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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
|