---
base_model: openai/whisper-base
datasets:
- fleurs
language:
- ru
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Russian 8000 - 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: 25.55451630144308
      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 Russian 8000 - Chee Li

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co./openai/whisper-base) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4957
- Wer: 25.5545

## 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: 850
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0635        | 5.4645  | 1000 | 0.3433          | 22.5882 |
| 0.0051        | 10.9290 | 2000 | 0.3879          | 23.0492 |
| 0.0019        | 16.3934 | 3000 | 0.4186          | 23.8976 |
| 0.0011        | 21.8579 | 4000 | 0.4422          | 24.4522 |
| 0.0007        | 27.3224 | 5000 | 0.4613          | 25.0    |
| 0.0005        | 32.7869 | 6000 | 0.4781          | 25.3140 |
| 0.0004        | 38.2514 | 7000 | 0.4907          | 25.4209 |
| 0.0003        | 43.7158 | 8000 | 0.4957          | 25.5545 |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1