---
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
model-index:
- name: wav2vec2-large-xls-r-300m-ipa
  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-ipa

This model was trained from scratch on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7309

## 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: 0.0001
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 240
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch    | Step  | Validation Loss |
|:-------------:|:--------:|:-----:|:---------------:|
| 0.1677        | 3.6866   | 200   | 1.0381          |
| 0.1214        | 7.3733   | 400   | 0.5607          |
| 0.1272        | 11.0599  | 600   | 0.5442          |
| 0.135         | 14.7465  | 800   | 0.5933          |
| 0.0824        | 18.4332  | 1000  | 0.6316          |
| 0.0711        | 22.1198  | 1200  | 0.5971          |
| 0.0653        | 25.8065  | 1400  | 0.6050          |
| 0.0499        | 29.4931  | 1600  | 0.6699          |
| 0.0516        | 33.1797  | 1800  | 0.6940          |
| 0.0507        | 36.8664  | 2000  | 0.7045          |
| 0.0478        | 40.5530  | 2200  | 0.7603          |
| 0.045         | 44.2396  | 2400  | 0.7415          |
| 0.0419        | 47.9263  | 2600  | 0.7341          |
| 0.0344        | 51.6129  | 2800  | 0.7328          |
| 0.0354        | 55.2995  | 3000  | 0.8550          |
| 0.0268        | 58.9862  | 3200  | 0.7838          |
| 0.0383        | 62.6728  | 3400  | 0.7995          |
| 0.0371        | 66.3594  | 3600  | 0.7765          |
| 0.0264        | 70.0461  | 3800  | 0.8186          |
| 0.0212        | 73.7327  | 4000  | 0.7439          |
| 0.0177        | 77.4194  | 4200  | 0.7830          |
| 0.0204        | 81.1060  | 4400  | 0.8145          |
| 0.0254        | 84.7926  | 4600  | 0.8149          |
| 0.0257        | 88.4793  | 4800  | 0.7663          |
| 0.0126        | 92.1659  | 5000  | 0.7704          |
| 0.0196        | 95.8525  | 5200  | 0.7660          |
| 0.0185        | 99.5392  | 5400  | 0.8580          |
| 0.0236        | 103.2258 | 5600  | 0.8169          |
| 0.0141        | 106.9124 | 5800  | 0.8222          |
| 0.0142        | 110.5991 | 6000  | 0.9001          |
| 0.0098        | 114.2857 | 6200  | 0.8509          |
| 0.0372        | 117.9724 | 6400  | 0.7734          |
| 0.0075        | 121.6590 | 6600  | 0.8911          |
| 0.0118        | 125.3456 | 6800  | 0.8347          |
| 0.0115        | 129.0323 | 7000  | 0.8926          |
| 0.0164        | 132.7189 | 7200  | 0.7985          |
| 0.006         | 136.4055 | 7400  | 0.7571          |
| 0.0124        | 140.0922 | 7600  | 0.8476          |
| 0.0141        | 143.7788 | 7800  | 0.8071          |
| 0.0065        | 147.4654 | 8000  | 0.7630          |
| 0.0095        | 151.1521 | 8200  | 0.7161          |
| 0.0063        | 154.8387 | 8400  | 0.8165          |
| 0.0107        | 158.5253 | 8600  | 0.7411          |
| 0.0037        | 162.2120 | 8800  | 0.7424          |
| 0.0045        | 165.8986 | 9000  | 0.7611          |
| 0.0044        | 169.5853 | 9200  | 0.7278          |
| 0.0043        | 173.2719 | 9400  | 0.7396          |
| 0.0025        | 176.9585 | 9600  | 0.7215          |
| 0.0029        | 180.6452 | 9800  | 0.7551          |
| 0.0067        | 184.3318 | 10000 | 0.7518          |
| 0.0062        | 188.0184 | 10200 | 0.7668          |
| 0.0065        | 191.7051 | 10400 | 0.7433          |
| 0.0024        | 195.3917 | 10600 | 0.7942          |
| 0.0039        | 199.0783 | 10800 | 0.7448          |
| 0.0024        | 202.7650 | 11000 | 0.7290          |
| 0.0036        | 206.4516 | 11200 | 0.7678          |
| 0.0001        | 210.1382 | 11400 | 0.7390          |
| 0.0009        | 213.8249 | 11600 | 0.7292          |
| 0.0008        | 217.5115 | 11800 | 0.7383          |
| 0.0009        | 221.1982 | 12000 | 0.7435          |
| 0.0009        | 224.8848 | 12200 | 0.7324          |
| 0.0007        | 228.5714 | 12400 | 0.7444          |
| 0.0002        | 232.2581 | 12600 | 0.7228          |
| 0.0005        | 235.9447 | 12800 | 0.7309          |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1