---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-russian
  results: []
---

<!-- 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. -->

# wav2vec2-russian

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2210
- Wer: 0.4966

## 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: 0.0001
- 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: 1000
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.0548        | 0.25  | 500   | 4.1857          | 0.9999 |
| 3.0204        | 0.5   | 1000  | 1.9996          | 0.9998 |
| 1.8692        | 0.74  | 1500  | 1.6426          | 0.8698 |
| 1.5154        | 0.99  | 2000  | 1.6156          | 0.7481 |
| 1.3677        | 1.24  | 2500  | 2.1281          | 0.7120 |
| 1.3223        | 1.49  | 3000  | 1.5192          | 0.6846 |
| 1.2512        | 1.73  | 3500  | 1.0993          | 0.6634 |
| 1.2257        | 1.98  | 4000  | 1.1039          | 0.6493 |
| 1.1418        | 2.23  | 4500  | 1.0170          | 0.6241 |
| 1.1213        | 2.48  | 5000  | 0.8436          | 0.6191 |
| 1.112         | 2.73  | 5500  | 0.7326          | 0.6102 |
| 1.0912        | 2.97  | 6000  | 0.7054          | 0.5976 |
| 1.0465        | 3.22  | 6500  | 1.0887          | 0.5941 |
| 1.0215        | 3.47  | 7000  | 1.4577          | 0.5793 |
| 1.0244        | 3.72  | 7500  | 1.6058          | 0.5855 |
| 1.0254        | 3.96  | 8000  | 1.3366          | 0.5750 |
| 0.9558        | 4.21  | 8500  | 0.8088          | 0.5644 |
| 0.966         | 4.46  | 9000  | 0.9650          | 0.5636 |
| 0.9674        | 4.71  | 9500  | 0.9047          | 0.5532 |
| 0.9373        | 4.96  | 10000 | 1.0342          | 0.5422 |
| 0.9126        | 5.2   | 10500 | 1.2346          | 0.5462 |
| 0.9063        | 5.45  | 11000 | 1.2696          | 0.5412 |
| 0.9126        | 5.7   | 11500 | 1.4693          | 0.5317 |
| 0.8936        | 5.95  | 12000 | 1.9096          | 0.5369 |
| 0.8621        | 6.19  | 12500 | 1.6382          | 0.5326 |
| 0.8695        | 6.44  | 13000 | 0.9466          | 0.5252 |
| 0.8423        | 6.69  | 13500 | 1.6286          | 0.5355 |
| 0.8494        | 6.94  | 14000 | 0.8368          | 0.5264 |
| 0.8354        | 7.19  | 14500 | 0.6893          | 0.5216 |
| 0.8133        | 7.43  | 15000 | 0.5916          | 0.5175 |
| 0.8147        | 7.68  | 15500 | 0.7813          | 0.5221 |
| 0.8258        | 7.93  | 16000 | 1.3814          | 0.5129 |
| 0.8079        | 8.18  | 16500 | 0.8368          | 0.5176 |
| 0.7868        | 8.42  | 17000 | 0.9456          | 0.5159 |
| 0.7955        | 8.67  | 17500 | 0.7412          | 0.5170 |
| 0.7921        | 8.92  | 18000 | 0.6256          | 0.5066 |
| 0.7536        | 9.17  | 18500 | 0.8792          | 0.5101 |
| 0.7667        | 9.42  | 19000 | 1.0615          | 0.5032 |
| 0.772         | 9.66  | 19500 | 1.1312          | 0.5086 |
| 0.7418        | 9.91  | 20000 | 1.3485          | 0.4990 |
| 0.7577        | 10.16 | 20500 | 1.0788          | 0.5037 |
| 0.7311        | 10.41 | 21000 | 0.9978          | 0.5032 |
| 0.7419        | 10.65 | 21500 | 1.3925          | 0.5017 |
| 0.74          | 10.9  | 22000 | 1.4191          | 0.4981 |
| 0.7297        | 11.15 | 22500 | 1.1082          | 0.4994 |
| 0.737         | 11.4  | 23000 | 1.1208          | 0.4971 |
| 0.7266        | 11.65 | 23500 | 1.1595          | 0.4952 |
| 0.7091        | 11.89 | 24000 | 1.2210          | 0.4966 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6