We lifted 20 functions out of this paper's own repositories and ran 17 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| bigpon/QPPWG | pwc_unofficial | 8 of 9 |
| bigpon/vcc20_baseline_cyclevae | pwc_unofficial | 7 of 8 |
| mukeshv0/ParallelWaveGAN | pwc_unofficial | 2 of 2 |
| copy not recorded | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| batch_f0 | Ran | bigpon/QPPWG/qppwg/utils/features.py code served (permissive licence) · get_code("0ed54be0f7619aca") |
| check_hdf5 | Ran | bigpon/vcc20_baseline_cyclevae/baseline/src/utils/utils.py code served (permissive licence) · get_code("92d84700541a2536") |
| dilated_factor | Ran | bigpon/QPPWG/qppwg/utils/features.py code served (permissive licence) · get_code("8cd88186efdab6e0") |
| find_files | Ran | mukeshv0/ParallelWaveGAN/parallel_wavegan/utils/utils.py code served (permissive licence) · get_code("562a6efae0225990") |
| loss_vae_laplace | Ran | bigpon/vcc20_baseline_cyclevae/baseline/src/nets/gru_vae.py code served (permissive licence) · get_code("04dd0d78b380bade") |
| low_cut_filter | Ran | bigpon/QPPWG/qppwg/bin/preprocess.py code served (permissive licence) · get_code("bbf58066f7748c5a") |
| low_cut_filter | Ran | bigpon/QPPWG/qppwg/utils/filters.py code served (permissive licence) · get_code("44d3c0be62e4bbab") |
| low_cut_filter | Ran | bigpon/vcc20_baseline_cyclevae/baseline/src/bin/feature_extract.py code served (permissive licence) · get_code("354a3798e3217759") |
| low_pass_filter | Ran | bigpon/QPPWG/qppwg/utils/filters.py code served (permissive licence) · get_code("c28f03204eefdd29") |
| padding | Ran | bigpon/vcc20_baseline_cyclevae/baseline/src/utils/dataset.py code served (permissive licence) · get_code("9164e9f0890be261") |
| path_replace | Ran | bigpon/QPPWG/qppwg/bin/preprocess.py code served (permissive licence) · get_code("a5d255facaefc424") |
| read_hdf5 | Ran | bigpon/vcc20_baseline_cyclevae/baseline/src/utils/utils.py code served (permissive licence) · get_code("c0be466d384f4bf1") |
| read_hdf5 | Ran | mukeshv0/ParallelWaveGAN/parallel_wavegan/utils/utils.py code served (permissive licence) · get_code("4f86bc80523a92c1") |
| sampling_vae_laplace | Ran | bigpon/vcc20_baseline_cyclevae/baseline/src/nets/gru_vae.py code served (permissive licence) · get_code("b45003226b3b8d82") |
| shape_hdf5 | Ran | bigpon/vcc20_baseline_cyclevae/baseline/src/utils/utils.py code served (permissive licence) · get_code("d3302da55afa1057") |
| spk_division | Ran | bigpon/QPPWG/qppwg/bin/preprocess.py code served (permissive licence) · get_code("102ba7c26e81660b") |
| validate_length | Ran | bigpon/QPPWG/qppwg/utils/features.py code served (permissive licence) · get_code("a4fce818fed4b3c9") |
| load_checkpoint | Not yet run | this paper's copy was not recorded; identical code first harvested from yanggeng1995/GAN-TTS pointer only · get_code("f443ef0155d2d898") |
| sampling_vae_laplace_batch | Not yet run | bigpon/vcc20_baseline_cyclevae/baseline/src/nets/gru_vae.py code served (permissive licence) · get_code("6e0be223aee6ee4e") |
| stft | Not yet run | bigpon/QPPWG/qppwg/losses/stft_loss.py code served (permissive licence) · get_code("d014b1a7520bc12b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network. In the proposed method, a non-autoregressive WaveNet is trained by jointly optimizing multi-resolution spectrogram and adversarial loss functions, which can effectively capture the time-frequency distribution of the realistic speech waveform. As our method does not require density distillation used in the conventional teacher-student framework, the entire model can be easily trained. Furthermore, our model is able to generate high-fidelity speech even with its compact architecture. In particular, the proposed Parallel WaveGAN has only 1.44 M parameters and can generate 24 kHz speech waveform 28.68 times faster than real-time on a single GPU environment. Perceptual listening test results verify that our proposed method achieves 4.16 mean opinion score within a Transformer-based text-to-speech framework, which is comparative to the best distillation-based Parallel WaveNet system.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1910.11480")
get_code_for_paper("1910.11480")
have("1910.11480")
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