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Chunked Autoregressive GAN for Conditional Waveform Synthesis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use chunked autoregression instead of non-autoregressive waveform generation | HiFi-GAN-style conditional waveform synthesis · VCTK | Pitch error (cents): 51.2 -> 29.4 | Helped |
| use chunked autoregression instead of non-autoregressive waveform generation | HiFi-GAN-style conditional waveform synthesis · DAPS | Periodicity RMSE: .142 -> .107 | Helped |
| use chunked autoregression instead of non-autoregressive waveform generation | HiFi-GAN-style conditional waveform synthesis · VCTK | Training speed (ms/step): 1186 -> 502 | Helped |
| use chunked autoregression instead of non-autoregressive waveform generation | HiFi-GAN-style conditional waveform synthesis · VCTK synthetic cumulative sum, 1024 | L1 distance: .050 -> .009 | Helped |
| replace the GAN-TTS generator with the HiFi-GAN generator (ablation) | CARGAN · VCTK | Pitch error (cents): 29.4 -> 37.9 | Hurt |
| replace the GAN-TTS generator with the HiFi-GAN generator (ablation) | CARGAN · DAPS | Pitch error (cents): 21.6 -> 27.0 | Hurt |
| remove loss compensation and restore the original loss weights (ablation) | CARGAN · VCTK | Pitch error (cents): 29.4 -> 33.7 | Hurt |
| remove loss compensation and restore the original loss weights (ablation) | CARGAN · DAPS | Pitch error (cents): 21.6 -> 34.1 | Hurt |
| omit prepending autoregressive context to discriminator inputs (ablation) | CARGAN · VCTK | Periodicity RMSE: .086 -> .088 | Hurt |
| omit prepending autoregressive context to discriminator inputs (ablation) | CARGAN · VCTK | Pitch error (cents): 29.4 -> 24.6 | Helped |
| vary chunk size (ablation) | CARGAN · DAPS | Pitch error (cents): 22.9 (512); 22.1 (1024); 21.6 (2048); 24.8 (4096); 24.1 (8192) | Mixed |
| increase every convolution kernel size to 15 (ablation) | CARGAN · VCTK speech synthesis | Pitch error (cents): 29.4 -> 24.3 | Helped |
Closest papers by meaning
- MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis (NeurIPS 2019 · similarity 0.93)
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