We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| Wadaboa/titanet | pwc_unofficial | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| collate_fn | Ran | Wadaboa/titanet/src/datasets.py code served (permissive licence) · get_code("c8322253612f1f9f") |
| copy_example | Ran | Wadaboa/titanet/src/transforms.py code served (permissive licence) · get_code("df2af7d3bc841ca8") |
| get_random_filename | Ran | Wadaboa/titanet/src/utils.py code served (permissive licence) · get_code("7d53367371a2e0a3") |
| to_numpy | Ran | Wadaboa/titanet/src/utils.py code served (permissive licence) · get_code("66330d0b22b2081f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker representations. We employ 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with global context followed by channel attention based statistics pooling layer to map variable-length utterances to a fixed-length embedding (t-vector). TitaNet is a scalable architecture and achieves state-of-the-art performance on speaker verification task with an equal error rate (EER) of 0.68% on the VoxCeleb1 trial file and also on speaker diarization tasks with diarization error rate (DER) of 1.73% on AMI-MixHeadset, 1.99% on AMI-Lapel and 1.11% on CH109. Furthermore, we investigate various sizes of TitaNet and present a light TitaNet-S model with only 6M parameters that achieve near state-of-the-art results in diarization tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.04410")
get_code_for_paper("2110.04410")
have("2110.04410")
Connect an agent — have() is free.