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