---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-300m-colab-only-gn
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: gn
      split: test
      args: gn
    metrics:
    - name: Wer
      type: wer
      value: 0.5229303156640858
---

<!-- 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-300m-colab-only-gn

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_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5274
- Wer: 0.5229

## 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: 100
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 20.8148       | 0.45  | 25   | 13.5976         | 1.0    |
| 7.0188        | 0.9   | 50   | 5.5263          | 1.0    |
| 4.1285        | 1.35  | 75   | 3.6078          | 1.0    |
| 3.338         | 1.8   | 100  | 3.3217          | 1.0    |
| 3.2829        | 2.25  | 125  | 3.2781          | 1.0    |
| 3.272         | 2.7   | 150  | 3.2601          | 1.0    |
| 3.2224        | 3.15  | 175  | 3.2234          | 1.0    |
| 3.1949        | 3.6   | 200  | 3.1998          | 1.0    |
| 3.1846        | 4.05  | 225  | 3.1841          | 1.0    |
| 3.1615        | 4.5   | 250  | 3.1719          | 1.0    |
| 3.1367        | 4.95  | 275  | 3.1132          | 1.0    |
| 3.0111        | 5.41  | 300  | 2.9344          | 1.0    |
| 2.7786        | 5.86  | 325  | 2.5643          | 1.0    |
| 2.2106        | 6.31  | 350  | 1.8132          | 1.0    |
| 1.6365        | 6.76  | 375  | 1.4008          | 0.9982 |
| 1.178         | 7.21  | 400  | 1.0678          | 0.9845 |
| 0.8903        | 7.66  | 425  | 0.8744          | 0.9369 |
| 0.7429        | 8.11  | 450  | 0.7213          | 0.8752 |
| 0.5931        | 8.56  | 475  | 0.6681          | 0.8189 |
| 0.5592        | 9.01  | 500  | 0.6622          | 0.7895 |
| 0.4316        | 9.46  | 525  | 0.6177          | 0.7644 |
| 0.4098        | 9.91  | 550  | 0.5599          | 0.7874 |
| 0.3176        | 10.36 | 575  | 0.5649          | 0.7001 |
| 0.3142        | 10.81 | 600  | 0.5828          | 0.6867 |
| 0.3227        | 11.26 | 625  | 0.5505          | 0.6736 |
| 0.275         | 11.71 | 650  | 0.5432          | 0.6540 |
| 0.2783        | 12.16 | 675  | 0.5372          | 0.6462 |
| 0.2316        | 12.61 | 700  | 0.5078          | 0.6379 |
| 0.2281        | 13.06 | 725  | 0.5059          | 0.6161 |
| 0.2191        | 13.51 | 750  | 0.5175          | 0.5956 |
| 0.1911        | 13.96 | 775  | 0.5216          | 0.5929 |
| 0.1731        | 14.41 | 800  | 0.5069          | 0.5789 |
| 0.1743        | 14.86 | 825  | 0.5207          | 0.5971 |
| 0.1755        | 15.32 | 850  | 0.5436          | 0.6307 |
| 0.1568        | 15.77 | 875  | 0.5374          | 0.6001 |
| 0.1629        | 16.22 | 900  | 0.5429          | 0.6102 |
| 0.1418        | 16.67 | 925  | 0.5089          | 0.5762 |
| 0.136         | 17.12 | 950  | 0.5291          | 0.5878 |
| 0.1354        | 17.57 | 975  | 0.5381          | 0.5840 |
| 0.1351        | 18.02 | 1000 | 0.5511          | 0.5947 |
| 0.1252        | 18.47 | 1025 | 0.5204          | 0.5643 |
| 0.1215        | 18.92 | 1050 | 0.5385          | 0.5613 |
| 0.1188        | 19.37 | 1075 | 0.5063          | 0.5718 |
| 0.1209        | 19.82 | 1100 | 0.5211          | 0.5488 |
| 0.1091        | 20.27 | 1125 | 0.5245          | 0.5557 |
| 0.112         | 20.72 | 1150 | 0.4910          | 0.5587 |
| 0.102         | 21.17 | 1175 | 0.5192          | 0.5581 |
| 0.0947        | 21.62 | 1200 | 0.5500          | 0.5718 |
| 0.1066        | 22.07 | 1225 | 0.5288          | 0.5488 |
| 0.1011        | 22.52 | 1250 | 0.5180          | 0.5438 |
| 0.0974        | 22.97 | 1275 | 0.5089          | 0.5277 |
| 0.0926        | 23.42 | 1300 | 0.5222          | 0.5301 |
| 0.0871        | 23.87 | 1325 | 0.5135          | 0.5366 |
| 0.0808        | 24.32 | 1350 | 0.4990          | 0.5331 |
| 0.0739        | 24.77 | 1375 | 0.5281          | 0.5351 |
| 0.0841        | 25.23 | 1400 | 0.5321          | 0.5360 |
| 0.0743        | 25.68 | 1425 | 0.5508          | 0.5447 |
| 0.0809        | 26.13 | 1450 | 0.5228          | 0.5396 |
| 0.0631        | 26.58 | 1475 | 0.5284          | 0.5351 |
| 0.0788        | 27.03 | 1500 | 0.5250          | 0.5289 |
| 0.0754        | 27.48 | 1525 | 0.5204          | 0.5259 |
| 0.0663        | 27.93 | 1550 | 0.5275          | 0.5313 |
| 0.0645        | 28.38 | 1575 | 0.5288          | 0.5259 |
| 0.0729        | 28.83 | 1600 | 0.5268          | 0.5259 |
| 0.0656        | 29.28 | 1625 | 0.5277          | 0.5232 |
| 0.0703        | 29.73 | 1650 | 0.5274          | 0.5229 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1