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whisper-small-multilingual-spoken-ner-end2end

Automatic Speech Recognition
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whisper
Generated from Trainer
Eval Results
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Quentin Meeus
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metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - facebook/voxpopuli
metrics:
  - wer
model-index:
  - name: WhisperForSpokenNER-end2end
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: facebook/voxpopuli de+es+fr+nl
          type: facebook/voxpopuli
          config: de+es+fr+nl
          split: None
        metrics:
          - name: Wer
            type: wer
            value: 0.14642407057340895

WhisperForSpokenNER-end2end

This model is a fine-tuned version of openai/whisper-small on the facebook/voxpopuli de+es+fr+nl dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3933
  • Wer: 0.1464

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.3562 0.36 200 0.3265 0.1920
0.3149 0.71 400 0.3136 0.1842
0.2778 1.07 600 0.3204 0.1786
0.2288 1.43 800 0.3156 0.1717
0.2307 1.79 1000 0.3056 0.1708
0.1482 2.14 1200 0.3138 0.1682
0.1368 2.5 1400 0.3136 0.1656
0.1405 2.86 1600 0.3082 0.1617
0.0639 3.22 1800 0.3201 0.1612
0.0673 3.57 2000 0.3242 0.1612
0.0688 3.93 2200 0.3235 0.1584
0.0227 4.29 2400 0.3420 0.1558
0.0232 4.65 2600 0.3430 0.1525
0.0229 5.0 2800 0.3450 0.1528
0.0064 5.36 3000 0.3631 0.1498
0.0059 5.72 3200 0.3652 0.1482
0.0043 6.08 3400 0.3756 0.1482
0.0021 6.43 3600 0.3798 0.1477
0.002 6.79 3800 0.3824 0.1484
0.0014 7.15 4000 0.3876 0.1471
0.0013 7.51 4200 0.3900 0.1473
0.0013 7.86 4400 0.3917 0.1461
0.0012 8.22 4600 0.3929 0.1462
0.0012 8.58 4800 0.3932 0.1465
0.0012 8.94 5000 0.3933 0.1464

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1