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  1. .gitattributes +1 -0
  2. README.md +58 -0
  3. lcnn/exp1.x/base.mdl +3 -0
  4. lcnn/exp1.x/exp1.1/cl-ckpt/train_facebook_mms-tts-eng.mdl +3 -0
  5. lcnn/exp1.x/exp1.1/cl-ckpt/train_griffin_lim.mdl +3 -0
  6. lcnn/exp1.x/exp1.1/cl-ckpt/train_suno_bark.mdl +3 -0
  7. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_blizzard2013_capacitron-t2-c150_v2.mdl +3 -0
  8. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_blizzard2013_capacitron-t2-c50.mdl +3 -0
  9. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ek1_tacotron2.mdl +3 -0
  10. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_jenny_jenny.mdl +3 -0
  11. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_fast_pitch.mdl +3 -0
  12. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_glow-tts.mdl +3 -0
  13. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_neural_hmm.mdl +3 -0
  14. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_overflow.mdl +3 -0
  15. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_speedy-speech.mdl +3 -0
  16. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_tacotron2-DCA.mdl +3 -0
  17. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_tacotron2-DDC.mdl +3 -0
  18. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_tacotron2-DDC_ph.mdl +3 -0
  19. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_vits--neon.mdl +3 -0
  20. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_ljspeech_vits.mdl +3 -0
  21. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_multi-dataset_tortoise-v2.mdl +3 -0
  22. lcnn/exp1.x/exp1.1/cl-ckpt/train_tts_models_en_sam_tacotron-DDC.mdl +3 -0
  23. lcnn/exp1.x/exp1.1/exp1.1.yaml +50 -0
  24. lcnn/exp1.x/exp1.2/cl-ckpt/train_facebook_mms-tts-eng.mdl +3 -0
  25. lcnn/exp1.x/exp1.2/cl-ckpt/train_griffin_lim.mdl +3 -0
  26. lcnn/exp1.x/exp1.2/cl-ckpt/train_suno_bark.mdl +3 -0
  27. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_blizzard2013_capacitron-t2-c150_v2.mdl +3 -0
  28. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_blizzard2013_capacitron-t2-c50.mdl +3 -0
  29. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ek1_tacotron2.mdl +3 -0
  30. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_jenny_jenny.mdl +3 -0
  31. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_fast_pitch.mdl +3 -0
  32. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_glow-tts.mdl +3 -0
  33. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_neural_hmm.mdl +3 -0
  34. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_overflow.mdl +3 -0
  35. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_speedy-speech.mdl +3 -0
  36. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_tacotron2-DCA.mdl +3 -0
  37. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_tacotron2-DDC.mdl +3 -0
  38. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_tacotron2-DDC_ph.mdl +3 -0
  39. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_vits--neon.mdl +3 -0
  40. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_ljspeech_vits.mdl +3 -0
  41. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_multi-dataset_tortoise-v2.mdl +3 -0
  42. lcnn/exp1.x/exp1.2/cl-ckpt/train_tts_models_en_sam_tacotron-DDC.mdl +3 -0
  43. lcnn/exp1.x/exp1.2/exp1.2.yaml +48 -0
  44. lcnn/exp1.x/exp1.3/cl-ckpt/train_facebook_mms-tts-eng.mdl +3 -0
  45. lcnn/exp1.x/exp1.3/cl-ckpt/train_griffin_lim.mdl +3 -0
  46. lcnn/exp1.x/exp1.3/cl-ckpt/train_suno_bark.mdl +3 -0
  47. lcnn/exp1.x/exp1.3/cl-ckpt/train_tts_models_en_blizzard2013_capacitron-t2-c150_v2.mdl +3 -0
  48. lcnn/exp1.x/exp1.3/cl-ckpt/train_tts_models_en_blizzard2013_capacitron-t2-c50.mdl +3 -0
  49. lcnn/exp1.x/exp1.3/cl-ckpt/train_tts_models_en_ek1_tacotron2.mdl +3 -0
  50. lcnn/exp1.x/exp1.3/cl-ckpt/train_tts_models_en_jenny_jenny.mdl +3 -0
.gitattributes CHANGED
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ ---
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+
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+
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+ This repo is used to host the configs and checkpoints used in our ICASSP2024 submission projects.
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+
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+
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+ ## How to run
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+
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+ The [codebase](https://github.com/XIAOYixuan/tomatoDD) is work-in-progress.
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+
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+ ## Exp Description
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+
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+ For each model, there are four exp setups, as shown in the following table.
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+
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+ For example, exp1.1 refers to an experiment where:
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+ - The genuine data comes from Librispeech.
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+ - The training order is organized by attacker's training dataset's name.
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+ - The training strategy is finetuning.
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+
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+ <table>
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+ <tr>
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+ <th rowspan="1">Genuine Data</th>
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+ <th colspan="2">Librispeech</th>
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+ <th colspan="2">MLAAD</th>
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+ </tr>
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+ <tr>
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+ <th>Task Order</th>
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+ <th>By Dataset</th>
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+ <th>Random</th>
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+ <th>Random</th>
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+ </tr>
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+ <tr>
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+ <td>Finetune</td>
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+ <td>exp1.1</td>
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+ <td>exp1.2</td>
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+ <td>exp1.4</td>
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+ </tr>
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+ <tr>
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+ <td>One-Class Softmax</td>
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+ <td>exp2.1</td>
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+ <td>exp2.2</td>
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+ <td>exp2.4</td>
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+ </tr>
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+ <tr>
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+ <td>Replay</td>
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+ <td>exp3.1</td>
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+ <td>exp3.2</td>
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+ <td>exp3.4</td>
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+ </tr>
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+ <tr>
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+ <td>LwF</td>
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+ <td>exp4.1</td>
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+ <td>exp4.2</td>
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+ <td>exp4.4</td>
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+ </tr>
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+ </table>
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+ data:
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+ model_class: MultiDS
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+ train_splits: ['base_train', 'base_dev', 'cl_train', 'test']
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+ infer_splits: ['test']
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+ base: # split: train, dev
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+ model_class: GeneralFAD
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+ data_path: dataset_meta/ASVSpoof19
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+ cl: # split: train, dev, test
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+ model_class: GeneralMultiDS
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+ data_path: dataset_meta/MLAAD_en_mix_libri
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+ data_list: meta_files/mlaad_attacker_order_by_ds.lst
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+ # num_workers and batch_size are used for dataloader
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+ num_workers: 16
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+ batch_size: 128
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+ test_num_workers: 16
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+ test_batch_size: 128
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+ max_len: 4
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+ transformation:
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+ func: LFCC
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+ fl: 320
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+ fs: 160
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+ fn: 512
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+ sr: 16000
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+ filter_num: 20
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+ with_energy: False
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+ model:
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+ model_class: PlainLCNN
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+ criterion:
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+ model_class: built-in # xent
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+ type: CrossEntropyLoss
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+ train:
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+ base_task_tag: asvspoof19
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+ has_best_model: False
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+ start_steps: 0
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+ max_steps: 10000000000
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+ max_epoch: 10
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+ finetune_max_epoch: 5
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+ # Adam optimizer
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+ lr: 0.0005
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+ finetune_lr: 0.0001
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+ #finetune_lr: 0.00005
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+ beta1: 0.9
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+ beta2: 0.999
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+ #weight_decay: 0.00005
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+ weight_decay: 0.0005
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+ # step LR
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+ lr_gamma: 0.5
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+ lr_step_size: 20
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+ validate_interval: 10000000000
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+ log_interval: 40
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+ data:
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+ model_class: MultiDS
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+ train_splits: ['base_train', 'cl_train', 'test']
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+ infer_splits: ['test']
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+ base: # split: train, dev
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+ model_class: GeneralFAD
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+ data_path: dataset_meta/ASVSpoof19
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+ cl: # split: train, dev, test
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+ model_class: GeneralMultiDS
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+ data_path: dataset_meta/MLAAD_en_mix_libri
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+ data_list: meta_files/mlaad_attacker_order_random.lst
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+ # num_workers and batch_size are used for dataloader
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+ num_workers: 16
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+ batch_size: 128
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+ test_num_workers: 16
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+ test_batch_size: 128
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+ max_len: 4
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+ transformation:
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+ func: LFCC
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+ fl: 320
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+ fs: 160
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+ fn: 512
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+ sr: 16000
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+ filter_num: 20
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+ with_energy: False
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+ model:
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+ model_class: PlainLCNN
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+ criterion:
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+ model_class: built-in # xent
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+ type: CrossEntropyLoss
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+ train:
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+ base_task_tag: asvspoof19
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+ has_best_model: True
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+ start_steps: 0
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+ max_steps: 10000000000
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+ max_epoch: 10
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+ finetune_max_epoch: 5
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+ # Adam optimizer
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+ lr: 0.0005
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+ finetune_lr: 0.0001
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+ beta1: 0.9
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+ beta2: 0.999
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+ weight_decay: 0.0005
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+ # step LR
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+ lr_gamma: 0.5
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+ lr_step_size: 20
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+ validate_interval: 10000000000
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+ log_interval: 40
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