Maswali ya WaveNet

Majibu na maelezo huonekana unapojibu maswali.

1. Je, WaveNet hutoaje sauti?
   • Sampuli mbichi moja kwa wakati mmoja, iliyowekwa kwenye sampuli za awali
   • Kwa kuchagua klipu kutoka kwa maktaba isiyobadilika
   • Kwa kubadilisha spectrogram moja kwa moja hadi maandishi
   • Sampuli zote kwa wakati mmoja bila kuagiza
2. Ni uvumbuzi gani muhimu wa kimaadili katika WaveNet?
   • Upeo wa tabaka za kuunganisha
   • Mipasuko ya sababu iliyopanuka
   • Milango ya kawaida pekee
   • Safu zilizounganishwa kikamilifu
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. Ni nini kikwazo kikuu cha vitendo cha WaveNet asilia?
   • Inaweza tu kuleta ukimya
   • Haihitaji data ya mafunzo
   • Ilifanya kazi kwenye picha tu
   • Sampuli za kufuatana zilifanya uzalishaji kuwa polepole
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. Ni shirika gani lilianzisha 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