---
language:
- kk
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Large v3 Kazakh
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: kk
      split: test
      args: 'config: kk, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 188.06064434617815
---

<!-- 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 Large v3 Kazakh

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5842
- Wer: 188.0606

## 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: 8
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Wer      |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| 0.0003        | 28.5714  | 1000 | 0.4718          | 546.6835 |
| 0.0           | 57.1429  | 2000 | 0.5506          | 175.4264 |
| 0.0           | 85.7143  | 3000 | 0.5751          | 185.3759 |
| 0.0           | 114.2857 | 4000 | 0.5842          | 188.0606 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 1.12.0
- Datasets 2.20.0
- Tokenizers 0.19.1