---
library_name: transformers
language:
- ba
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- stdbug/common-voice-17-ba
metrics:
- wer
model-index:
- name: Whisper base bashkir
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 17.0 (ba)
      type: stdbug/common-voice-17-ba
      args: 'config: ba, split: test'
    metrics:
    - type: wer
      value: 35.15895985683671
      name: Wer
---

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

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 17.0 (ba) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2265
- Wer: 35.1590

## 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: 8
- 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: 16709
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1674        | 0.9999 | 16709 | 0.2265          | 35.1590 |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0