thiagobarbosa's picture
End of training
b0c0173 verified
metadata
language:
  - pt
license: apache-2.0
base_model: openai/whisper-small
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_0
metrics:
  - wer
model-index:
  - name: Whisper small using Common Voice 16 (pt)
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Mozilla Common Voices - 16.0 - Portuguese
          type: mozilla-foundation/common_voice_16_0
          config: pt
          split: test
          args: pt
        metrics:
          - name: Wer
            type: wer
            value: 17.33354880413704

Whisper small using Common Voice 16 (pt)

This model is a fine-tuned version of openai/whisper-small on the Mozilla Common Voices - 16.0 - Portuguese dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2712
  • Wer: 17.3335
  • Wer Normalized: 11.3321

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Wer Normalized
0.2591 0.37 500 0.2776 20.0727 13.6707
0.2626 0.74 1000 0.2599 19.4005 13.3337
0.1131 1.11 1500 0.2516 18.0414 12.1330
0.1016 1.48 2000 0.2482 18.3597 11.9244
0.1094 1.85 2500 0.2411 17.4192 11.6017
0.0524 2.22 3000 0.2512 17.3546 11.4637
0.0433 2.59 3500 0.2496 17.0895 11.2984
0.0453 2.96 4000 0.2479 17.0362 11.2679
0.0201 3.33 4500 0.2693 17.6632 11.7109
0.0229 3.7 5000 0.2712 17.3335 11.3321

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2
  • Datasets 2.16.1
  • Tokenizers 0.15.1