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# xls-r-es-test

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ES dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1304
- WER: 0.1261
- CER: 0.035

## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.9613        | 0.07  | 500   | 2.9647          | 1.0    |
| 2.604         | 0.14  | 1000  | 1.8300          | 0.9562 |
| 1.177         | 0.21  | 1500  | 0.3652          | 0.3077 |
| 1.0745        | 0.28  | 2000  | 0.2707          | 0.2504 |
| 1.0103        | 0.35  | 2500  | 0.2338          | 0.2157 |
| 0.9858        | 0.42  | 3000  | 0.2321          | 0.2129 |
| 0.974         | 0.49  | 3500  | 0.2164          | 0.2031 |
| 0.9699        | 0.56  | 4000  | 0.2078          | 0.1970 |
| 0.9513        | 0.63  | 4500  | 0.2173          | 0.2139 |
| 0.9657        | 0.7   | 5000  | 0.2050          | 0.1979 |
| 0.9484        | 0.77  | 5500  | 0.2008          | 0.1919 |
| 0.9317        | 0.84  | 6000  | 0.2012          | 0.1911 |
| 0.9366        | 0.91  | 6500  | 0.2024          | 0.1976 |
| 0.9242        | 0.98  | 7000  | 0.2062          | 0.2028 |
| 0.9138        | 1.05  | 7500  | 0.1924          | 0.1863 |
| 0.921         | 1.12  | 8000  | 0.1935          | 0.1836 |
| 0.9117        | 1.19  | 8500  | 0.1887          | 0.1815 |
| 0.9064        | 1.26  | 9000  | 0.1909          | 0.1839 |
| 0.9118        | 1.32  | 9500  | 0.1869          | 0.1830 |
| 0.9121        | 1.39  | 10000 | 0.1863          | 0.1802 |
| 0.9048        | 1.46  | 10500 | 0.1845          | 0.1791 |
| 0.8955        | 1.53  | 11000 | 0.1863          | 0.1774 |
| 0.8947        | 1.6   | 11500 | 0.1907          | 0.1814 |
| 0.9073        | 1.67  | 12000 | 0.1892          | 0.1853 |
| 0.8927        | 1.74  | 12500 | 0.1821          | 0.1750 |
| 0.8732        | 1.81  | 13000 | 0.1815          | 0.1768 |
| 0.8761        | 1.88  | 13500 | 0.1822          | 0.1749 |
| 0.8751        | 1.95  | 14000 | 0.1789          | 0.1715 |
| 0.8889        | 2.02  | 14500 | 0.1819          | 0.1791 |
| 0.8864        | 2.09  | 15000 | 0.1826          | 0.1794 |
| 0.886         | 2.16  | 15500 | 0.1788          | 0.1776 |
| 0.8915        | 2.23  | 16000 | 0.1756          | 0.1719 |
| 0.8689        | 2.3   | 16500 | 0.1769          | 0.1711 |
| 0.879         | 2.37  | 17000 | 0.1777          | 0.1739 |
| 0.8692        | 2.44  | 17500 | 0.1765          | 0.1705 |
| 0.8504        | 2.51  | 18000 | 0.1699          | 0.1652 |
| 0.8728        | 2.58  | 18500 | 0.1705          | 0.1694 |
| 0.8523        | 2.65  | 19000 | 0.1674          | 0.1645 |
| 0.8513        | 2.72  | 19500 | 0.1661          | 0.1611 |
| 0.8498        | 2.79  | 20000 | 0.1660          | 0.1631 |
| 0.8432        | 2.86  | 20500 | 0.1636          | 0.1610 |
| 0.8492        | 2.93  | 21000 | 0.1708          | 0.1688 |
| 0.8561        | 3.0   | 21500 | 0.1663          | 0.1604 |
| 0.842         | 3.07  | 22000 | 0.1690          | 0.1625 |
| 0.857         | 3.14  | 22500 | 0.1642          | 0.1605 |
| 0.8518        | 3.21  | 23000 | 0.1626          | 0.1585 |
| 0.8506        | 3.28  | 23500 | 0.1651          | 0.1605 |
| 0.8394        | 3.35  | 24000 | 0.1647          | 0.1585 |
| 0.8431        | 3.42  | 24500 | 0.1632          | 0.1573 |
| 0.8566        | 3.49  | 25000 | 0.1614          | 0.1550 |
| 0.8534        | 3.56  | 25500 | 0.1645          | 0.1589 |
| 0.8386        | 3.63  | 26000 | 0.1632          | 0.1582 |
| 0.8357        | 3.7   | 26500 | 0.1631          | 0.1556 |
| 0.8299        | 3.77  | 27000 | 0.1612          | 0.1550 |
| 0.8421        | 3.84  | 27500 | 0.1602          | 0.1552 |
| 0.8375        | 3.91  | 28000 | 0.1592          | 0.1537 |
| 0.8328        | 3.97  | 28500 | 0.1587          | 0.1537 |
| 0.8155        | 4.04  | 29000 | 0.1587          | 0.1520 |
| 0.8335        | 4.11  | 29500 | 0.1624          | 0.1556 |
| 0.8138        | 4.18  | 30000 | 0.1581          | 0.1547 |
| 0.8195        | 4.25  | 30500 | 0.1560          | 0.1507 |
| 0.8092        | 4.32  | 31000 | 0.1561          | 0.1534 |
| 0.8191        | 4.39  | 31500 | 0.1549          | 0.1493 |
| 0.8008        | 4.46  | 32000 | 0.1540          | 0.1493 |
| 0.8138        | 4.53  | 32500 | 0.1544          | 0.1493 |
| 0.8173        | 4.6   | 33000 | 0.1553          | 0.1511 |
| 0.8081        | 4.67  | 33500 | 0.1541          | 0.1484 |
| 0.8192        | 4.74  | 34000 | 0.1560          | 0.1506 |
| 0.8068        | 4.81  | 34500 | 0.1540          | 0.1503 |
| 0.8105        | 4.88  | 35000 | 0.1529          | 0.1483 |
| 0.7976        | 4.95  | 35500 | 0.1507          | 0.1451 |
| 0.8143        | 5.02  | 36000 | 0.1505          | 0.1462 |
| 0.8053        | 5.09  | 36500 | 0.1517          | 0.1476 |
| 0.785         | 5.16  | 37000 | 0.1526          | 0.1478 |
| 0.7936        | 5.23  | 37500 | 0.1489          | 0.1421 |
| 0.807         | 5.3   | 38000 | 0.1483          | 0.1420 |
| 0.8092        | 5.37  | 38500 | 0.1481          | 0.1435 |
| 0.793         | 5.44  | 39000 | 0.1503          | 0.1438 |
| 0.814         | 5.51  | 39500 | 0.1495          | 0.1480 |
| 0.807         | 5.58  | 40000 | 0.1472          | 0.1424 |
| 0.7913        | 5.65  | 40500 | 0.1471          | 0.1422 |
| 0.7844        | 5.72  | 41000 | 0.1473          | 0.1422 |
| 0.7888        | 5.79  | 41500 | 0.1445          | 0.1385 |
| 0.7806        | 5.86  | 42000 | 0.1435          | 0.1394 |
| 0.7773        | 5.93  | 42500 | 0.1461          | 0.1424 |
| 0.786         | 6.0   | 43000 | 0.1450          | 0.1413 |
| 0.7784        | 6.07  | 43500 | 0.1463          | 0.1424 |
| 0.7937        | 6.14  | 44000 | 0.1438          | 0.1386 |
| 0.7738        | 6.21  | 44500 | 0.1437          | 0.1383 |
| 0.7728        | 6.28  | 45000 | 0.1424          | 0.1371 |
| 0.7681        | 6.35  | 45500 | 0.1416          | 0.1376 |
| 0.776         | 6.42  | 46000 | 0.1415          | 0.1380 |
| 0.7773        | 6.49  | 46500 | 0.1416          | 0.1371 |
| 0.7692        | 6.56  | 47000 | 0.1398          | 0.1345 |
| 0.7642        | 6.62  | 47500 | 0.1381          | 0.1341 |
| 0.7692        | 6.69  | 48000 | 0.1392          | 0.1334 |
| 0.7667        | 6.76  | 48500 | 0.1392          | 0.1348 |
| 0.7712        | 6.83  | 49000 | 0.1398          | 0.1333 |
| 0.7628        | 6.9   | 49500 | 0.1392          | 0.1344 |
| 0.7622        | 6.97  | 50000 | 0.1377          | 0.1329 |
| 0.7639        | 7.04  | 50500 | 0.1361          | 0.1316 |
| 0.742         | 7.11  | 51000 | 0.1376          | 0.1327 |
| 0.7526        | 7.18  | 51500 | 0.1387          | 0.1342 |
| 0.7606        | 7.25  | 52000 | 0.1363          | 0.1316 |
| 0.7626        | 7.32  | 52500 | 0.1365          | 0.1313 |
| 0.752         | 7.39  | 53000 | 0.1354          | 0.1309 |
| 0.7562        | 7.46  | 53500 | 0.1362          | 0.1312 |
| 0.7557        | 7.53  | 54000 | 0.1358          | 0.1325 |
| 0.7588        | 7.6   | 54500 | 0.1343          | 0.1311 |
| 0.7485        | 7.67  | 55000 | 0.1346          | 0.1301 |
| 0.7466        | 7.74  | 55500 | 0.1354          | 0.1314 |
| 0.7558        | 7.81  | 56000 | 0.1359          | 0.1325 |
| 0.7578        | 7.88  | 56500 | 0.1363          | 0.1334 |
| 0.7411        | 7.95  | 57000 | 0.1346          | 0.1301 |
| 0.7478        | 8.02  | 57500 | 0.1355          | 0.1305 |
| 0.7451        | 8.09  | 58000 | 0.1349          | 0.1302 |
| 0.7383        | 8.16  | 58500 | 0.1349          | 0.1294 |
| 0.7482        | 8.23  | 59000 | 0.1341          | 0.1293 |
| 0.742         | 8.3   | 59500 | 0.1338          | 0.1296 |
| 0.7343        | 8.37  | 60000 | 0.1348          | 0.1307 |
| 0.7385        | 8.44  | 60500 | 0.1324          | 0.1282 |
| 0.7567        | 8.51  | 61000 | 0.1334          | 0.1281 |
| 0.7342        | 8.58  | 61500 | 0.1338          | 0.1289 |
| 0.7401        | 8.65  | 62000 | 0.1331          | 0.1285 |
| 0.7362        | 8.72  | 62500 | 0.1329          | 0.1283 |
| 0.7241        | 8.79  | 63000 | 0.1323          | 0.1277 |
| 0.7244        | 8.86  | 63500 | 0.1317          | 0.1269 |
| 0.7274        | 8.93  | 64000 | 0.1308          | 0.1260 |
| 0.7411        | 9.0   | 64500 | 0.1309          | 0.1256 |
| 0.7255        | 9.07  | 65000 | 0.1316          | 0.1265 |
| 0.7406        | 9.14  | 65500 | 0.1315          | 0.1270 |
| 0.7418        | 9.21  | 66000 | 0.1315          | 0.1269 |
| 0.7301        | 9.27  | 66500 | 0.1315          | 0.1273 |
| 0.7248        | 9.34  | 67000 | 0.1323          | 0.1274 |
| 0.7423        | 9.41  | 67500 | 0.1309          | 0.1267 |
| 0.7152        | 9.48  | 68000 | 0.1312          | 0.1271 |
| 0.7295        | 9.55  | 68500 | 0.1306          | 0.1262 |
| 0.7231        | 9.62  | 69000 | 0.1308          | 0.1263 |
| 0.7344        | 9.69  | 69500 | 0.1313          | 0.1267 |
| 0.7264        | 9.76  | 70000 | 0.1305          | 0.1263 |
| 0.7309        | 9.83  | 70500 | 0.1303          | 0.1262 |
| 0.73          | 9.9   | 71000 | 0.1303          | 0.1261 |
| 0.7353        | 9.97  | 71500 | 0.1304          | 0.1260 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0

{"language": ["es"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "es", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-es-test", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8.0", "type": "mozilla-foundation/common_voice_8_0", "args": "es"}, "metrics": [{"type": "wer", "value": 12.62, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "es"}, "metrics": [{"type": "wer", "value": 36.08, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "es"}, "metrics": [{"type": "wer", "value": 39.19, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition mozilla-foundation/common_voice_8_0 generated_from_trainer es robust-speech-event hf-asr-leaderboard dataset:mozilla-foundation/common_voice_8_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #es #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

xls-r-es-test
=============


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ES dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1304
* WER: 0.1261
* CER: 0.035


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: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #es #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #es #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0