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# xlsr-syntesized-turkish-8-hour

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: 0.1542
- Wer: 0.0998

## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.6973        | 0.26  | 100  | 3.0648          | 1.0    |
| 2.1789        | 0.52  | 200  | 0.8048          | 0.8535 |
| 0.4723        | 0.78  | 300  | 0.3334          | 0.4693 |
| 0.3885        | 1.04  | 400  | 0.2105          | 0.2848 |
| 0.2986        | 1.3   | 500  | 0.1913          | 0.2544 |
| 0.2826        | 1.56  | 600  | 0.1811          | 0.2523 |
| 0.2613        | 1.82  | 700  | 0.1681          | 0.2505 |
| 0.2205        | 2.08  | 800  | 0.1530          | 0.2090 |
| 0.185         | 2.34  | 900  | 0.1427          | 0.2027 |
| 0.1947        | 2.6   | 1000 | 0.1471          | 0.2052 |
| 0.2077        | 2.86  | 1100 | 0.1425          | 0.2268 |
| 0.1724        | 3.12  | 1200 | 0.1466          | 0.2120 |
| 0.1674        | 3.39  | 1300 | 0.1420          | 0.1999 |
| 0.1531        | 3.65  | 1400 | 0.1637          | 0.2203 |
| 0.1596        | 3.91  | 1500 | 0.1429          | 0.1844 |
| 0.1322        | 4.17  | 1600 | 0.1228          | 0.1678 |
| 0.1351        | 4.43  | 1700 | 0.1223          | 0.1752 |
| 0.1301        | 4.69  | 1800 | 0.1171          | 0.1737 |
| 0.1264        | 4.95  | 1900 | 0.1237          | 0.1741 |
| 0.1182        | 5.21  | 2000 | 0.1166          | 0.1643 |
| 0.1121        | 5.47  | 2100 | 0.1259          | 0.1626 |
| 0.1152        | 5.73  | 2200 | 0.1222          | 0.1469 |
| 0.1153        | 5.99  | 2300 | 0.1262          | 0.1514 |
| 0.1009        | 6.25  | 2400 | 0.1369          | 0.1668 |
| 0.108         | 6.51  | 2500 | 0.1239          | 0.1605 |
| 0.0893        | 6.77  | 2600 | 0.1309          | 0.1423 |
| 0.1047        | 7.03  | 2700 | 0.1299          | 0.1520 |
| 0.0929        | 7.29  | 2800 | 0.1239          | 0.1582 |
| 0.0874        | 7.55  | 2900 | 0.1252          | 0.1466 |
| 0.0926        | 7.81  | 3000 | 0.1299          | 0.1459 |
| 0.0889        | 8.07  | 3100 | 0.1321          | 0.1353 |
| 0.0832        | 8.33  | 3200 | 0.1269          | 0.1319 |
| 0.0803        | 8.59  | 3300 | 0.1177          | 0.1308 |
| 0.072         | 8.85  | 3400 | 0.1160          | 0.1321 |
| 0.0718        | 9.11  | 3500 | 0.1375          | 0.1343 |
| 0.0727        | 9.38  | 3600 | 0.1504          | 0.1391 |
| 0.0639        | 9.64  | 3700 | 0.1284          | 0.1246 |
| 0.0632        | 9.9   | 3800 | 0.1280          | 0.1212 |
| 0.0633        | 10.16 | 3900 | 0.1278          | 0.1336 |
| 0.0632        | 10.42 | 4000 | 0.1241          | 0.1320 |
| 0.0595        | 10.68 | 4100 | 0.1321          | 0.1327 |
| 0.0625        | 10.94 | 4200 | 0.1318          | 0.1239 |
| 0.0581        | 11.2  | 4300 | 0.1397          | 0.1210 |
| 0.0568        | 11.46 | 4400 | 0.1418          | 0.1248 |
| 0.0576        | 11.72 | 4500 | 0.1242          | 0.1233 |
| 0.0533        | 11.98 | 4600 | 0.1370          | 0.1246 |
| 0.0469        | 12.24 | 4700 | 0.1358          | 0.1204 |
| 0.0512        | 12.5  | 4800 | 0.1386          | 0.1215 |
| 0.0493        | 12.76 | 4900 | 0.1464          | 0.1204 |
| 0.0522        | 13.02 | 5000 | 0.1411          | 0.1254 |
| 0.0512        | 13.28 | 5100 | 0.1478          | 0.1281 |
| 0.0442        | 13.54 | 5200 | 0.1282          | 0.1218 |
| 0.0435        | 13.8  | 5300 | 0.1520          | 0.1216 |
| 0.0404        | 14.06 | 5400 | 0.1428          | 0.1157 |
| 0.0425        | 14.32 | 5500 | 0.1409          | 0.1149 |
| 0.0384        | 14.58 | 5600 | 0.1429          | 0.1127 |
| 0.0366        | 14.84 | 5700 | 0.1398          | 0.1132 |
| 0.0398        | 15.1  | 5800 | 0.1362          | 0.1171 |
| 0.0374        | 15.36 | 5900 | 0.1420          | 0.1110 |
| 0.0342        | 15.62 | 6000 | 0.1403          | 0.1098 |
| 0.038         | 15.89 | 6100 | 0.1400          | 0.1082 |
| 0.0389        | 16.15 | 6200 | 0.1427          | 0.1100 |
| 0.034         | 16.41 | 6300 | 0.1498          | 0.1077 |
| 0.0317        | 16.67 | 6400 | 0.1478          | 0.1045 |
| 0.0321        | 16.93 | 6500 | 0.1463          | 0.1051 |
| 0.0305        | 17.19 | 6600 | 0.1520          | 0.1060 |
| 0.0327        | 17.45 | 6700 | 0.1414          | 0.1040 |
| 0.0302        | 17.71 | 6800 | 0.1539          | 0.1053 |
| 0.0334        | 17.97 | 6900 | 0.1494          | 0.1041 |
| 0.0288        | 18.23 | 7000 | 0.1517          | 0.1028 |
| 0.0274        | 18.49 | 7100 | 0.1522          | 0.1013 |
| 0.026         | 18.75 | 7200 | 0.1573          | 0.1016 |
| 0.03          | 19.01 | 7300 | 0.1531          | 0.1008 |
| 0.0258        | 19.27 | 7400 | 0.1547          | 0.0996 |
| 0.0254        | 19.53 | 7500 | 0.1545          | 0.1004 |
| 0.0242        | 19.79 | 7600 | 0.1542          | 0.0998 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.10.0+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "xlsr-syntesized-turkish-8-hour", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

xlsr-syntesized-turkish-8-hour
==============================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1542
* Wer: 0.0998


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: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.10.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.10.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.10.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.10.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2