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whisper-base-vi-1

Automatic Speech Recognition
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TensorBoard
Safetensors
Vietnamese
whisper
whisper-event
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metadata
language:
  - vi
license: apache-2.0
base_model: openai/whisper-base
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_0
metrics:
  - wer
model-index:
  - name: Whisper Base Vietnamese
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_16_0 vi
          type: mozilla-foundation/common_voice_16_0
          config: vi
          split: test
          args: vi
        metrics:
          - name: Wer
            type: wer
            value: 37.80239886155723

Whisper Base Vietnamese

This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_16_0 vi dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7770
  • Wer: 37.8024

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: 5e-07
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.6043 33.0 500 0.9039 42.6408
0.2836 66.0 1000 0.7761 38.3106
0.1593 99.0 1500 0.7770 37.8024
0.0835 133.0 2000 0.8019 37.8634
0.0395 166.0 2500 0.8317 38.1582
0.0217 199.0 3000 0.8563 38.2395
0.0146 233.0 3500 0.8744 38.2801
0.0107 266.0 4000 0.8893 38.4733
0.0082 299.0 4500 0.9031 38.3310
0.0065 333.0 5000 0.9155 38.4326
0.0053 366.0 5500 0.9267 38.6156
0.0044 399.0 6000 0.9381 38.7579
0.0037 433.0 6500 0.9486 38.7782
0.0032 466.0 7000 0.9580 39.0120
0.0028 499.0 7500 0.9669 39.1441
0.0025 533.0 8000 0.9747 39.1746
0.0022 566.0 8500 0.9810 39.2864
0.0021 599.0 9000 0.9866 39.2763
0.002 633.0 9500 0.9899 39.3271
0.0019 666.0 10000 0.9911 39.3271

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.2.dev0
  • Tokenizers 0.15.0