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whisper-small-mn-8

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

  • Loss: 0.2421
  • Wer: 26.5185
  • Cer: 9.4681

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: 32
  • eval_batch_size: 32
  • 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: 15000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.3717 0.35 1000 0.4004 46.9576 16.9664
0.286 0.69 2000 0.3129 37.3935 13.5504
0.2287 1.04 3000 0.2768 33.1931 11.7806
0.2257 1.39 4000 0.2590 30.7243 11.0232
0.2029 1.73 5000 0.2428 29.2003 10.4144
0.1691 2.08 6000 0.2408 28.4357 10.0306
0.1626 2.43 7000 0.2369 28.0588 10.0486
0.1588 2.77 8000 0.2321 27.2340 9.6819
0.1271 3.12 9000 0.2349 26.8407 9.5574
0.1263 3.47 10000 0.2356 27.1630 9.6519
0.1314 3.81 11000 0.2340 26.5567 9.4278
0.1062 4.16 12000 0.2390 26.6332 9.5162
0.1081 4.5 13000 0.2398 26.5840 9.5085
0.1033 4.85 14000 0.2402 26.7096 9.4801
0.097 5.2 15000 0.2421 26.5185 9.4681

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2
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Datasets used to train bayartsogt/whisper-small-mn-8

Collection including bayartsogt/whisper-small-mn-8

Evaluation results