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Whisper_medium_ro_VladS_10000_steps_3_gpus_small_lr

Automatic Speech Recognition
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Romanian
whisper
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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_17_0_romanian_speech_synthesis
metrics:
  - wer
model-index:
  - name: Whisper Medium Ro - Sarbu Vlad - 3 gpus
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0 + Romanian speech synthesis
          type: VladS159/common_voice_17_0_romanian_speech_synthesis
          args: 'config: ro, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 6.359842831470257

Whisper Medium Ro - Sarbu Vlad - 3 gpus

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 + Romanian speech synthesis dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0878
  • Wer: 6.3598

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-06
  • 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: 1000
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2346 0.42 500 0.1671 15.7382
0.1441 0.85 1000 0.1183 12.1318
0.0887 1.27 1500 0.1026 10.3012
0.0821 1.7 2000 0.0926 9.8230
0.0403 2.12 2500 0.0871 9.1590
0.0394 2.55 3000 0.0914 8.8788
0.0381 2.97 3500 0.0790 8.2909
0.0178 3.4 4000 0.0825 7.7579
0.0185 3.82 4500 0.0776 7.4929
0.0102 4.25 5000 0.0818 7.4350
0.0105 4.67 5500 0.0803 6.9599
0.0054 5.1 6000 0.0830 6.9264
0.0046 5.52 6500 0.0827 6.6949
0.005 5.95 7000 0.0831 6.7101
0.0043 6.37 7500 0.0840 6.6462
0.003 6.8 8000 0.0845 6.5274
0.0017 7.22 8500 0.0875 6.5121
0.0015 7.65 9000 0.0867 6.4025
0.0012 8.07 9500 0.0875 6.3568
0.001 8.5 10000 0.0878 6.3598

Framework versions

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