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# 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        | 1.17  | 30500  | 0.2001          | 0.1347 |
| 0.1068        | 1.19  | 31000  | 0.1528          | 0.1255 |
| 0.1069        | 1.21  | 31500  | 0.1528          | 0.1266 |
| 0.1042        | 1.23  | 32000  | 0.2272          | 0.1318 |
| 0.1073        | 1.25  | 32500  | 0.5753          | 0.1869 |
| 0.1021        | 1.27  | 33000  | 0.3459          | 0.1477 |
| 0.1023        | 1.29  | 33500  | 0.2412          | 0.1362 |
| 0.0988        | 1.31  | 34000  | 0.2124          | 0.1319 |
| 0.1047        | 1.33  | 34500  | 0.3733          | 0.1497 |
| 0.1078        | 1.35  | 35000  | 0.1553          | 0.1281 |
| 0.0988        | 1.37  | 35500  | 0.1364          | 0.1239 |
| 0.0957        | 1.38  | 36000  | 0.1484          | 0.1278 |
| 0.1038        | 1.4   | 36500  | 0.1723          | 0.1253 |
| 0.1001        | 1.42  | 37000  | 0.3668          | 0.1648 |
| 0.101         | 1.44  | 37500  | 0.2136          | 0.1339 |
| 0.1022        | 1.46  | 38000  | 0.1140          | 0.1162 |
| 0.0989        | 1.48  | 38500  | 0.1628          | 0.1265 |
| 0.0982        | 1.5   | 39000  | 0.2204          | 0.1376 |
| 0.1012        | 1.52  | 39500  | 0.1716          | 0.1297 |
| 0.1067        | 1.54  | 40000  | 0.1362          | 0.1234 |
| 0.1022        | 1.56  | 40500  | 0.1170          | 0.1178 |
| 0.1011        | 1.58  | 41000  | 0.1578          | 0.1240 |
| 0.0845        | 1.6   | 41500  | 0.1659          | 0.1243 |
| 0.0929        | 1.62  | 42000  | 0.1813          | 0.1310 |
| 0.0904        | 1.63  | 42500  | 0.1309          | 0.1215 |
| 0.0885        | 1.65  | 43000  | 0.1964          | 0.1359 |
| 0.0895        | 1.67  | 43500  | 0.1309          | 0.1179 |
| 0.0855        | 1.69  | 44000  | 0.1472          | 0.1258 |
| 0.0876        | 1.71  | 44500  | 0.1189          | 0.1190 |
| 0.0925        | 1.73  | 45000  | 0.1477          | 0.1209 |
| 0.0866        | 1.75  | 45500  | 0.2537          | 0.1428 |
| 0.0938        | 1.77  | 46000  | 0.1406          | 0.1240 |
| 0.0901        | 1.79  | 46500  | 0.1416          | 0.1201 |
| 0.0839        | 1.81  | 47000  | 0.1323          | 0.1201 |
| 0.0866        | 1.83  | 47500  | 0.1176          | 0.1149 |
| 0.0876        | 1.85  | 48000  | 0.1141          | 0.1139 |
| 0.0857        | 1.87  | 48500  | 0.2148          | 0.1297 |
| 0.089         | 1.88  | 49000  | 0.1707          | 0.1231 |
| 0.0861        | 1.9   | 49500  | 0.1457          | 0.1183 |
| 0.0855        | 1.92  | 50000  | 0.4576          | 0.1654 |
| 0.0808        | 1.94  | 50500  | 0.2264          | 0.1285 |
| 0.0859        | 1.96  | 51000  | 0.1630          | 0.1201 |
| 0.0859        | 1.98  | 51500  | 0.1613          | 0.1165 |
| 0.086         | 2.0   | 52000  | 0.1529          | 0.1196 |
| 0.0769        | 2.02  | 52500  | 0.1258          | 0.1139 |
| 0.0783        | 2.04  | 53000  | 0.1105          | 0.1136 |
| 0.0775        | 2.06  | 53500  | 0.1177          | 0.1128 |
| 0.08          | 2.08  | 54000  | 0.1328          | 0.1156 |
| 0.0765        | 2.1   | 54500  | 0.1229          | 0.1137 |
| 0.0791        | 2.12  | 55000  | 0.1218          | 0.1121 |
| 0.0831        | 2.13  | 55500  | 0.1106          | 0.1135 |
| 0.0769        | 2.15  | 56000  | 0.1466          | 0.1166 |
| 0.0761        | 2.17  | 56500  | 0.1177          | 0.1126 |
| 0.0779        | 2.19  | 57000  | 0.1249          | 0.1120 |
| 0.0749        | 2.21  | 57500  | 0.1258          | 0.1130 |
| 0.0746        | 2.23  | 58000  | 0.1268          | 0.1122 |
| 0.074         | 2.25  | 58500  | 0.1141          | 0.1153 |
| 0.0726        | 2.27  | 59000  | 0.1231          | 0.1107 |
| 0.0771        | 2.29  | 59500  | 0.1393          | 0.1125 |
| 0.0776        | 2.31  | 60000  | 0.1224          | 0.1115 |
| 0.0756        | 2.33  | 60500  | 0.1071          | 0.1085 |
| 0.0753        | 2.35  | 61000  | 0.1072          | 0.1089 |
| 0.0698        | 2.37  | 61500  | 0.1129          | 0.1094 |
| 0.0726        | 2.38  | 62000  | 0.1109          | 0.1106 |
| 0.0758        | 2.4   | 62500  | 0.1052          | 0.1103 |
| 0.0743        | 2.42  | 63000  | 0.1079          | 0.1106 |
| 0.0765        | 2.44  | 63500  | 0.1248          | 0.1108 |
| 0.0724        | 2.46  | 64000  | 0.1248          | 0.1076 |
| 0.0659        | 2.48  | 64500  | 0.1099          | 0.1088 |
| 0.0674        | 2.5   | 65000  | 0.1156          | 0.1098 |
| 0.0691        | 2.52  | 65500  | 0.1122          | 0.1093 |
| 0.0677        | 2.54  | 66000  | 0.1228          | 0.1082 |
| 0.0695        | 2.56  | 66500  | 0.1049          | 0.1066 |
| 0.0687        | 2.58  | 67000  | 0.1025          | 0.1062 |
| 0.0682        | 2.6   | 67500  | 0.1080          | 0.1064 |
| 0.0663        | 2.61  | 68000  | 0.1009          | 0.1058 |
| 0.0654        | 2.63  | 68500  | 0.1145          | 0.1071 |
| 0.0641        | 2.65  | 69000  | 0.1178          | 0.1082 |
| 0.0662        | 2.67  | 69500  | 0.1106          | 0.1084 |
| 0.0623        | 2.69  | 70000  | 0.1086          | 0.1057 |
| 0.0692        | 2.71  | 70500  | 0.1048          | 0.1071 |
| 0.0663        | 2.73  | 71000  | 0.1119          | 0.1069 |
| 0.0639        | 2.75  | 71500  | 0.1147          | 0.1062 |
| 0.0597        | 2.77  | 72000  | 0.1121          | 0.1072 |
| 0.0688        | 2.79  | 72500  | 0.1149          | 0.1060 |
| 0.0616        | 2.81  | 73000  | 0.1126          | 0.1069 |
| 0.0633        | 2.83  | 73500  | 0.1302          | 0.1074 |
| 0.0651        | 2.85  | 74000  | 0.1260          | 0.1066 |
| 0.0637        | 2.86  | 74500  | 0.1233          | 0.1075 |
| 0.0641        | 2.88  | 75000  | 0.1199          | 0.1066 |
| 0.0655        | 2.9   | 75500  | 0.1249          | 0.1075 |
| 0.065         | 2.92  | 76000  | 0.1192          | 0.1061 |
| 0.0626        | 2.94  | 76500  | 0.1267          | 0.1069 |
| 0.0622        | 2.96  | 77000  | 0.1289          | 0.1094 |
| 0.0608        | 2.98  | 77500  | 0.1502          | 0.1096 |
| 0.0631        | 3.0   | 78000  | 0.1493          | 0.1099 |
| 0.0535        | 3.02  | 78500  | 0.1220          | 0.1064 |
| 0.0582        | 3.04  | 79000  | 0.1274          | 0.1077 |
| 0.052         | 3.06  | 79500  | 0.1296          | 0.1072 |
| 0.0562        | 3.08  | 80000  | 0.1160          | 0.1050 |
| 0.0533        | 3.1   | 80500  | 0.1066          | 0.1031 |
| 0.0564        | 3.11  | 81000  | 0.1300          | 0.1078 |
| 0.0589        | 3.13  | 81500  | 0.1167          | 0.1056 |
| 0.0582        | 3.15  | 82000  | 0.1129          | 0.1025 |
| 0.0594        | 3.17  | 82500  | 0.1255          | 0.1054 |
| 0.0559        | 3.19  | 83000  | 0.1258          | 0.1045 |
| 0.0535        | 3.21  | 83500  | 0.1150          | 0.1029 |
| 0.0538        | 3.23  | 84000  | 0.1043          | 0.1017 |
| 0.0537        | 3.25  | 84500  | 0.1073          | 0.1028 |
| 0.0534        | 3.27  | 85000  | 0.1011          | 0.1011 |
| 0.0527        | 3.29  | 85500  | 0.0987          | 0.1010 |
| 0.0549        | 3.31  | 86000  | 0.1008          | 0.1015 |
| 0.0516        | 3.33  | 86500  | 0.1031          | 0.1017 |
| 0.0549        | 3.35  | 87000  | 0.1103          | 0.1028 |
| 0.056         | 3.36  | 87500  | 0.0980          | 0.1008 |
| 0.0528        | 3.38  | 88000  | 0.1045          | 0.1020 |
| 0.0555        | 3.4   | 88500  | 0.0979          | 0.1005 |
| 0.0517        | 3.42  | 89000  | 0.0948          | 0.0992 |
| 0.0495        | 3.44  | 89500  | 0.0974          | 0.1002 |
| 0.0496        | 3.46  | 90000  | 0.1035          | 0.1013 |
| 0.0497        | 3.48  | 90500  | 0.1167          | 0.1035 |
| 0.0485        | 3.5   | 91000  | 0.1098          | 0.1009 |
| 0.0465        | 3.52  | 91500  | 0.1168          | 0.1009 |
| 0.05          | 3.54  | 92000  | 0.1088          | 0.1005 |
| 0.0514        | 3.56  | 92500  | 0.1116          | 0.1000 |
| 0.0467        | 3.58  | 93000  | 0.1053          | 0.0998 |
| 0.045         | 3.6   | 93500  | 0.1099          | 0.1012 |
| 0.0507        | 3.61  | 94000  | 0.1186          | 0.1012 |
| 0.0452        | 3.63  | 94500  | 0.1119          | 0.0998 |
| 0.0452        | 3.65  | 95000  | 0.1099          | 0.1002 |
| 0.0452        | 3.67  | 95500  | 0.1228          | 0.1015 |
| 0.0448        | 3.69  | 96000  | 0.1271          | 0.1025 |
| 0.0485        | 3.71  | 96500  | 0.1338          | 0.1037 |
| 0.048         | 3.73  | 97000  | 0.1288          | 0.1030 |
| 0.0476        | 3.75  | 97500  | 0.1183          | 0.1012 |
| 0.0457        | 3.77  | 98000  | 0.1171          | 0.1007 |
| 0.0492        | 3.79  | 98500  | 0.1142          | 0.1004 |
| 0.049         | 3.81  | 99000  | 0.1141          | 0.1006 |
| 0.046         | 3.83  | 99500  | 0.1165          | 0.1007 |
| 0.0444        | 3.85  | 100000 | 0.1173          | 0.1010 |
| 0.0456        | 3.86  | 100500 | 0.1150          | 0.1004 |
| 0.0467        | 3.88  | 101000 | 0.1130          | 0.1003 |
| 0.0465        | 3.9   | 101500 | 0.1137          | 0.1003 |
| 0.0451        | 3.92  | 102000 | 0.1127          | 0.1004 |
| 0.0445        | 3.94  | 102500 | 0.1118          | 0.1003 |
| 0.0453        | 3.96  | 103000 | 0.1112          | 0.1002 |
| 0.0458        | 3.98  | 103500 | 0.1103          | 0.1002 |
| 0.0454        | 4.0   | 104000 | 0.1101          | 0.1002 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 1.13.3
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-libri-train360-colab", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-libri-train360-colab
=============================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0
* Datasets 1.13.3
* Tokenizers 0.10.3



### 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 ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0
* Datasets 1.13.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0
* Datasets 1.13.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0
* Datasets 1.13.3
* Tokenizers 0.10.3