transformers tensorboard safetensors wav2vec2-bert automatic-speech-recognition mozilla-foundation/common_voice_16_0 generated_from_trainer en base_model:ylacombe/wav2vec2-bert-CV16-en-libri license:mit endpoints_compatible region:us
---
language:
- en
license: mit
base_model: ylacombe/wav2vec2-bert-CV16-en-libri
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_16_0
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-bert-CV16-en-libri-cv
  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-bert-CV16-en-libri-cv

This model is a fine-tuned version of [ylacombe/wav2vec2-bert-CV16-en-libri](https://huggingface.co/ylacombe/wav2vec2-bert-CV16-en-libri) on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - EN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2168
- Wer: 0.1352
- Cer: 0.0525

## 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 3
- total_train_batch_size: 108
- total_eval_batch_size: 36
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15000
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.3302        | 0.05  | 500   | 0.4543          | 0.2333 | 0.0889 |
| 0.2579        | 0.1   | 1000  | 0.4172          | 0.2213 | 0.0832 |
| 0.2527        | 0.15  | 1500  | 0.3999          | 0.2142 | 0.0799 |
| 0.269         | 0.2   | 2000  | 0.3763          | 0.2049 | 0.0768 |
| 0.229         | 0.24  | 2500  | 0.3629          | 0.2029 | 0.0753 |
| 0.2286        | 0.29  | 3000  | 0.3494          | 0.1972 | 0.0733 |
| 0.2422        | 0.34  | 3500  | 0.3365          | 0.1929 | 0.0720 |
| 0.1989        | 0.39  | 4000  | 0.3362          | 0.1900 | 0.0711 |
| 0.2036        | 0.44  | 4500  | 0.3282          | 0.1871 | 0.0696 |
| 0.198         | 0.49  | 5000  | 0.3156          | 0.1803 | 0.0677 |
| 0.1757        | 0.54  | 5500  | 0.3069          | 0.1797 | 0.0682 |
| 0.1966        | 0.59  | 6000  | 0.2984          | 0.1786 | 0.0663 |
| 0.1924        | 0.64  | 6500  | 0.3014          | 0.1795 | 0.0676 |
| 0.19          | 0.68  | 7000  | 0.3059          | 0.1741 | 0.0656 |
| 0.1723        | 0.73  | 7500  | 0.3036          | 0.1758 | 0.0673 |
| 0.1688        | 0.78  | 8000  | 0.2990          | 0.1749 | 0.0670 |
| 0.1776        | 0.83  | 8500  | 0.2984          | 0.1742 | 0.0663 |
| 0.151         | 0.88  | 9000  | 0.3027          | 0.1707 | 0.0651 |
| 0.1609        | 0.93  | 9500  | 0.3001          | 0.1738 | 0.0667 |
| 0.1735        | 0.98  | 10000 | 0.3007          | 0.1748 | 0.0667 |
| 0.1355        | 1.03  | 10500 | 0.2953          | 0.1716 | 0.0665 |
| 0.1538        | 1.08  | 11000 | 0.2872          | 0.1733 | 0.0672 |
| 0.1448        | 1.12  | 11500 | 0.2927          | 0.1695 | 0.0657 |
| 0.1686        | 1.17  | 12000 | 0.2864          | 0.1731 | 0.0673 |
| 0.1506        | 1.22  | 12500 | 0.2891          | 0.1734 | 0.0667 |
| 0.1621        | 1.27  | 13000 | 0.2837          | 0.1722 | 0.0669 |
| 0.1573        | 1.32  | 13500 | 0.2792          | 0.1728 | 0.0660 |
| 0.1566        | 1.37  | 14000 | 0.2747          | 0.1702 | 0.0661 |
| 0.1528        | 1.42  | 14500 | 0.2781          | 0.1754 | 0.0673 |
| 0.153         | 1.47  | 15000 | 0.2900          | 0.1788 | 0.0692 |
| 0.1575        | 1.52  | 15500 | 0.2713          | 0.1758 | 0.0670 |
| 0.1543        | 1.56  | 16000 | 0.2846          | 0.1728 | 0.0666 |
| 0.1354        | 1.61  | 16500 | 0.2781          | 0.1696 | 0.0657 |
| 0.1246        | 1.66  | 17000 | 0.2941          | 0.1729 | 0.0674 |
| 0.1538        | 1.71  | 17500 | 0.2803          | 0.1707 | 0.0662 |
| 0.143         | 1.76  | 18000 | 0.2705          | 0.1669 | 0.0650 |
| 0.1505        | 1.81  | 18500 | 0.2632          | 0.1687 | 0.0653 |
| 0.1415        | 1.86  | 19000 | 0.2623          | 0.1651 | 0.0636 |
| 0.1402        | 1.91  | 19500 | 0.2607          | 0.1668 | 0.0647 |
| 0.1354        | 1.96  | 20000 | 0.2649          | 0.1643 | 0.0635 |
| 0.1234        | 2.0   | 20500 | 0.2684          | 0.1616 | 0.0636 |
| 0.129         | 2.05  | 21000 | 0.2589          | 0.1595 | 0.0624 |
| 0.1198        | 2.1   | 21500 | 0.2629          | 0.1629 | 0.0631 |
| 0.1083        | 2.15  | 22000 | 0.2608          | 0.1604 | 0.0627 |
| 0.1446        | 2.2   | 22500 | 0.2598          | 0.1614 | 0.0629 |
| 0.1315        | 2.25  | 23000 | 0.2681          | 0.1640 | 0.0643 |
| 0.1218        | 2.3   | 23500 | 0.2616          | 0.1607 | 0.0639 |
| 0.1167        | 2.35  | 24000 | 0.2732          | 0.1599 | 0.0627 |
| 0.1009        | 2.4   | 24500 | 0.2566          | 0.1600 | 0.0627 |
| 0.1133        | 2.44  | 25000 | 0.2533          | 0.1566 | 0.0614 |
| 0.1135        | 2.49  | 25500 | 0.2470          | 0.1561 | 0.0606 |
| 0.1042        | 2.54  | 26000 | 0.2508          | 0.1546 | 0.0604 |
| 0.1238        | 2.59  | 26500 | 0.2568          | 0.1565 | 0.0616 |
| 0.1178        | 2.64  | 27000 | 0.2564          | 0.1574 | 0.0615 |
| 0.1207        | 2.69  | 27500 | 0.2456          | 0.1552 | 0.0605 |
| 0.1112        | 2.74  | 28000 | 0.2434          | 0.1516 | 0.0595 |
| 0.1097        | 2.79  | 28500 | 0.2467          | 0.1550 | 0.0605 |
| 0.1253        | 2.84  | 29000 | 0.2428          | 0.1541 | 0.0600 |
| 0.1172        | 2.88  | 29500 | 0.2399          | 0.1513 | 0.0592 |
| 0.12          | 2.93  | 30000 | 0.2393          | 0.1518 | 0.0589 |
| 0.0976        | 2.98  | 30500 | 0.2442          | 0.1520 | 0.0596 |
| 0.1422        | 3.03  | 31000 | 0.2398          | 0.1503 | 0.0588 |
| 0.1285        | 3.08  | 31500 | 0.2446          | 0.1518 | 0.0591 |
| 0.122         | 3.13  | 32000 | 0.2401          | 0.1503 | 0.0587 |
| 0.1132        | 3.18  | 32500 | 0.2437          | 0.1514 | 0.0591 |
| 0.1275        | 3.23  | 33000 | 0.2466          | 0.1485 | 0.0584 |
| 0.1299        | 3.28  | 33500 | 0.2380          | 0.1463 | 0.0571 |
| 0.1129        | 3.32  | 34000 | 0.2416          | 0.1472 | 0.0576 |
| 0.1367        | 3.37  | 34500 | 0.2418          | 0.1479 | 0.0581 |
| 0.1166        | 3.42  | 35000 | 0.2418          | 0.1458 | 0.0573 |
| 0.1264        | 3.47  | 35500 | 0.2349          | 0.1449 | 0.0569 |
| 0.1325        | 3.52  | 36000 | 0.2332          | 0.1458 | 0.0567 |
| 0.1208        | 3.57  | 36500 | 0.2372          | 0.1469 | 0.0578 |
| 0.1309        | 3.62  | 37000 | 0.2354          | 0.1455 | 0.0570 |
| 0.1303        | 3.67  | 37500 | 0.2281          | 0.1435 | 0.0559 |
| 0.1193        | 3.72  | 38000 | 0.2306          | 0.1438 | 0.0563 |
| 0.1148        | 3.76  | 38500 | 0.2259          | 0.1439 | 0.0558 |
| 0.1066        | 3.81  | 39000 | 0.2293          | 0.1421 | 0.0558 |
| 0.0899        | 3.86  | 39500 | 0.2266          | 0.1408 | 0.0552 |
| 0.123         | 3.91  | 40000 | 0.2254          | 0.1419 | 0.0555 |
| 0.1162        | 3.96  | 40500 | 0.2251          | 0.1422 | 0.0557 |
| 0.0856        | 4.01  | 41000 | 0.2253          | 0.1401 | 0.0549 |
| 0.0983        | 4.06  | 41500 | 0.2258          | 0.1389 | 0.0547 |
| 0.0893        | 4.11  | 42000 | 0.2260          | 0.1406 | 0.0547 |
| 0.0892        | 4.16  | 42500 | 0.2272          | 0.1391 | 0.0544 |
| 0.0761        | 4.2   | 43000 | 0.2301          | 0.1396 | 0.0547 |
| 0.0931        | 4.25  | 43500 | 0.2259          | 0.1377 | 0.0538 |
| 0.081         | 4.3   | 44000 | 0.2221          | 0.1389 | 0.0540 |
| 0.0878        | 4.35  | 44500 | 0.2232          | 0.1383 | 0.0538 |
| 0.0837        | 4.4   | 45000 | 0.2258          | 0.1381 | 0.0540 |
| 0.0917        | 4.45  | 45500 | 0.2211          | 0.1371 | 0.0535 |
| 0.0736        | 4.5   | 46000 | 0.2226          | 0.1364 | 0.0534 |
| 0.0728        | 4.55  | 46500 | 0.2218          | 0.1358 | 0.0531 |
| 0.0824        | 4.6   | 47000 | 0.2205          | 0.1365 | 0.0533 |
| 0.0794        | 4.64  | 47500 | 0.2198          | 0.1359 | 0.0529 |
| 0.0823        | 4.69  | 48000 | 0.2199          | 0.1354 | 0.0527 |
| 0.0849        | 4.74  | 48500 | 0.2176          | 0.1348 | 0.0525 |
| 0.095         | 4.79  | 49000 | 0.2185          | 0.1354 | 0.0529 |
| 0.0951        | 4.84  | 49500 | 0.2163          | 0.1354 | 0.0527 |
| 0.0902        | 4.89  | 50000 | 0.2163          | 0.1350 | 0.0525 |
| 0.066         | 4.94  | 50500 | 0.2167          | 0.1350 | 0.0525 |
| 0.0776        | 4.99  | 51000 | 0.2169          | 0.1351 | 0.0524 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
