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whisper-small-20-11

Automatic Speech Recognition
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metadata
library_name: transformers
language:
  - hi
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small Ori vi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          args: 'config: hi, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 17.65774934574004

Whisper Small Ori vi

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3950
  • Wer: 17.6577

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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • training_steps: 1300
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.1125 0.0667 30 0.9877 30.4667
0.9337 0.1333 60 0.7582 18.3338
0.7185 0.2 90 0.4716 16.3129
0.493 0.2667 120 0.4382 16.1893
0.4328 0.3333 150 0.4298 15.6223
0.4127 0.4 180 0.4208 16.8726
0.3865 0.4667 210 0.4171 20.0422
0.419 0.5333 240 0.4141 17.0835
0.4141 0.6 270 0.4157 15.8258
0.464 0.6667 300 0.4077 16.9235
0.4303 0.7333 330 0.4043 18.4865
0.4418 0.8 360 0.4050 16.7999
0.4786 0.8667 390 0.3981 15.1352
0.4238 0.9333 420 0.3953 17.0907
0.3986 1.0 450 0.3926 16.7054
0.2304 1.0667 480 0.3948 16.3928
0.2583 1.1333 510 0.3943 16.6327
0.2385 1.2 540 0.3997 15.1425
0.2126 1.2667 570 0.3985 15.0552
0.2259 1.3333 600 0.3970 16.5964
0.2237 1.4 630 0.3964 16.5382
0.2344 1.4667 660 0.3983 17.9485
0.2068 1.5333 690 0.3974 17.9703
0.2535 1.6 720 0.3950 17.6577

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.4.0
  • Datasets 3.1.0
  • Tokenizers 0.20.0