---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: kyrgyz_asr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ky
      split: None
      args: ky
    metrics:
    - name: Wer
      type: wer
      value: 38.50746268656716
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# kyrgyz_asr

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3324
- Wer: 38.5075

## Model description

This is a test fine-tuning of Whisper Tiny for the Kyrgyz language using a dataset from the Mozilla Foundation. The code is taken from [this source](https://astanahub.com/en/blog/obuchaem-whisper-small-dlia-raspoznavaniia-kazakhskoi-rechi).

## Intended uses & limitations

More information needed

## Training and evaluation data

mozilla-foundation/common_voice_17_0 (ky - kyrgyz)

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.59          | 0.4735 | 1000 | 0.5917          | 60.8051 |
| 0.4987        | 0.9470 | 2000 | 0.4195          | 47.8517 |
| 0.3932        | 1.4205 | 3000 | 0.3561          | 42.6685 |
| 0.3441        | 1.8939 | 4000 | 0.3324          | 38.5075 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0