We lifted 3 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| neosapience/mlp-singer | canonical | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| load_checkpoint_config | Not yet run | neosapience/mlp-singer/utils.py code served (permissive licence) · get_code("77dc10651b3d716e") |
| load_config | Not yet run | neosapience/mlp-singer/utils.py code served (permissive licence) · get_code("17adba9beade4106") |
| load_trainer | Not yet run | neosapience/mlp-singer/utils.py code served (permissive licence) · get_code("cd8544d4f5d2bacd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent developments in deep learning have significantly improved the quality of synthesized singing voice audio. However, prominent neural singing voice synthesis systems suffer from slow inference speed due to their autoregressive design. Inspired by MLP-Mixer, a novel architecture introduced in the vision literature for attention-free image classification, we propose MLP Singer, a parallel Korean singing voice synthesis system. To the best of our knowledge, this is the first work that uses an entirely MLP-based architecture for voice synthesis. Listening tests demonstrate that MLP Singer outperforms a larger autoregressive GAN-based system, both in terms of audio quality and synthesis speed. In particular, MLP Singer achieves a real-time factor of up to 200 and 3400 on CPUs and GPUs respectively, enabling order of magnitude faster generation on both environments.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.07886")
get_code_for_paper("2106.07886")
have("2106.07886")
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