metadata
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
metrics:
- bleu
- wer
model-index:
- name: Whisper Medium GA-EN Speech Translation Raw
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IWSLT-2023, FLEURS, BiteSize, and SpokenWords
type: ymoslem/IWSLT2023-GA-EN
metrics:
- name: Bleu
type: bleu
value: 30.23
- name: Wer
type: wer
value: 65.37595677622693
Whisper Medium GA-EN Speech Translation Raw
This model is a fine-tuned version of openai/whisper-medium on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset. It achieves the following results on the evaluation set:
- Loss: 1.4321
- Bleu: 30.23
- Chrf: 48.18
- Wer: 65.3760
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|---|---|---|---|---|---|---|
| 2.6013 | 0.0539 | 100 | 2.2401 | 3.18 | 17.57 | 139.4417 |
| 2.5749 | 0.1079 | 200 | 3.0398 | 0.0 | 3.87 | 100.4052 |
| 2.3449 | 0.1618 | 300 | 2.0560 | 7.53 | 24.09 | 121.0266 |
| 2.0392 | 0.2157 | 400 | 1.9721 | 10.7 | 29.63 | 109.7253 |
| 1.9155 | 0.2697 | 500 | 1.9402 | 16.73 | 31.59 | 81.9901 |
| 1.9148 | 0.3236 | 600 | 1.7868 | 11.12 | 32.9 | 117.1544 |
| 1.698 | 0.3776 | 700 | 1.7244 | 20.14 | 36.31 | 83.8811 |
| 1.7283 | 0.4315 | 800 | 1.6586 | 16.74 | 34.0 | 94.5070 |
| 1.5213 | 0.4854 | 900 | 1.6387 | 19.49 | 38.29 | 84.2413 |
| 1.3123 | 0.5394 | 1000 | 1.6292 | 22.27 | 41.45 | 80.2792 |
| 1.1584 | 0.5933 | 1100 | 1.5900 | 25.48 | 42.03 | 74.2008 |
| 1.1734 | 0.6472 | 1200 | 1.5495 | 17.77 | 40.1 | 106.9338 |
| 1.2271 | 0.7012 | 1300 | 1.4978 | 21.7 | 43.63 | 84.2413 |
| 1.0872 | 0.7551 | 1400 | 1.4690 | 25.34 | 43.98 | 74.2909 |
| 0.9331 | 0.8091 | 1500 | 1.4688 | 20.09 | 43.14 | 90.5448 |
| 0.7861 | 0.8630 | 1600 | 1.4284 | 26.49 | 46.76 | 76.4971 |
| 0.8392 | 0.9169 | 1700 | 1.3909 | 27.22 | 46.91 | 73.3904 |
| 0.7236 | 0.9709 | 1800 | 1.4349 | 26.98 | 46.01 | 74.2008 |
| 0.2741 | 1.0248 | 1900 | 1.4279 | 28.92 | 47.63 | 68.3476 |
| 0.2782 | 1.0787 | 2000 | 1.4321 | 30.23 | 48.18 | 65.3760 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1