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whisper-large-zh-cv11

Automatic Speech Recognition
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PyTorch
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Chinese
whisper
whisper-event
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metadata
language:
  - zh
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Large Chinese (Mandarin)
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 zh-CN
          type: mozilla-foundation/common_voice_11_0
          config: zh-CN
          split: validation[:1000]
          args: zh-CN
        metrics:
          - name: Wer
            type: wer
            value: 51.67420814479639

Whisper Large Chinese (Mandarin)

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

  • Loss: 0.2435
  • Wer: 51.6742
  • Cer: 8.5279

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: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • training_steps: 20000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.3314 0.83 1000 0.2110 65.7014 10.8047
0.2747 1.66 2000 0.2005 58.1900 9.4191
0.1989 2.49 3000 0.1983 56.1991 9.0939
0.1142 3.31 4000 0.2076 55.0226 9.1589
0.0747 4.14 5000 0.2131 56.3801 9.0483
0.0709 4.97 6000 0.2165 54.6606 8.9768
0.0432 5.8 7000 0.2222 54.0271 8.9508
0.0261 6.63 8000 0.2299 54.4796 9.0353
0.0152 7.46 9000 0.2290 52.7602 8.8076
0.0054 8.28 10000 0.2435 51.6742 8.5279
0.0028 9.11 11000 0.2421 53.0317 8.9833
0.0045 9.94 12000 0.2462 52.9412 8.7751
0.0016 10.77 13000 0.2501 52.3077 8.9573

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2