README.md

metadata

library_name: transformers
language:
  - dv
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Ｍedium Dv - Leon Lee
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 13
          type: mozilla-foundation/common_voice_13_0
          config: dv
          split: test
          args: dv
        metrics:
          - name: Wer
            type: wer
            value: 8.432729422401502

Whisper Ｍedium Dv - Leon Lee

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 13 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2803
• Wer Ortho: 48.8335
• Wer: 8.4327

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: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 8000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.1344	0.8157	500	0.1613	59.9206	12.1049
0.0732	1.6313	1000	0.1382	52.9285	10.2271
0.0411	2.4470	1500	0.1447	52.3087	9.7628
0.0244	3.2626	2000	0.1538	51.6749	9.4534
0.0164	4.0783	2500	0.1839	53.8617	9.4290
0.0162	4.8940	3000	0.1734	51.7863	9.0604
0.0086	5.7096	3500	0.1962	50.8949	9.0222
0.0048	6.5253	4000	0.2299	50.7904	8.8205
0.003	7.3409	4500	0.2336	50.7487	8.8344
0.0017	8.1566	5000	0.2303	50.2472	8.6275
0.0017	8.9723	5500	0.2455	49.9896	8.6327
0.0005	9.7879	6000	0.2551	49.8015	8.5371
0.0001	10.6036	6500	0.2682	48.8962	8.4414
0.0	11.4192	7000	0.2732	48.6663	8.4206
0.0	12.2349	7500	0.2800	48.8892	8.4605
0.0	13.0506	8000	0.2803	48.8335	8.4327

Framework versions

• Transformers 4.48.1
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0