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whisper-medium-eu-cv16_1

Automatic Speech Recognition
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metadata
language:
  - eu
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: Whisper Medium Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_16_1 eu
          type: mozilla-foundation/common_voice_16_1
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 9.200601844614663

Whisper Medium Basque

This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_16_1 eu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4303
  • Wer: 9.2006

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: 64
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 40000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0055 10.03 1000 0.2463 11.8425
0.003 20.05 2000 0.2638 11.3178
0.0018 30.08 3000 0.2837 10.9583
0.0009 40.1 4000 0.2768 10.4414
0.0008 50.13 5000 0.2880 10.1776
0.0012 60.15 6000 0.2903 10.0526
0.0002 70.18 7000 0.2909 9.8357
0.0013 80.2 8000 0.2766 9.9392
0.0001 90.23 9000 0.3110 9.3003
0.0 100.25 10000 0.3278 9.3315
0.0 110.28 11000 0.3393 9.3081
0.0 120.3 12000 0.3508 9.2993
0.0 130.33 13000 0.3617 9.3218
0.0 140.35 14000 0.3732 9.3354
0.0 150.38 15000 0.3849 9.3735
0.0 160.4 16000 0.3073 9.3335
0.0 170.43 17000 0.3320 9.3569
0.0 180.45 18000 0.3453 9.3022
0.0 190.48 19000 0.3561 9.3071
0.0 200.5 20000 0.3660 9.2983
0.0 210.53 21000 0.3755 9.2876
0.0 220.55 22000 0.3847 9.4976
0.0 230.58 23000 0.3940 9.5054
0.0 240.6 24000 0.4021 9.4703
0.0 250.63 25000 0.4126 9.4537
0.0 260.65 26000 0.3174 9.2758
0.0 270.68 27000 0.3444 9.2622
0.0 280.7 28000 0.3588 9.2084
0.0 290.73 29000 0.3698 9.3472
0.0 300.75 30000 0.3786 9.3423
0.0 310.78 31000 0.3868 9.3169
0.0 320.8 32000 0.3948 9.3286
0.0 330.83 33000 0.4018 9.3335
0.0 340.85 34000 0.4081 9.3286
0.0 350.88 35000 0.4138 9.3364
0.0 360.9 36000 0.4191 9.3432
0.0 370.93 37000 0.4234 9.3315
0.0 380.95 38000 0.4270 9.3403
0.0 390.98 39000 0.4294 9.2153
0.0 401.0 40000 0.4303 9.2006

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1