---
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
language:
- hu
widget:
- example_title: Sample 1
  src: https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample1.flac
- example_title: Sample 2
  src: https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample2.flac
metrics:
- wer
pipeline_tag: automatic-speech-recognition
model-index:
- name: Whisper Small Hungarian V2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.0 - Hungarian
      type: mozilla-foundation/common_voice_16_0
      config: hu
      split: test
      args: hu
    metrics:
    - name: Wer
      type: wer
      value: 8.1
      verified: true

---
<!-- 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 Small Hu v2

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set (at step 12000 best WER&CER modell):
- Loss: 0.1098
- Wer Ortho: 9.3671
- Wer: 8.1026

## 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: 1.25e-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: 500
- training_steps: 15000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:-------:|
| 0.2871        | 0.33  | 1000  | 0.3110          | 31.4525   | 28.3160 |
| 0.1967        | 0.67  | 2000  | 0.2263          | 23.7836   | 20.9280 |
| 0.1542        | 1.0   | 3000  | 0.1808          | 19.5351   | 16.7032 |
| 0.1083        | 1.34  | 4000  | 0.1658          | 17.2709   | 14.8236 |
| 0.1054        | 1.67  | 5000  | 0.1438          | 14.9554   | 12.6653 |
| 0.0468        | 2.01  | 6000  | 0.1248          | 12.8869   | 10.8034 |
| 0.0437        | 2.34  | 7000  | 0.1235          | 12.2817   | 10.3913 |
| 0.05          | 2.68  | 8000  | 0.1197          | 11.4958   | 9.6887  |
| 0.022         | 3.01  | 9000  | 0.1119          | 10.4932   | 8.9623  |
| 0.0286        | 3.35  | 10000 | 0.1141          | 10.4149   | 9.0780  |
| 0.0233        | 3.68  | 11000 | 0.1150          | 10.0536   | 8.6837  |
| 0.0124        | 4.02  | 12000 | 0.1098          | 9.3671    | 8.1026  |
| 0.0165        | 4.35  | 13000 | 0.1143          | 9.7947    | 8.3813  |
| 0.0174        | 4.69  | 14000 | 0.1136          | 9.3249    | 8.0729  |
| 0.0107        | 5.02  | 15000 | 0.1150          | 9.2527    | 8.2745  |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0