whisper-small-tl-1 / README.md
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metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Small Tagalog
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs fil_ph
          type: google/fleurs
          config: fil_ph
          split: test
          args: fil_ph
        metrics:
          - name: Wer
            type: wer
            value: 20.66525391659729

Whisper Small Tagalog

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

  • Loss: 0.6250
  • Wer: 20.6653

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
0.0759 76.0 1000 0.5043 22.2622
0.0099 153.0 2000 0.5464 21.3653
0.0043 230.0 3000 0.5707 21.2215
0.0024 307.0 4000 0.5909 20.9377
0.0015 384.0 5000 0.6090 20.6728
0.001 461.0 6000 0.6250 20.6653
0.0007 538.0 7000 0.6395 20.8582
0.0005 615.0 8000 0.6519 20.9415
0.0004 692.0 9000 0.6613 20.9112
0.0004 769.0 10000 0.6653 20.9377

Framework versions

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