṣiṣakọ ati itumọ ohun
   • Ṣiṣẹda awọn aworan lati ọrọ
   • Kọ koodu kọmputa
   • Ṣiṣẹda 3D awọn ohun idanilaraya
2. What was true of the language balance in Whisper’s training audio?
   • Every language contributed the same number of hours
   • The dataset was English-heavy, with much less audio for most languages
   • The dataset contained only English audio
   • Most data was studio-recorded speech in one language
3. What does Whisper use to condition the decoder on the requested task and language?
   • A separate camera feed
   • A hand-written transcript of the answer
   • Special tokens in the decoder prompt
   • A unique model checkpoint for every utterance
4. Agbara wo ni Whisper KO pese ni abinibi?
   • Itumọ ọrọ lati awọn ede miiran si Gẹẹsi
   • Ṣiṣawari iru ede ti a nsọ
   • Fifi timestamps kun si tiransikiripiti
   • Ṣiṣẹda fidio ojulowo lati itọsi kan
5. Which long-form transcription problems does OpenAI’s paper describe?
   • Repetition loops and missing words near segment edges
   • Perfect diarization of every speaker
   • Automatic correction of every proper noun
   • Guaranteed removal of background music
6. Kini ailera ti a mọ ti Whisper lati ṣọna fun?
   • Ko le ṣe ilana eyikeyi ohun English
   • Lẹẹkọọkan le ọrọ 'hallucinate' ọrọ ti a ko sọ
   • O ṣe agbejade ohun nikan, kii ṣe ọrọ rara
   • O kọ lati fi awọn aami ifamisi kun
7. Approximately what share of Whisper’s training data was English audio paired with English transcripts?
   • About 18%
   • Nipa 1%
   • About 65%
   • About 100%
8. What use does OpenAI caution against when transcription errors could have serious consequences?
   • Noncommercial experimentation with public samples
   • Fifi timestamps kun si tiransikiripiti
   • Using the model to identify a language
   • High-risk decision-making without appropriate verification