Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Tacotron 2 adanwo

Review Tacotron 2’s text-to-mel pipeline, attention safeguards, and the learned stop prediction that determines utterance length.

Jẹmọ itọsọna awọn ọnaTacotron 2
Ibeere 1 ti 8

Aṣoju agbedemeji wo ni Tacotron 2 ṣe asọtẹlẹ lati ọrọ?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Aṣoju agbedemeji wo ni Tacotron 2 ṣe asọtẹlẹ lati ọrọ?
    • A mel-spectrogram
    • Aworan JPEG kan
    • Awọn foonu ifọrọranṣẹ aise nikan
    • Faili MIDI kan
  2. Kini o ṣe iyipada spectrogram Tacotron 2 sinu fọọmu igbi gangan?
    • A lọkọọkan checker
    • Ayipada WaveNet vocoder
    • A search engine
    • A convolutional image classifier
  3. Iṣoro wo ni ifarabalẹ ipo-iṣe iranlọwọ ṣe idiwọ?
    • Overheating awọn GPU
    • Pipadanu asopọ intanẹẹti
    • Tun tabi fo awọn ọrọ lakoko iṣelọpọ
    • Awọn aṣiṣe Akọtọ ninu ọrọ titẹ sii
  4. How does Tacotron 2 determine that it has generated the end of an utterance?
    • A human must press a button for every utterance
    • It always generates a fixed number of frames
    • The vocoder detects silence and interrupts the decoder
    • A learned stop-token prediction signals the decoder to stop
  5. Iru ara faaji gbogbogbo wo ni apakan ọrọ-si-spectrogram lo?
    • Ọkọọkan-si-tẹle pẹlu akiyesi
    • Tabili wiwa ti o rọrun
    • Igi ipinnu
    • A aimi ofin ṣeto
  6. Eyi ti nigbamii awoṣe ni a yiyara, ti kii-autoregressive arọpo ni ẹmí to Tacotron 2?
    • ResNet
    • FastSpeech 2
    • BERT
    • AlexNet
  7. What does the post-net in Tacotron 2 refine?
    • The input text spelling
    • The speaker’s identity metadata
    • The predicted mel-spectrogram
    • The final transcript punctuation
  8. What does FastSpeech 2 add as conditioning information for speech variation?
    • A video soundtrack transcript only
    • A random microphone serial number
    • A mandatory image embedding
    • Pitch, energy, and more accurate duration information