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---
language:
- hu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Medium HU
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: hu
split: test
args: hu
metrics:
- name: Wer
type: wer
value: 14.829034193161366
---
<!-- 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 HU
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2699
- Wer Ortho: 17.1763
- Wer: 14.8290
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 20000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:-------:|
| 0.0804 | 1.38 | 2000 | 0.1977 | 19.2869 | 16.6612 |
| 0.038 | 2.76 | 4000 | 0.2028 | 18.2211 | 15.7494 |
| 0.014 | 4.14 | 6000 | 0.2190 | 17.9961 | 15.3466 |
| 0.0107 | 5.51 | 8000 | 0.2328 | 17.3490 | 14.9370 |
| 0.0144 | 6.89 | 10000 | 0.2376 | 17.4153 | 14.9559 |
| 0.0049 | 8.27 | 12000 | 0.2424 | 16.9984 | 14.6953 |
| 0.0071 | 9.65 | 14000 | 0.2594 | 17.6961 | 15.3586 |
| 0.0037 | 11.03 | 16000 | 0.2546 | 17.2007 | 14.8667 |
| 0.0078 | 12.41 | 18000 | 0.2644 | 17.5757 | 15.1495 |
| 0.0043 | 13.78 | 20000 | 0.2699 | 17.1763 | 14.8290 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|