---
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: wavlm-common_voice-ur
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: ur
      split: test
      args: ur
    metrics:
    - name: Wer
      type: wer
      value: 0.37960668937751624
---

<!-- 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. -->

# wavlm-common_voice-ur

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.3796

## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.9073        | 0.11  | 100   | inf             | 1.0    |
| 3.3187        | 0.22  | 200   | inf             | 1.0    |
| 2.9683        | 0.32  | 300   | inf             | 0.9991 |
| 2.454         | 0.43  | 400   | inf             | 0.9915 |
| 1.1169        | 0.54  | 500   | inf             | 0.7906 |
| 1.5943        | 0.65  | 600   | inf             | 0.7260 |
| 0.9991        | 0.75  | 700   | inf             | 0.7305 |
| 1.0608        | 0.86  | 800   | inf             | 0.6655 |
| 1.4739        | 0.97  | 900   | inf             | 0.6120 |
| 0.8682        | 1.08  | 1000  | inf             | 0.6087 |
| 0.8025        | 1.18  | 1100  | inf             | 0.5991 |
| 0.8468        | 1.29  | 1200  | inf             | 0.5605 |
| 0.5896        | 1.4   | 1300  | inf             | 0.5550 |
| 0.6304        | 1.51  | 1400  | inf             | 0.5441 |
| 0.6533        | 1.61  | 1500  | inf             | 0.5297 |
| 0.7636        | 1.72  | 1600  | inf             | 0.5210 |
| 0.5155        | 1.83  | 1700  | inf             | 0.5331 |
| 0.6266        | 1.94  | 1800  | inf             | 0.5182 |
| 0.4286        | 2.05  | 1900  | inf             | 0.4956 |
| 0.527         | 2.15  | 2000  | inf             | 0.4935 |
| 0.4919        | 2.26  | 2100  | inf             | 0.4933 |
| 0.3977        | 2.37  | 2200  | inf             | 0.5015 |
| 0.5349        | 2.48  | 2300  | inf             | 0.4942 |
| 0.5066        | 2.58  | 2400  | inf             | 0.4684 |
| 0.6734        | 2.69  | 2500  | inf             | 0.4870 |
| 0.5411        | 2.8   | 2600  | inf             | 0.4919 |
| 0.3451        | 2.91  | 2700  | inf             | 0.4607 |
| 0.3913        | 3.01  | 2800  | inf             | 0.4558 |
| 0.3046        | 3.12  | 2900  | inf             | 0.4685 |
| 0.2954        | 3.23  | 3000  | inf             | 0.4638 |
| 0.5469        | 3.34  | 3100  | inf             | 0.4495 |
| 0.2334        | 3.44  | 3200  | inf             | 0.4547 |
| 0.3119        | 3.55  | 3300  | inf             | 0.4619 |
| 0.6393        | 3.66  | 3400  | inf             | 0.4541 |
| 0.4133        | 3.77  | 3500  | inf             | 0.4456 |
| 0.4946        | 3.88  | 3600  | inf             | 0.4369 |
| 0.3484        | 3.98  | 3700  | inf             | 0.4335 |
| 0.3996        | 4.09  | 3800  | inf             | 0.4717 |
| 0.2754        | 4.2   | 3900  | inf             | 0.4414 |
| 0.3141        | 4.31  | 4000  | inf             | 0.4390 |
| 0.2231        | 4.41  | 4100  | inf             | 0.4353 |
| 0.2673        | 4.52  | 4200  | inf             | 0.4410 |
| 0.2911        | 4.63  | 4300  | inf             | 0.4337 |
| 0.3643        | 4.74  | 4400  | inf             | 0.4362 |
| 0.2706        | 4.84  | 4500  | inf             | 0.4359 |
| 0.2464        | 4.95  | 4600  | inf             | 0.4249 |
| 0.1453        | 5.06  | 4700  | inf             | 0.4293 |
| 0.2619        | 5.17  | 4800  | inf             | 0.4201 |
| 0.1888        | 5.27  | 4900  | inf             | 0.4222 |
| 0.2571        | 5.38  | 5000  | inf             | 0.4333 |
| 0.1653        | 5.49  | 5100  | inf             | 0.4192 |
| 0.2102        | 5.6   | 5200  | inf             | 0.4232 |
| 0.1456        | 5.71  | 5300  | inf             | 0.4198 |
| 0.3314        | 5.81  | 5400  | inf             | 0.4169 |
| 0.1703        | 5.92  | 5500  | inf             | 0.4118 |
| 0.1546        | 6.03  | 5600  | inf             | 0.4147 |
| 0.2065        | 6.14  | 5700  | inf             | 0.4291 |
| 0.1792        | 6.24  | 5800  | inf             | 0.4175 |
| 0.2433        | 6.35  | 5900  | inf             | 0.4157 |
| 0.352         | 6.46  | 6000  | inf             | 0.4083 |
| 0.2406        | 6.57  | 6100  | inf             | 0.4341 |
| 0.2397        | 6.67  | 6200  | inf             | 0.4185 |
| 0.2145        | 6.78  | 6300  | inf             | 0.4147 |
| 0.1733        | 6.89  | 6400  | inf             | 0.4150 |
| 0.1867        | 7.0   | 6500  | inf             | 0.4154 |
| 0.612         | 7.1   | 6600  | inf             | 0.4159 |
| 0.1413        | 7.21  | 6700  | inf             | 0.4162 |
| 0.2074        | 7.32  | 6800  | inf             | 0.4146 |
| 0.1362        | 7.43  | 6900  | inf             | 0.4087 |
| 0.2971        | 7.53  | 7000  | inf             | 0.4061 |
| 0.1443        | 7.64  | 7100  | inf             | 0.4132 |
| 0.3066        | 7.75  | 7200  | inf             | 0.4059 |
| 0.2163        | 7.86  | 7300  | inf             | 0.4026 |
| 0.1251        | 7.97  | 7400  | inf             | 0.4022 |
| 0.154         | 8.07  | 7500  | inf             | 0.3980 |
| 0.1809        | 8.18  | 7600  | inf             | 0.4030 |
| 0.0985        | 8.29  | 7700  | inf             | 0.3992 |
| 0.1672        | 8.4   | 7800  | inf             | 0.4049 |
| 0.1508        | 8.5   | 7900  | inf             | 0.3985 |
| 0.1893        | 8.61  | 8000  | inf             | 0.3999 |
| 0.1045        | 8.72  | 8100  | inf             | 0.4014 |
| 0.2569        | 8.83  | 8200  | inf             | 0.3976 |
| 0.2654        | 8.93  | 8300  | inf             | 0.4021 |
| 0.0641        | 9.04  | 8400  | inf             | 0.3964 |
| 0.1145        | 9.15  | 8500  | inf             | 0.3995 |
| 0.1808        | 9.26  | 8600  | inf             | 0.3960 |
| 0.0766        | 9.36  | 8700  | inf             | 0.3938 |
| 0.1537        | 9.47  | 8800  | inf             | 0.3909 |
| 0.2864        | 9.58  | 8900  | inf             | 0.4028 |
| 0.1372        | 9.69  | 9000  | inf             | 0.3970 |
| 0.06          | 9.8   | 9100  | inf             | 0.3911 |
| 0.0831        | 9.9   | 9200  | inf             | 0.3954 |
| 0.1469        | 10.01 | 9300  | inf             | 0.3952 |
| 0.0683        | 10.12 | 9400  | inf             | 0.3899 |
| 0.0694        | 10.23 | 9500  | inf             | 0.3918 |
| 0.0919        | 10.33 | 9600  | inf             | 0.3895 |
| 0.1842        | 10.44 | 9700  | inf             | 0.3945 |
| 0.0581        | 10.55 | 9800  | inf             | 0.3979 |
| 0.1397        | 10.66 | 9900  | inf             | 0.3911 |
| 0.0657        | 10.76 | 10000 | inf             | 0.3886 |
| 0.1316        | 10.87 | 10100 | inf             | 0.3877 |
| 0.1434        | 10.98 | 10200 | inf             | 0.3858 |
| 0.05          | 11.09 | 10300 | inf             | 0.3842 |
| 0.0565        | 11.19 | 10400 | inf             | 0.3873 |
| 0.1696        | 11.3  | 10500 | inf             | 0.3873 |
| 0.0819        | 11.41 | 10600 | inf             | 0.3901 |
| 0.0631        | 11.52 | 10700 | inf             | 0.3927 |
| 0.1276        | 11.63 | 10800 | inf             | 0.3868 |
| 0.1002        | 11.73 | 10900 | inf             | 0.3848 |
| 0.081         | 11.84 | 11000 | inf             | 0.3873 |
| 0.1745        | 11.95 | 11100 | inf             | 0.3895 |
| 0.097         | 12.06 | 11200 | inf             | 0.4021 |
| 0.0875        | 12.16 | 11300 | inf             | 0.3876 |
| 0.027         | 12.27 | 11400 | inf             | 0.3873 |
| 0.0859        | 12.38 | 11500 | inf             | 0.3863 |
| 0.1192        | 12.49 | 11600 | inf             | 0.3799 |
| 0.1055        | 12.59 | 11700 | inf             | 0.3795 |
| 0.0603        | 12.7  | 11800 | inf             | 0.3785 |
| 0.111         | 12.81 | 11900 | inf             | 0.3783 |
| 0.0313        | 12.92 | 12000 | inf             | 0.3800 |
| 0.0241        | 13.02 | 12100 | inf             | 0.3796 |
| 0.1072        | 13.13 | 12200 | inf             | 0.3803 |
| 0.1758        | 13.24 | 12300 | inf             | 0.3809 |
| 0.1334        | 13.35 | 12400 | inf             | 0.3794 |
| 0.1372        | 13.46 | 12500 | inf             | 0.3798 |
| 0.1919        | 13.56 | 12600 | inf             | 0.3791 |
| 0.1753        | 13.67 | 12700 | inf             | 0.3781 |
| 0.294         | 13.78 | 12800 | inf             | 0.3788 |
| 0.3132        | 13.89 | 12900 | inf             | 0.3786 |
| 0.0486        | 13.99 | 13000 | inf             | 0.3778 |
| 0.1199        | 14.1  | 13100 | inf             | 0.3777 |
| 0.0381        | 14.21 | 13200 | inf             | 0.3808 |
| 0.0875        | 14.32 | 13300 | inf             | 0.3795 |
| 0.0122        | 14.42 | 13400 | inf             | 0.3797 |
| 0.1417        | 14.53 | 13500 | inf             | 0.3780 |
| 0.1754        | 14.64 | 13600 | inf             | 0.3788 |
| 0.0426        | 14.75 | 13700 | inf             | 0.3780 |
| 0.0309        | 14.85 | 13800 | inf             | 0.3787 |
| 0.1447        | 14.96 | 13900 | inf             | 0.3796 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2