./whisper-large-cit-synth-do0.15-wd0-lr1e-06
This model is a fine-tuned version of openai/whisper-large-v3 on the SF 200 synth 2000 dataset. It achieves the following results on the evaluation set:
- Loss: 1.5469
- Wer: 52.8152
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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 200
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
2.3143 | 0.0808 | 10 | 2.3730 | 62.3073 |
2.3217 | 0.1616 | 20 | 2.2910 | 60.9606 |
2.4278 | 0.2424 | 30 | 2.1875 | 59.9059 |
2.0697 | 0.3232 | 40 | 2.1152 | 59.2082 |
1.9675 | 0.4040 | 50 | 2.0410 | 59.1270 |
1.891 | 0.4848 | 60 | 1.9668 | 57.3098 |
1.7818 | 0.5657 | 70 | 1.8887 | 56.4660 |
1.7774 | 0.6465 | 80 | 1.8145 | 55.1030 |
1.8454 | 0.7273 | 90 | 1.7588 | 53.7238 |
1.6584 | 0.8081 | 100 | 1.7129 | 53.0586 |
2.0202 | 0.8889 | 110 | 1.6738 | 52.4258 |
1.6375 | 0.9697 | 120 | 1.6416 | 51.9552 |
1.3514 | 1.0505 | 130 | 1.6172 | 51.6307 |
1.5016 | 1.1313 | 140 | 1.5977 | 51.1601 |
1.8013 | 1.2121 | 150 | 1.5811 | 50.9330 |
1.4723 | 1.2929 | 160 | 1.5693 | 52.9937 |
1.5952 | 1.3737 | 170 | 1.5605 | 52.8152 |
1.4724 | 1.4545 | 180 | 1.5527 | 52.8476 |
1.4115 | 1.5354 | 190 | 1.5488 | 52.7990 |
1.5161 | 1.6162 | 200 | 1.5469 | 52.8152 |
Framework versions
- Transformers 4.41.2
- Pytorch 1.13.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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