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whisper-base-ckb

Automatic Speech Recognition
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Safetensors
whisper
Generated from Trainer
Eval Results
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metadata
license: apache-2.0
base_model: openai/whisper-base
tags:
  - generated_from_trainer
datasets:
  - razhan/common_voice_ckb_16
metrics:
  - wer
model-index:
  - name: whisper-base-ckb
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: razhan/common_voice_ckb_16
          type: razhan/common_voice_ckb_16
        metrics:
          - name: Wer
            type: wer
            value: 0.12623194275685162

whisper-base-ckb

This model is a fine-tuned version of openai/whisper-base on the razhan/common_voice_ckb_16 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0641
  • Wer: 0.1262

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: 1e-05
  • train_batch_size: 192
  • eval_batch_size: 128
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 6
  • total_train_batch_size: 1152
  • total_eval_batch_size: 768
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • training_steps: 2300
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.3434 1.09 100 0.3840 0.6054
0.2089 2.17 200 0.2654 0.4740
0.167 3.26 300 0.2246 0.4190
0.1452 4.35 400 0.1964 0.3803
0.1287 5.43 500 0.1788 0.3542
0.1163 6.52 600 0.1650 0.3326
0.1068 7.61 700 0.1560 0.3155
0.1015 8.7 800 0.1489 0.3059
0.0968 9.78 900 0.1440 0.2954
0.0939 10.87 1000 0.1420 0.2918
0.0919 11.96 1100 0.1315 0.2742
0.0839 13.04 1200 0.1217 0.2597
0.0713 14.13 1300 0.1132 0.2371
0.0687 15.22 1400 0.1091 0.2372
0.0647 16.3 1500 0.1022 0.2173
0.059 17.39 1600 0.0967 0.2043
0.0539 18.48 1700 0.0897 0.1929
0.0518 19.57 1800 0.0827 0.1718
0.0495 20.65 1900 0.0787 0.1667
0.0444 21.74 2000 0.0718 0.1469
0.0392 22.83 2100 0.0671 0.1368
0.0335 23.91 2200 0.0645 0.1263
0.0292 25.0 2300 0.0641 0.1262

Framework versions

  • Transformers 4.38.0.dev0
  • Pytorch 2.0.1
  • Datasets 2.16.1
  • Tokenizers 0.15.0