readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-libri-train360-colab
    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-libri-train360-colab
  
  This model is a fine-tuned version of [GW12/wav2vec2-libri-train100-colab](https://huggingface.co/GW12/wav2vec2-libri-train100-colab) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1101
  - Wer: 0.1002
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 3.1196        | 0.02  | 500    | 0.2020          | 0.1494 |
  | 0.1695        | 0.04  | 1000   | 0.1600          | 0.1462 |
  | 0.1726        | 0.06  | 1500   | 0.1996          | 0.1457 |
  | 0.1654        | 0.08  | 2000   | 0.1531          | 0.1448 |
  | 0.1665        | 0.1   | 2500   | 0.1582          | 0.1491 |
  | 0.1555        | 0.12  | 3000   | 0.1566          | 0.1478 |
  | 0.1562        | 0.13  | 3500   | 0.1555          | 0.1501 |
  | 0.1604        | 0.15  | 4000   | 0.1465          | 0.1422 |
  | 0.1522        | 0.17  | 4500   | 0.1423          | 0.1452 |
  | 0.1534        | 0.19  | 5000   | 0.1375          | 0.1431 |
  | 0.1576        | 0.21  | 5500   | 0.1872          | 0.1421 |
  | 0.1543        | 0.23  | 6000   | 0.1547          | 0.1381 |
  | 0.1501        | 0.25  | 6500   | 0.1446          | 0.1381 |
  | 0.1508        | 0.27  | 7000   | 0.2108          | 0.1507 |
  | 0.1479        | 0.29  | 7500   | 0.1495          | 0.1364 |
  | 0.1474        | 0.31  | 8000   | 0.1571          | 0.1406 |
  | 0.1475        | 0.33  | 8500   | 0.1570          | 0.1390 |
  | 0.1453        | 0.35  | 9000   | 0.1547          | 0.1377 |
  | 0.1465        | 0.37  | 9500   | 0.1633          | 0.1336 |
  | 0.1424        | 0.38  | 10000  | 0.1344          | 0.1358 |
  | 0.1417        | 0.4   | 10500  | 0.2518          | 0.1515 |
  | 0.1427        | 0.42  | 11000  | 0.1697          | 0.1409 |
  | 0.1434        | 0.44  | 11500  | 0.1649          | 0.1373 |
  | 0.1384        | 0.46  | 12000  | 0.1743          | 0.1403 |
  | 0.1394        | 0.48  | 12500  | 0.1485          | 0.1407 |
  | 0.1392        | 0.5   | 13000  | 0.1421          | 0.1352 |
  | 2.3614        | 0.52  | 13500  | 0.9494          | 0.1673 |
  | 0.1621        | 0.54  | 14000  | 0.4273          | 0.1539 |
  | 0.1454        | 0.56  | 14500  | 0.1764          | 0.1399 |
  | 0.1453        | 0.58  | 15000  | 0.1750          | 0.1414 |
  | 0.1375        | 0.6   | 15500  | 0.1845          | 0.1410 |
  | 0.1436        | 0.62  | 16000  | 0.1583          | 0.1413 |
  | 0.1405        | 0.63  | 16500  | 0.1893          | 0.1413 |
  | 0.139         | 0.65  | 17000  | 0.2281          | 0.1619 |
  | 0.1374        | 0.67  | 17500  | 0.1863          | 0.1413 |
  | 0.1386        | 0.69  | 18000  | 0.2301          | 0.1479 |
  | 0.1435        | 0.71  | 18500  | 0.2349          | 0.1579 |
  | 0.1293        | 0.73  | 19000  | 0.1878          | 0.1461 |
  | 0.1311        | 0.75  | 19500  | 0.2092          | 0.1342 |
  | 0.1357        | 0.77  | 20000  | 0.1788          | 0.1421 |
  | 0.1258        | 0.79  | 20500  | 0.1336          | 0.1302 |
  | 0.1284        | 0.81  | 21000  | 0.1459          | 0.1306 |
  | 0.1452        | 0.83  | 21500  | 0.1316          | 0.1319 |
  | 0.1241        | 0.85  | 22000  | 0.1497          | 0.1285 |
  | 0.1292        | 0.87  | 22500  | 0.1417          | 0.1318 |
  | 0.1255        | 0.88  | 23000  | 0.1262          | 0.1305 |
  | 0.1239        | 0.9   | 23500  | 0.1417          | 0.1302 |
  | 0.1237        | 0.92  | 24000  | 0.1704          | 0.1309 |
  | 0.1231        | 0.94  | 24500  | 0.1466          | 0.1308 |
  | 0.1303        | 0.96  | 25000  | 0.2085          | 0.1392 |
  | 0.1252        | 0.98  | 25500  | 0.1514          | 0.1441 |
  | 0.1244        | 1.0   | 26000  | 0.1353          | 0.1282 |
  | 0.1034        | 1.02  | 26500  | 0.1306          | 0.1279 |
  | 0.1035        | 1.04  | 27000  | 0.1785          | 0.1288 |
  | 0.1063        | 1.06  | 27500  | 0.1742          | 0.1311 |
  | 0.1065        | 1.08  | 28000  | 0.1505          | 0.1269 |
  | 0.1093        | 1.1   | 28500  | 0.1394          | 0.1264 |
  | 0.1115        | 1.12  | 29000  | 0.1490          | 0.1325 |
  | 0.1044        | 1.13  | 29500  | 0.5477          | 0.1736 |
  | 0.1003        | 1.15  | 30000  | 0.2347          | 0.1351 |
  | 0.1049        | 1