AudioLM Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeipi pfungwa huru inoshandiswa neAudioLM kugadzira odhiyo?
   • Inobata odhiyo senhevedzano yezviratidzo uye inofanotaura inotevera, senge modhi yemutauro
   • Inopa odhiyo kubva musheti mimhanzi
   • Inotevedzera ruzha rusina kurongeka pasina kudzidza
   • Inorekodha uye inodzokorora maitiro evanhu
2. Ndeipi basa re 'semantic' tokens muAudioLM?
   • Ivo vanobata timbre yakanaka uye kurekodha ruzha
   • Vanotora chimiro chenguva refu sefonetiki uye mutinhimira
   • Vanochengeta zvinyorwa zvinyorwa
   • Ivo vanodzora kubuda kwefaira fomati
3. Ndeipi neural codec inogadzira AudioLM's acoustic tokens?
   • MP3
   • WaveNet
   • SoundStream
   • Zevezera
4. Chii chinodiwa AudioLM se data rekudzidzisa?
   • Odhiyo yakapetwa nezvinyorwa zvine hudzamu
   • Chete MIDI mafaera
   • Mifananidzo ye waveforms
   • Raw odhiyo yega, isina zvinyorwa
5. After AudioLM predicts semantic tokens, how does its hierarchical generation add acoustic detail?
   • It predicts coarse acoustic tokens, then fine acoustic tokens
   • It predicts fine acoustic tokens, then coarse acoustic tokens
   • It predicts coarse acoustic tokens before semantic tokens
   • It predicts acoustic tokens without a semantic-token stage
6. Ndeipi yakazotevera sisitimu yakavakirwa zvakananga paAudioLM's token-based approach?
   • Stable Diffusion
   • MusicLM
   • GPT-2
   • DALL-E
7. Which model does AudioLM use to produce its semantic token sequence?
   • A text-only GPT decoder trained on transcripts
   • The SoundStream waveform decoder
   • A self-supervised w2v-BERT representation model
   • A hand-authored MIDI score
8. What audio types did the AudioLM paper demonstrate continuing from an audio prompt?
   • Only environmental noise and no music
   • Spoken text transcripts and MIDI files only
   • Images and silent video clips
   • Speech and piano music