---
base_model: openai/whisper-small
datasets:
- fleurs
language:
- ru
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Small ru - Chee Li
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Google Fleurs
      type: fleurs
      config: ru_ru
      split: None
      args: 'config: ru split: test'
    metrics:
    - type: wer
      value: 50.354088722608225
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Small ru - Chee Li

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2500
- Wer: 50.3541

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0049        | 5.4645  | 1000 | 0.2170          | 29.2090 |
| 0.0013        | 10.9290 | 2000 | 0.2340          | 43.3993 |
| 0.0006        | 16.3934 | 3000 | 0.2457          | 49.9800 |
| 0.0004        | 21.8579 | 4000 | 0.2500          | 50.3541 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1