FFTNet vocoder implementation

Overview

Unofficial Implementation of FFTNet vocode paper.

  • implement the model.
  • implement tests.
  • overfit on a single batch (sanity check).
  • linearize weights for eval time.
  • measure the run-time on GPU and CPU. (1 sec audio takes ~47 secs) If anyone knows additional tricks from the paper, let me know. So far I asked the authors but nobody returned.
  • train on LJSpeech spectrograms.
  • distill model as in Parallel WaveNet paper.
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Comments
  • GPU infer time is slow than CPU infer time

    GPU infer time is slow than CPU infer time

    hi! I used run_time_test.py to test time!

    Avg time per step inference on CPU: 0.002576863145828247 Avg time per step inference on GPU: 0.003681471061706543

    opened by maozhiqiang 2
  • Wiki changes

    Wiki changes

    FYI: The following changes were made to this repository's wiki:

    These were made as the result of a recent automated defacement of publically writeable wikis.

    opened by moz-hwine 0
  • Does the fast step can generate 1s in about 0.8s as the paper said?

    Does the fast step can generate 1s in about 0.8s as the paper said?

    Avg time per step inference on CPU: 0.002576863145828247 Avg time per step inference on GPU: 0.003681471061706543

    0.003681471061706543 * 16000 = 59s

    so how can we get the result in the paper?

    opened by azraelkuan 5
Owner
Eren Gölge
AI researcher @Coqui.ai
Eren Gölge
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