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metadata
library_name: transformers
language:
  - yue
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_15_0
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: Whisper Small Canontese X v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16.1
          type: mozilla-foundation/common_voice_15_0
          config: zh-HK
          split: None
          args: 'config: zh-HK, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 59.33048433048433
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 15.0
          type: mozilla-foundation/common_voice_16_1
        metrics:
          - name: Wer
            type: wer
            value: 59.33048433048433

Whisper Small Canontese X v2

This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.1 and the Common Voice 15.0 datasets. It achieves the following results on the evaluation set:

  • Loss: 0.2720
  • Wer: 59.3305

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 3000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2939 0.7918 1000 0.3060 65.9188
0.1498 1.5835 2000 0.2803 61.6809
0.0662 2.3753 3000 0.2720 59.3305

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1