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license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-basque
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-basque
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1445
- Wer: 8.2615
## 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-05
- train_batch_size: 96
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1909 | 0.12 | 500 | 0.2820 | 22.5178 |
| 0.1253 | 0.25 | 1000 | 0.2133 | 16.1484 |
| 0.1046 | 0.37 | 1500 | 0.1899 | 12.7139 |
| 0.0874 | 0.5 | 2000 | 0.1793 | 11.4088 |
| 0.0836 | 0.62 | 2500 | 0.1621 | 10.5470 |
| 0.0726 | 0.74 | 3000 | 0.1597 | 10.1161 |
| 0.0707 | 0.87 | 3500 | 0.1498 | 9.4355 |
| 0.0652 | 0.99 | 4000 | 0.1470 | 8.5737 |
| 0.0416 | 1.11 | 4500 | 0.1482 | 8.5925 |
| 0.0415 | 1.24 | 5000 | 0.1490 | 8.6299 |
| 0.0394 | 1.36 | 5500 | 0.1474 | 8.0929 |
| 0.0381 | 1.49 | 6000 | 0.1425 | 8.3489 |
| 0.038 | 1.61 | 6500 | 0.1414 | 8.2990 |
| 0.0333 | 1.73 | 7000 | 0.1391 | 8.2553 |
| 0.0342 | 1.86 | 7500 | 0.1382 | 8.3864 |
| 0.0341 | 1.98 | 8000 | 0.1386 | 8.4301 |
| 0.0196 | 2.11 | 8500 | 0.1447 | 8.1429 |
| 0.0208 | 2.23 | 9000 | 0.1448 | 8.3115 |
| 0.018 | 2.35 | 9500 | 0.1449 | 8.3177 |
| 0.0172 | 2.48 | 10000 | 0.1445 | 8.2615 |
### Framework versions
- Transformers 4.38.0
- Pytorch 2.1.1+cu121
- Datasets 2.8.0
- Tokenizers 0.15.2
|