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---
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_11_0
language:
- ru
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Small Ru - v4
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: ru
split: test
args: 'config: ru, split: test'
metrics:
- type: wer
value: 11.993477274677849
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 Small Ru - v4
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2167
- Wer Ortho: 16.3879
- Wer: 11.9935
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.1695 | 0.4921 | 500 | 0.2079 | 17.6749 | 13.3434 |
| 0.1548 | 0.9843 | 1000 | 0.1894 | 16.4416 | 12.2240 |
| 0.0704 | 1.4764 | 1500 | 0.1878 | 16.1107 | 12.0106 |
| 0.0722 | 1.9685 | 2000 | 0.1854 | 15.7395 | 11.7887 |
| 0.0328 | 2.4606 | 2500 | 0.1927 | 15.7822 | 11.6404 |
| 0.0344 | 2.9528 | 3000 | 0.1929 | 15.5746 | 11.6060 |
| 0.0147 | 3.4449 | 3500 | 0.2059 | 15.6992 | 11.5141 |
| 0.0148 | 3.9370 | 4000 | 0.2046 | 15.7859 | 11.5962 |
| 0.0067 | 4.4291 | 4500 | 0.2169 | 16.0374 | 11.6784 |
| 0.0078 | 4.9213 | 5000 | 0.2167 | 16.3879 | 11.9935 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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