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Training in progress, epoch 1
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metadata
library_name: transformers
tags:
  - audio-classification
  - generated_from_trainer
datasets:
  - voxceleb
metrics:
  - accuracy
model-index:
  - name: ecapa-tdnn-voxceleb1-c512-aam
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: confit/voxceleb
          type: voxceleb
          config: verification
          split: train
          args: verification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9757901815736382

ecapa-tdnn-voxceleb1-c512-aam

This model is a fine-tuned version of on the confit/voxceleb dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5840
  • Accuracy: 0.9758

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0005
  • train_batch_size: 256
  • eval_batch_size: 1
  • seed: 914
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
9.047 1.0 575 8.3662 0.4304
5.3508 2.0 1150 4.0252 0.8191
3.3124 3.0 1725 2.1083 0.9260
2.3212 4.0 2300 1.2224 0.9435
1.6276 5.0 2875 0.8229 0.9677
1.1418 6.0 3450 0.5840 0.9758
1.0484 7.0 4025 0.5781 0.9738
0.0 8.0 4600 nan 0.0007
0.0 9.0 5175 nan 0.0007
0.0 10.0 5750 nan 0.0007

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.0.0+cu117
  • Datasets 3.2.0
  • Tokenizers 0.21.0