metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-cit-do1.5-wd1e-3-lr5
results: []
whisper-large-cit-do1.5-wd1e-3-lr5
This model is a fine-tuned version of openai/whisper-large-v3 on the SF 200 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8623
- Wer: 27.9176
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: 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 |
---|---|---|---|---|
0.9999 | 0.8889 | 10 | 0.8228 | 34.3249 |
0.7031 | 1.7778 | 20 | 0.6328 | 32.2654 |
0.4625 | 2.6667 | 30 | 0.5498 | 30.4348 |
0.2785 | 3.5556 | 40 | 0.5278 | 32.2654 |
0.1827 | 4.4444 | 50 | 0.5557 | 28.6041 |
0.1029 | 5.3333 | 60 | 0.6138 | 28.3753 |
0.06 | 6.2222 | 70 | 0.6641 | 29.7483 |
0.0266 | 7.1111 | 80 | 0.7666 | 29.0618 |
0.0229 | 8.0 | 90 | 0.7114 | 29.9771 |
0.0143 | 8.8889 | 100 | 0.7417 | 27.0023 |
0.0183 | 9.7778 | 110 | 0.8423 | 30.8924 |
0.0115 | 10.6667 | 120 | 0.7061 | 29.0618 |
0.0091 | 11.5556 | 130 | 0.7661 | 28.8330 |
0.0029 | 12.4444 | 140 | 0.8232 | 28.1465 |
0.0064 | 13.3333 | 150 | 0.8213 | 29.5195 |
0.0032 | 14.2222 | 160 | 0.8389 | 27.6888 |
0.0021 | 15.1111 | 170 | 0.8511 | 28.3753 |
0.0023 | 16.0 | 180 | 0.8545 | 28.3753 |
0.0015 | 16.8889 | 190 | 0.8599 | 28.1465 |
0.0013 | 17.7778 | 200 | 0.8623 | 27.9176 |
Framework versions
- Transformers 4.41.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1