---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-en-phoneme-ctc-60h-balanced
  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-xls-r-300m-en-phoneme-ctc-60h-balanced

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1532
- Per: 0.0378
- Phoneme Accuracy: 0.9622

## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Per    | Phoneme Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:----------------:|
| 4.9502        | 0.3185  | 500   | 4.3875          | 1.0    | 0.0              |
| 3.6506        | 0.6369  | 1000  | 3.5930          | 1.0    | 0.0              |
| 3.6533        | 0.9554  | 1500  | 3.5748          | 1.0    | 0.0              |
| 3.6035        | 1.2739  | 2000  | 3.5249          | 1.0    | 0.0              |
| 1.4335        | 1.5924  | 2500  | 0.7984          | 0.1635 | 0.8365           |
| 0.6011        | 1.9108  | 3000  | 0.3071          | 0.0706 | 0.9294           |
| 0.4718        | 2.2293  | 3500  | 0.2315          | 0.0576 | 0.9424           |
| 0.4498        | 2.5478  | 4000  | 0.2091          | 0.0528 | 0.9472           |
| 0.3683        | 2.8662  | 4500  | 0.1897          | 0.0504 | 0.9496           |
| 0.331         | 3.1847  | 5000  | 0.1810          | 0.0472 | 0.9528           |
| 0.3256        | 3.5032  | 5500  | 0.1725          | 0.0458 | 0.9542           |
| 0.2949        | 3.8217  | 6000  | 0.1643          | 0.0441 | 0.9559           |
| 0.2894        | 4.1401  | 6500  | 0.1662          | 0.0436 | 0.9564           |
| 0.2542        | 4.4586  | 7000  | 0.1592          | 0.0430 | 0.9570           |
| 0.2422        | 4.7771  | 7500  | 0.1563          | 0.0433 | 0.9567           |
| 0.2485        | 5.0955  | 8000  | 0.1577          | 0.0417 | 0.9583           |
| 0.2592        | 5.4140  | 8500  | 0.1538          | 0.0427 | 0.9573           |
| 0.2295        | 5.7325  | 9000  | 0.1520          | 0.0412 | 0.9588           |
| 0.215         | 6.0510  | 9500  | 0.1493          | 0.0402 | 0.9598           |
| 0.2304        | 6.3694  | 10000 | 0.1508          | 0.0410 | 0.9590           |
| 0.2111        | 6.6879  | 10500 | 0.1475          | 0.0397 | 0.9603           |
| 0.2007        | 7.0064  | 11000 | 0.1477          | 0.0393 | 0.9607           |
| 0.1947        | 7.3248  | 11500 | 0.1529          | 0.0390 | 0.9610           |
| 0.1895        | 7.6433  | 12000 | 0.1512          | 0.0390 | 0.9610           |
| 0.2178        | 7.9618  | 12500 | 0.1443          | 0.0396 | 0.9604           |
| 0.2088        | 8.2803  | 13000 | 0.1467          | 0.0393 | 0.9607           |
| 0.1813        | 8.5987  | 13500 | 0.1488          | 0.0383 | 0.9617           |
| 0.1796        | 8.9172  | 14000 | 0.1450          | 0.0384 | 0.9616           |
| 0.1657        | 9.2357  | 14500 | 0.1474          | 0.0382 | 0.9618           |
| 0.1687        | 9.5541  | 15000 | 0.1478          | 0.0384 | 0.9616           |
| 0.1759        | 9.8726  | 15500 | 0.1452          | 0.0379 | 0.9621           |
| 0.1699        | 10.1911 | 16000 | 0.1495          | 0.0376 | 0.9624           |
| 0.1737        | 10.5096 | 16500 | 0.1492          | 0.0374 | 0.9626           |
| 0.1784        | 10.8280 | 17000 | 0.1465          | 0.0385 | 0.9615           |
| 0.1697        | 11.1465 | 17500 | 0.1480          | 0.0385 | 0.9615           |
| 0.1618        | 11.4650 | 18000 | 0.1477          | 0.0375 | 0.9625           |
| 0.1499        | 11.7834 | 18500 | 0.1474          | 0.0398 | 0.9602           |
| 0.1363        | 12.1019 | 19000 | 0.1532          | 0.0378 | 0.9622           |


### Framework versions

- Transformers 4.57.6
- Pytorch 2.11.0+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2