---
language:
- dv
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
datasets:
- common_voice
model-index:
- name: XLS-R-300M - Dhivehi- CV8
  results:
  - task: 
      name: Automatic Speech Recognition 
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 8
      type: mozilla-foundation/common_voice_8_0
      args: dv
    metrics:
       - name: Test WER
         type: wer
         value: 29.69
       - name: Test CER
         type: cer
         value: 5.48
---

<!-- 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. -->

# xls-r-300m-dv

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: 0.3149
- Wer: 0.2947

## 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.9617        | 0.66  | 400   | 1.4251          | 0.9768 |
| 0.9081        | 1.33  | 800   | 0.6068          | 0.7290 |
| 0.6575        | 1.99  | 1200  | 0.4700          | 0.6234 |
| 0.548         | 2.65  | 1600  | 0.4158          | 0.5868 |
| 0.5031        | 3.32  | 2000  | 0.4067          | 0.5728 |
| 0.4792        | 3.98  | 2400  | 0.3965          | 0.5673 |
| 0.4344        | 4.64  | 2800  | 0.3862          | 0.5383 |
| 0.4237        | 5.31  | 3200  | 0.3794          | 0.5316 |
| 0.3984        | 5.97  | 3600  | 0.3395          | 0.5177 |
| 0.3788        | 6.63  | 4000  | 0.3528          | 0.5329 |
| 0.3685        | 7.3   | 4400  | 0.3404          | 0.5060 |
| 0.3535        | 7.96  | 4800  | 0.3425          | 0.5069 |
| 0.3391        | 8.62  | 5200  | 0.3576          | 0.5118 |
| 0.331         | 9.29  | 5600  | 0.3259          | 0.4783 |
| 0.3192        | 9.95  | 6000  | 0.3145          | 0.4794 |
| 0.2956        | 10.61 | 6400  | 0.3111          | 0.4650 |
| 0.2936        | 11.28 | 6800  | 0.3303          | 0.4741 |
| 0.2868        | 11.94 | 7200  | 0.3109          | 0.4597 |
| 0.2743        | 12.6  | 7600  | 0.3191          | 0.4557 |
| 0.2654        | 13.27 | 8000  | 0.3286          | 0.4570 |
| 0.2556        | 13.93 | 8400  | 0.3186          | 0.4468 |
| 0.2452        | 14.59 | 8800  | 0.3405          | 0.4582 |
| 0.241         | 15.26 | 9200  | 0.3418          | 0.4533 |
| 0.2313        | 15.92 | 9600  | 0.3388          | 0.4405 |
| 0.2234        | 16.58 | 10000 | 0.3659          | 0.4421 |
| 0.2194        | 17.25 | 10400 | 0.3559          | 0.4490 |
| 0.2168        | 17.91 | 10800 | 0.3452          | 0.4355 |
| 0.2036        | 18.57 | 11200 | 0.3496          | 0.4259 |
| 0.2046        | 19.24 | 11600 | 0.3282          | 0.4245 |
| 0.1917        | 19.9  | 12000 | 0.3201          | 0.4052 |
| 0.1908        | 20.56 | 12400 | 0.3439          | 0.4165 |
| 0.1838        | 21.23 | 12800 | 0.3165          | 0.3950 |
| 0.1828        | 21.89 | 13200 | 0.3332          | 0.4079 |
| 0.1774        | 22.55 | 13600 | 0.3485          | 0.4072 |
| 0.1776        | 23.22 | 14000 | 0.3308          | 0.3868 |
| 0.1693        | 23.88 | 14400 | 0.3153          | 0.3906 |
| 0.1656        | 24.54 | 14800 | 0.3408          | 0.3899 |
| 0.1629        | 25.21 | 15200 | 0.3333          | 0.3854 |
| 0.164         | 25.87 | 15600 | 0.3172          | 0.3775 |
| 0.1505        | 26.53 | 16000 | 0.3105          | 0.3777 |
| 0.1524        | 27.2  | 16400 | 0.3136          | 0.3726 |
| 0.1482        | 27.86 | 16800 | 0.3110          | 0.3710 |
| 0.1423        | 28.52 | 17200 | 0.3299          | 0.3687 |
| 0.1419        | 29.19 | 17600 | 0.3271          | 0.3645 |
| 0.135         | 29.85 | 18000 | 0.3333          | 0.3638 |
| 0.1319        | 30.51 | 18400 | 0.3272          | 0.3640 |
| 0.131         | 31.18 | 18800 | 0.3438          | 0.3636 |
| 0.1252        | 31.84 | 19200 | 0.3266          | 0.3557 |
| 0.1238        | 32.5  | 19600 | 0.3195          | 0.3516 |
| 0.1203        | 33.17 | 20000 | 0.3405          | 0.3534 |
| 0.1159        | 33.83 | 20400 | 0.3287          | 0.3509 |
| 0.115         | 34.49 | 20800 | 0.3474          | 0.3433 |
| 0.108         | 35.16 | 21200 | 0.3245          | 0.3381 |
| 0.1091        | 35.82 | 21600 | 0.3185          | 0.3448 |
| 0.1043        | 36.48 | 22000 | 0.3309          | 0.3363 |
| 0.1034        | 37.15 | 22400 | 0.3288          | 0.3349 |
| 0.1015        | 37.81 | 22800 | 0.3222          | 0.3284 |
| 0.0953        | 38.47 | 23200 | 0.3272          | 0.3315 |
| 0.0966        | 39.14 | 23600 | 0.3196          | 0.3239 |
| 0.0938        | 39.8  | 24000 | 0.3199          | 0.3280 |
| 0.0905        | 40.46 | 24400 | 0.3193          | 0.3166 |
| 0.0893        | 41.13 | 24800 | 0.3224          | 0.3222 |
| 0.0858        | 41.79 | 25200 | 0.3216          | 0.3142 |
| 0.0839        | 42.45 | 25600 | 0.3241          | 0.3135 |
| 0.0819        | 43.12 | 26000 | 0.3260          | 0.3071 |
| 0.0782        | 43.78 | 26400 | 0.3202          | 0.3075 |
| 0.0775        | 44.44 | 26800 | 0.3140          | 0.3067 |
| 0.0751        | 45.11 | 27200 | 0.3118          | 0.3020 |
| 0.0736        | 45.77 | 27600 | 0.3155          | 0.2976 |
| 0.071         | 46.43 | 28000 | 0.3105          | 0.2998 |
| 0.0715        | 47.1  | 28400 | 0.3065          | 0.2993 |
| 0.0668        | 47.76 | 28800 | 0.3161          | 0.2972 |
| 0.0698        | 48.42 | 29200 | 0.3137          | 0.2967 |
| 0.0681        | 49.09 | 29600 | 0.3130          | 0.2971 |
| 0.0651        | 49.75 | 30000 | 0.3149          | 0.2947 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0