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metadata
language:
  - cs
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
base_model: openai/whisper-large-v2
model-index:
  - name: Whisper Large-v2 Czech CV11 v2
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 cs
          type: mozilla-foundation/common_voice_11_0
          config: cs
          split: test
          args: cs
        metrics:
          - type: wer
            value: 9.045873924973758
            name: Wer

Whisper Large-v2 Czech CV11 v2

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 cs dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2120
  • Wer: 9.0459

Model description

Fine tuned with deepspeed optimization and batch_size: 32.

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: 32
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • 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: 5000

Training results

Training Loss Epoch Step Validation Loss Wer
0.0106 4.24 1000 0.1625 9.9888
0.0034 8.47 2000 0.1841 9.8304
0.0011 12.71 3000 0.1917 9.4031
0.0004 16.95 4000 0.2075 9.1177
0.0003 21.19 5000 0.2120 9.0459

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2