whisper-medium-5k
This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1389
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
--Original sentence:
集団内のすべての個体が特定の表現形質に関して同一である場合 それらは単形性と呼ばれます。
When all individuals in a population are identical with respect to a particular phenotypic trait, they are called monomorphic.
--sin2piusc/whisper-medium-5ksteps:
集団内のすべての個体が特定の表現形質に関して同一である場合 それらは単形性と呼ばれます
When all individuals in a population are identical with respect to a particular phenotypic trait, they are called monomorphic.
--openai/whisper-medium:
集団内のすべての個体が特定の表現形式に関して同一である場合、それらは単形性と呼ばれます。
If all individuals in a population are identical with respect to a particular form of expression, they are called monomorphic.
--sin2piusc/whisper-medium-5ksteps:
When I drink alcohol, I can become quite unsightly, so I ordered a glass of water and stopped drinking.
--openai/whisper-medium:
I don't like drinking alcohol, so I asked for water and avoided it.
--Original sentence:
I can be quite unsightly when I'm drunk, so I abstained from alcohol and mainly drank water.
Training procedure
On a laptop running windows.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.3706 | 0.3697 | 200 | 1.1454 |
0.7963 | 0.7394 | 400 | 0.5219 |
0.2503 | 1.1091 | 600 | 0.2178 |
0.2062 | 1.4787 | 800 | 0.2005 |
0.1867 | 1.8484 | 1000 | 0.1869 |
0.1644 | 2.2181 | 1200 | 0.1738 |
0.1501 | 2.5878 | 1400 | 0.1630 |
0.1386 | 2.9575 | 1600 | 0.1524 |
0.1186 | 3.3272 | 1800 | 0.1458 |
0.1086 | 3.6969 | 2000 | 0.1424 |
0.1019 | 4.0665 | 2200 | 0.1364 |
0.0871 | 4.4362 | 2400 | 0.1347 |
0.085 | 4.8059 | 2600 | 0.1326 |
0.0746 | 5.1756 | 2800 | 0.1336 |
0.0729 | 5.5453 | 3000 | 0.1312 |
0.0688 | 5.9150 | 3200 | 0.1316 |
0.0598 | 6.2847 | 3400 | 0.1328 |
0.0574 | 6.6543 | 3600 | 0.1340 |
0.0598 | 7.0240 | 3800 | 0.1336 |
0.0481 | 7.3937 | 4000 | 0.1356 |
0.0514 | 7.7634 | 4200 | 0.1366 |
0.0465 | 8.1331 | 4400 | 0.1382 |
0.0428 | 8.5028 | 4600 | 0.1378 |
0.043 | 8.8725 | 4800 | 0.1384 |
0.0425 | 9.2421 | 5000 | 0.1389 |
Framework versions
- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
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