We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| clovaai/voxceleb_trainer | canonical | 2 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| find_option_type | Ran | clovaai/voxceleb_trainer/trainSpeakerNet.py code served (permissive licence) · get_code("3a86e2f4675702d4") |
| is_within_directory | Ran | this paper's copy was not recorded; identical code first harvested from jeromerony/dml_cross_entropy pointer only · get_code("1236c5af96d325a8") |
| md5 | Ran | clovaai/voxceleb_trainer/dataprep.py code served (permissive licence) · get_code("77c379958bd0a3ed") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The objective of this paper is 'open-set' speaker recognition of unseen speakers, where ideal embeddings should be able to condense information into a compact utterance-level representation that has small intra-speaker and large inter-speaker distance. A popular belief in speaker recognition is that networks trained with classification objectives outperform metric learning methods. In this paper, we present an extensive evaluation of most popular loss functions for speaker recognition on the VoxCeleb dataset. We demonstrate that the vanilla triplet loss shows competitive performance compared to classification-based losses, and those trained with our proposed metric learning objective outperform state-of-the-art methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2003.11982")
get_code_for_paper("2003.11982")
have("2003.11982")
Connect an agent — have() is free.