---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: xls-r-300-cv17-bulgarian
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: bg
      split: validation
      args: bg
    metrics:
    - name: Wer
      type: wer
      value: 0.2967878948765596
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning-polish/runs/snulovqw)
# xls-r-300-cv17-bulgarian

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_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4329
- Wer: 0.2968
- Cer: 0.0726

## 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 4.0388        | 0.6579  | 100  | 4.1422          | 1.0    | 1.0    |
| 3.047         | 1.3158  | 200  | 3.0730          | 1.0    | 1.0    |
| 2.7349        | 1.9737  | 300  | 2.7601          | 0.9939 | 0.9946 |
| 0.6047        | 2.6316  | 400  | 0.6984          | 0.7954 | 0.1942 |
| 0.3868        | 3.2895  | 500  | 0.5550          | 0.5994 | 0.1519 |
| 0.3423        | 3.9474  | 600  | 0.4548          | 0.4804 | 0.1195 |
| 0.1942        | 4.6053  | 700  | 0.3973          | 0.4277 | 0.1034 |
| 0.1754        | 5.2632  | 800  | 0.4166          | 0.4391 | 0.1055 |
| 0.1734        | 5.9211  | 900  | 0.4146          | 0.4195 | 0.1018 |
| 0.1089        | 6.5789  | 1000 | 0.3859          | 0.3867 | 0.0937 |
| 0.233         | 7.2368  | 1100 | 0.4183          | 0.4054 | 0.1005 |
| 0.1519        | 7.8947  | 1200 | 0.4459          | 0.4151 | 0.1030 |
| 0.1176        | 8.5526  | 1300 | 0.4026          | 0.3845 | 0.0937 |
| 0.0997        | 9.2105  | 1400 | 0.3849          | 0.3590 | 0.0869 |
| 0.1266        | 9.8684  | 1500 | 0.4281          | 0.3781 | 0.0947 |
| 0.0945        | 10.5263 | 1600 | 0.4471          | 0.3983 | 0.0979 |
| 0.0575        | 11.1842 | 1700 | 0.4290          | 0.3660 | 0.0897 |
| 0.0854        | 11.8421 | 1800 | 0.4258          | 0.3749 | 0.0938 |
| 0.0558        | 12.5    | 1900 | 0.4242          | 0.3644 | 0.0907 |
| 0.0774        | 13.1579 | 2000 | 0.4339          | 0.3616 | 0.0888 |
| 0.0397        | 13.8158 | 2100 | 0.4155          | 0.3581 | 0.0882 |
| 0.0603        | 14.4737 | 2200 | 0.4681          | 0.3737 | 0.0943 |
| 0.0723        | 15.1316 | 2300 | 0.4446          | 0.3560 | 0.0875 |
| 0.0746        | 15.7895 | 2400 | 0.4430          | 0.3573 | 0.0889 |
| 0.0727        | 16.4474 | 2500 | 0.4549          | 0.3470 | 0.0870 |
| 0.0458        | 17.1053 | 2600 | 0.4581          | 0.3520 | 0.0873 |
| 0.0694        | 17.7632 | 2700 | 0.4414          | 0.3575 | 0.0896 |
| 0.0462        | 18.4211 | 2800 | 0.4235          | 0.3261 | 0.0802 |
| 0.0539        | 19.0789 | 2900 | 0.4496          | 0.3329 | 0.0810 |
| 0.0368        | 19.7368 | 3000 | 0.4043          | 0.3406 | 0.0846 |
| 0.0347        | 20.3947 | 3100 | 0.4367          | 0.3225 | 0.0789 |
| 0.019         | 21.0526 | 3200 | 0.4487          | 0.3272 | 0.0801 |
| 0.0361        | 21.7105 | 3300 | 0.4272          | 0.3241 | 0.0785 |
| 0.0475        | 22.3684 | 3400 | 0.4324          | 0.3191 | 0.0781 |
| 0.0341        | 23.0263 | 3500 | 0.4564          | 0.3398 | 0.0847 |
| 0.0454        | 23.6842 | 3600 | 0.4415          | 0.3188 | 0.0789 |
| 0.0346        | 24.3421 | 3700 | 0.4187          | 0.3072 | 0.0751 |
| 0.1315        | 25.0    | 3800 | 0.4480          | 0.3124 | 0.0765 |
| 0.0663        | 25.6579 | 3900 | 0.4488          | 0.3151 | 0.0779 |
| 0.0225        | 26.3158 | 4000 | 0.4372          | 0.3006 | 0.0739 |
| 0.0382        | 26.9737 | 4100 | 0.4164          | 0.2987 | 0.0730 |
| 0.0194        | 27.6316 | 4200 | 0.4190          | 0.2942 | 0.0718 |
| 0.0101        | 28.2895 | 4300 | 0.4328          | 0.2960 | 0.0726 |
| 0.0224        | 28.9474 | 4400 | 0.4302          | 0.2944 | 0.0720 |
| 0.0174        | 29.6053 | 4500 | 0.4329          | 0.2968 | 0.0726 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1