---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-russian-colab-beam_search_test
  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-large-xls-r-300m-russian-colab-beam_search_test

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7619
- Wer: 0.4680

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.0158       | 4.16  | 100  | 5.4134          | 1.0    |
| 4.0394        | 8.33  | 200  | 3.4304          | 1.0    |
| 3.2721        | 12.49 | 300  | 3.2273          | 1.0    |
| 3.1277        | 16.66 | 400  | 2.8023          | 0.9984 |
| 1.3791        | 20.82 | 500  | 0.9888          | 0.8546 |
| 0.3659        | 24.99 | 600  | 0.7602          | 0.6304 |
| 0.1858        | 29.16 | 700  | 0.7965          | 0.6156 |
| 0.1403        | 33.33 | 800  | 0.7998          | 0.5839 |
| 0.1173        | 37.49 | 900  | 0.8353          | 0.5941 |
| 0.0917        | 41.66 | 1000 | 0.8272          | 0.5522 |
| 0.0743        | 45.82 | 1100 | 0.8342          | 0.5471 |
| 0.063         | 49.99 | 1200 | 0.7988          | 0.5352 |
| 0.0528        | 54.16 | 1300 | 0.7740          | 0.5201 |
| 0.0456        | 58.33 | 1400 | 0.7636          | 0.5165 |
| 0.0389        | 62.49 | 1500 | 0.7922          | 0.5161 |
| 0.0329        | 66.66 | 1600 | 0.8035          | 0.5158 |
| 0.0283        | 70.82 | 1700 | 0.7873          | 0.4832 |
| 0.0255        | 74.99 | 1800 | 0.7853          | 0.4870 |
| 0.0236        | 79.16 | 1900 | 0.8236          | 0.5045 |
| 0.0202        | 83.33 | 2000 | 0.7661          | 0.4796 |
| 0.0165        | 87.49 | 2100 | 0.7584          | 0.4680 |
| 0.0156        | 91.66 | 2200 | 0.7685          | 0.4772 |
| 0.0149        | 95.82 | 2300 | 0.7519          | 0.4696 |
| 0.0126        | 99.99 | 2400 | 0.7619          | 0.4680 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3