whisper-atcosim3
This model is a fine-tuned version of openai/whisper-medium on the atco2_atcosim dataset. It achieves the following results on the evaluation set:
- Loss: 0.0476
- Wer: 0.0198
Model description
This model is a special ASR model, derived doing fine-tuning of OpenAI Whisper model on ATC conversations. The base model is: OpenAI Whisper Medium
Intended uses & limitations
More information needed
Training and evaluation data
The model has been trained on the atco2_atcosim dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- training_steps: 600
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.8218 | 0.2 | 50 | 0.3785 | 0.1451 |
| 0.1429 | 0.39 | 100 | 0.1213 | 0.0714 |
| 0.1155 | 0.59 | 150 | 0.0807 | 0.0517 |
| 0.0764 | 0.79 | 200 | 0.0652 | 0.0272 |
| 0.0724 | 0.98 | 250 | 0.0607 | 0.0393 |
| 0.0357 | 1.18 | 300 | 0.0569 | 0.0242 |
| 0.03 | 1.38 | 350 | 0.0553 | 0.0243 |
| 0.0325 | 1.57 | 400 | 0.0556 | 0.0228 |
| 0.03 | 1.77 | 450 | 0.0501 | 0.0242 |
| 0.0232 | 1.97 | 500 | 0.0485 | 0.0205 |
| 0.0143 | 2.16 | 550 | 0.0480 | 0.0194 |
| 0.0105 | 2.36 | 600 | 0.0476 | 0.0198 |
Framework versions
- Transformers 4.29.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.11.0