---
language:
- hu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: Whisper medium Hu CV18
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 18.0
      type: fleurs
      config: hu_hu
      split: None
      args: hu_hu
    metrics:
    - name: Wer
      type: wer
      value: 20.222211012182512
---

<!-- 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 CV18

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the Common Voice 18.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4280
- Wer Ortho: 26.6073
- Wer: 20.2222

## 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: 6.25e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.195         | 0.2757 | 200  | 0.4397          | 29.9304   | 25.2390 |
| 0.1548        | 0.5513 | 400  | 0.4146          | 28.6874   | 23.6903 |
| 0.126         | 0.8270 | 600  | 0.4022          | 28.3332   | 22.4632 |
| 0.077         | 1.1027 | 800  | 0.4045          | 27.5831   | 21.3673 |
| 0.0744        | 1.3784 | 1000 | 0.4096          | 27.8566   | 21.4102 |
| 0.0718        | 1.6540 | 1200 | 0.3955          | 26.5619   | 20.7733 |
| 0.0681        | 1.9297 | 1400 | 0.3990          | 26.5267   | 20.6207 |
| 0.032         | 2.2054 | 1600 | 0.4056          | 25.8913   | 20.1680 |
| 0.0323        | 2.4810 | 1800 | 0.4232          | 26.1182   | 20.2878 |
| 0.0356        | 2.7567 | 2000 | 0.4280          | 26.6073   | 20.2222 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.3.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1