Kediska WaveNet

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Sidee WaveNet u soo saartaa maqal?
   • Hal muunad ceeriin ah, oo shardi ku ah muunado hore
   • Adiga oo dooranaya clips maktabad go'an
   • Iyadoo spectrograms si toos ah loogu beddelayo qoraal
   • Dhammaan muunado isku mar ah oo aan la dalban
2. Waa maxay hal-abuurka kacaanka ee furaha ah ee WaveNet?
   • Lakabyada isku ururinta ugu badan
   • Khilaafyada sababa ee kala fogaaday
   • Albaabada soo noqnoqda kaliya
   • Lakabyo si buuxda isugu xidhan
3. How did the original WaveNet represent quantized waveform samples for prediction?
   • As a single continuous average for the entire clip
   • As speaker IDs only
   • As a categorical distribution over 256 µ-law-quantized values
   • As a sequence of image patches
4. Maxay ahayd cilad-darro weyn oo ku timid WaveNet-kii asalka ahaa?
   • Waxay dhalin kartaa aamusnaan
   • Uma baahna xog tababar
   • Waxay kaliya ka shaqeysay sawirro
   • Muunad isdaba-joog ah oo jiilka laga dhigay mid gaabis ah
5. In the speech-synthesis experiments described in the WaveNet paper, what does the model generate?
   • Raw speech waveforms sample by sample, conditioned on linguistic features
   • A mel-spectrogram that is itself the final audio
   • Only phoneme labels without audio
   • A MIDI score
6. Ururkee soo bandhigay WaveNet?
   • NASA
   • DeepMind
   • OpenAI
   • Adobe
7. What does causal convolution prevent in WaveNet?
   • Using preceding samples as context
   • Generating any audio waveform
   • Using future audio samples to predict the current sample
   • Conditioning on text or speaker features
8. What activation structure does the original WaveNet use in its gated convolutional layers?
   • A ReLU output concatenated with image patches
   • A softmax over speakers only
   • A linear layer without nonlinearities
   • A tanh-filter output multiplied elementwise by a sigmoid gate