whisper-tiny-mn-9 / README.md
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metadata
language: mn
license: apache-2.0
tags:
  - whisper-event
  - hf-asr-leaderboard
  - generated_from_multiple_datasets
datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  - bayartsogt/ulaanbal-v0
  - bayartsogt/youtube-mongolian-v1
metrics:
  - wer
  - cer
model-index:
  - name: whisper-tiny-mn-9
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: mn
          split: test
        metrics:
          - type: wer
            value: 45.51015949311776
            name: Wer
          - type: cer
            value: 17.33769077861258
            name: Cer

whisper-tiny-mn-9

This model is a fine-tuned version of openai/whisper-tiny on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3885
  • Wer: 45.5102
  • Cer: 17.3377

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: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 20000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.587 0.69 1000 0.6937 75.6336 29.6764
0.4536 1.39 2000 0.5539 64.8187 24.8324
0.3798 2.08 3000 0.4963 57.7944 22.1842
0.3423 2.77 4000 0.4661 54.3751 20.9705
0.3122 3.47 5000 0.4449 52.5945 20.3405
0.3002 4.16 6000 0.4285 50.5080 19.3499
0.2842 4.85 7000 0.4171 49.3937 19.0282
0.2655 5.54 8000 0.4099 48.6727 18.6045
0.2555 6.24 9000 0.4035 48.2084 18.3392
0.2525 6.93 10000 0.3990 47.3290 17.8338
0.243 7.62 11000 0.3963 47.0559 18.2524
0.2358 8.32 12000 0.3948 46.7337 17.8186
0.2288 9.01 13000 0.3901 46.5480 17.9172
0.2171 9.7 14000 0.3910 46.0236 17.6266
0.2184 10.4 15000 0.3904 46.4387 17.8228
0.2099 11.09 16000 0.3893 45.9744 17.4379
0.216 11.78 17000 0.3889 45.6194 17.2939
0.2095 12.47 18000 0.3895 45.7887 17.4438
0.2056 13.17 19000 0.3882 45.6085 17.2888
0.2064 13.86 20000 0.3885 45.5102 17.3377

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2