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End of training
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metadata
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - wer
model-index:
  - name: wav2vec2-large-mms-1b-even-pakendorf
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: train
          args: default
        metrics:
          - name: Wer
            type: wer
            value: 0.7591335595927331

wav2vec2-large-mms-1b-even-pakendorf

This model is a fine-tuned version of facebook/mms-1b-all on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: inf
  • Wer: 0.7591
  • Cer: 0.2779

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: 0.001
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 4
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.847 0.1895 300 inf 0.9027 0.3662
1.8253 0.3790 600 inf 0.9087 0.3658
1.6956 0.5685 900 inf 0.8723 0.3412
1.6616 0.7581 1200 inf 0.8437 0.3209
1.5962 0.9476 1500 inf 0.8392 0.3217
1.6299 1.1371 1800 inf 0.8447 0.3201
1.5242 1.3266 2100 inf 0.8191 0.3076
1.582 1.5161 2400 inf 0.8157 0.3070
1.5555 1.7056 2700 inf 0.8092 0.3061
1.5476 1.8951 3000 inf 0.7999 0.3009
1.4725 2.0846 3300 inf 0.7945 0.2952
1.4902 2.2742 3600 inf 0.7834 0.2936
1.3984 2.4637 3900 inf 0.7836 0.2900
1.4633 2.6532 4200 inf 0.7942 0.2872
1.4533 2.8427 4500 inf 0.7804 0.2863
1.4814 3.0322 4800 inf 0.7728 0.2859
1.4397 3.2217 5100 inf 0.7693 0.2818
1.4218 3.4112 5400 inf 0.7702 0.2831
1.3655 3.6008 5700 inf 0.7650 0.2795
1.34 3.7903 6000 inf 0.7615 0.2792
1.3351 3.9798 6300 inf 0.7591 0.2779

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1