We lifted 14 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| mozilla/LPCNet | canonical | 5 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| dump_layer_ignore | Ran | mozilla/LPCNet/training_tf2/dump_lpcnet.py code served (permissive licence) · get_code("52188c0085750c07") |
| interp_mulaw | Ran | mozilla/LPCNet/training_tf2/lossfuncs.py code served (permissive licence) · get_code("a6e64cda1739e303") |
| printVector | Ran | mozilla/LPCNet/training_tf2/keraslayerdump.py code served (permissive licence) · get_code("a228df437cb01d43") |
| quant_regularizer | Ran | mozilla/LPCNet/training_tf2/lpcnet_plc.py code served (permissive licence) · get_code("a1a9d70573973a74") |
| tree_to_pdf | Ran | mozilla/LPCNet/training_tf2/lpcnet.py code served (permissive licence) · get_code("9e51f3aeba5a1306") |
| dump_sparse_gru | Not yet run | mozilla/LPCNet/training_tf2/dump_lpcnet.py code served (permissive licence) · get_code("6e0af699c31a78db") |
| dump_sparse_gru | Not yet run | mozilla/LPCNet/training_tf2/keraslayerdump.py code served (permissive licence) · get_code("3874048af50f43b6") |
| interleave | Not yet run | mozilla/LPCNet/training_tf2/lpcnet.py code served (permissive licence) · get_code("462214bb0c5c4921") |
| lpc2rc | Not yet run | mozilla/LPCNet/training_tf2/dataloader.py code served (permissive licence) · get_code("72b68e6945caf8ec") |
| metric_icel | Not yet run | mozilla/LPCNet/training_tf2/lossfuncs.py code served (permissive licence) · get_code("3d3bf2278d25692a") |
| metric_oginterploss | Not yet run | mozilla/LPCNet/training_tf2/lossfuncs.py code served (permissive licence) · get_code("d9840947585808dc") |
| printSparseVector | Not yet run | mozilla/LPCNet/training_tf2/dump_lpcnet.py code served (permissive licence) · get_code("7437251d9a634f0b") |
| printSparseVector | Not yet run | mozilla/LPCNet/training_tf2/keraslayerdump.py code served (permissive licence) · get_code("3a4032c58aaadc9d") |
| tree_to_pdf_train | Not yet run | mozilla/LPCNet/training_tf2/lpcnet.py code served (permissive licence) · get_code("1f1e48ec8adf543a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural speech synthesis algorithms are a promising new approach for coding speech at very low bitrate. They have so far demonstrated quality that far exceeds traditional vocoders, at the cost of very high complexity. In this work, we present a low-bitrate neural vocoder based on the LPCNet model. The use of linear prediction and sparse recurrent networks makes it possible to achieve real-time operation on general-purpose hardware. We demonstrate that LPCNet operating at 1.6 kb/s achieves significantly higher quality than MELP and that uncompressed LPCNet can exceed the quality of a waveform codec operating at low bitrate. This opens the way for new codec designs based on neural synthesis models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1903.12087")
get_code_for_paper("1903.12087")
have("1903.12087")
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