---
language:
- uk
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small Ukrainian
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: uk
      split: test
      args: 'config: uk, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 20.106509860483175
---

<!-- 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-uk

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3673
- Wer: 20.1065

## 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: 6e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1947        | 0.94  | 1000  | 0.2269          | 22.7263 |
| 0.1034        | 1.89  | 2000  | 0.2102          | 20.6058 |
| 0.0572        | 2.83  | 3000  | 0.2192          | 20.3908 |
| 0.0261        | 3.77  | 4000  | 0.2483          | 21.0204 |
| 0.0112        | 4.72  | 5000  | 0.2758          | 21.1480 |
| 0.0058        | 5.66  | 6000  | 0.3166          | 20.3270 |
| 0.0026        | 6.6   | 7000  | 0.3268          | 20.5877 |
| 0.0017        | 7.55  | 8000  | 0.3483          | 20.0455 |
| 0.0006        | 8.49  | 9000  | 0.3635          | 20.0996 |
| 0.0005        | 9.43  | 10000 | 0.3673          | 20.1065 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2