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Whisper_medium_ro_VladS_4500_steps_multi_gpu_28_02_2024

Automatic Speech Recognition
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Romanian
whisper
hf-asr-leaderboard
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metadata
language:
  - ro
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - VladS159/common_voice_16_1_romanian_speech_synthesis
metrics:
  - wer
model-index:
  - name: Whisper Medium Ro - Sarbu Vlad - multi gpu - 3
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16.1 + Romanian speech synthesis
          type: VladS159/common_voice_16_1_romanian_speech_synthesis
          args: 'config: ro, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 6.5648576295935746

Whisper Medium Ro - Sarbu Vlad - multi gpu - 3

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 16.1 + Romanian speech synthesis dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0743
  • Wer: 6.5649

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: 10
  • eval_batch_size: 10
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 3
  • total_train_batch_size: 30
  • total_eval_batch_size: 30
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 450
  • training_steps: 4500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2982 0.22 250 0.1790 15.9224
0.1365 0.43 500 0.1313 12.7038
0.1381 0.65 750 0.1126 11.4201
0.1207 0.86 1000 0.1037 11.1432
0.0579 1.08 1250 0.0931 9.6404
0.0665 1.3 1500 0.0929 9.4822
0.0572 1.51 1750 0.0875 9.4457
0.0556 1.73 2000 0.0825 8.6122
0.0458 1.94 2250 0.0778 8.2836
0.0243 2.16 2500 0.0786 7.9095
0.0197 2.38 2750 0.0795 7.8578
0.0229 2.59 3000 0.0758 7.4714
0.0175 2.81 3250 0.0755 7.3497
0.0109 3.03 3500 0.0751 7.0759
0.0098 3.24 3750 0.0773 7.1094
0.0081 3.46 4000 0.0748 6.7778
0.0087 3.67 4250 0.0754 6.6774
0.0086 3.89 4500 0.0743 6.5649

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0
  • Datasets 2.17.0
  • Tokenizers 0.15.1