
<!-- 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_2-colab

This model is a fine-tuned version of [GW12/wav2vec2-libri-train360-colab](https://huggingface.co/GW12/wav2vec2-libri-train360-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1024
- Wer: 0.0959

## 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: 8
- eval_batch_size: 8
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.219         | 0.04  | 500   | 0.1976          | 0.1215 |
| 0.0762        | 0.08  | 1000  | 0.2818          | 0.1324 |
| 0.0824        | 0.12  | 1500  | 0.4541          | 0.1602 |
| 0.0807        | 0.15  | 2000  | 0.1556          | 0.1162 |
| 0.0799        | 0.19  | 2500  | 0.1618          | 0.1164 |
| 0.0826        | 0.23  | 3000  | 0.3510          | 0.1379 |
| 0.0809        | 0.27  | 3500  | 0.1486          | 0.1182 |
| 0.0854        | 0.31  | 4000  | 0.1267          | 0.1177 |
| 0.0817        | 0.35  | 4500  | 0.1581          | 0.1218 |
| 0.0835        | 0.38  | 5000  | 0.1670          | 0.1251 |
| 0.0841        | 0.42  | 5500  | 0.1576          | 0.1179 |
| 0.0798        | 0.46  | 6000  | 0.2201          | 0.1300 |
| 0.083         | 0.5   | 6500  | 0.1165          | 0.1179 |
| 0.0878        | 0.54  | 7000  | 0.2640          | 0.1430 |
| 0.0811        | 0.58  | 7500  | 0.1585          | 0.1288 |
| 0.083         | 0.62  | 8000  | 0.3127          | 0.1370 |
| 0.083         | 0.65  | 8500  | 0.4790          | 0.1449 |
| 0.0775        | 0.69  | 9000  | 0.1651          | 0.1163 |
| 0.0787        | 0.73  | 9500  | 1.6426          | 0.2083 |
| 0.0781        | 0.77  | 10000 | 0.2307          | 0.1324 |
| 0.0827        | 0.81  | 10500 | 0.1765          | 0.1318 |
| 0.0816        | 0.85  | 11000 | 0.1679          | 0.1201 |
| 0.0797        | 0.88  | 11500 | 0.2506          | 0.1508 |
| 0.0813        | 0.92  | 12000 | 0.1893          | 0.1239 |
| 0.0758        | 0.96  | 12500 | 0.1266          | 0.1147 |
| 0.091         | 1.0   | 13000 | 0.1606          | 0.1180 |
| 0.0677        | 1.04  | 13500 | 0.1107          | 0.1118 |
| 0.0733        | 1.08  | 14000 | 0.1734          | 0.1565 |
| 0.072         | 1.12  | 14500 | 0.1141          | 0.1126 |
| 0.0731        | 1.15  | 15000 | 0.1125          | 0.1112 |
| 0.0793        | 1.19  | 15500 | 0.1818          | 0.1146 |
| 0.07          | 1.23  | 16000 | 0.2678          | 0.1265 |
| 0.0658        | 1.27  | 16500 | 0.2909          | 0.1203 |
| 0.0678        | 1.31  | 17000 | 0.3241          | 0.1280 |
| 0.0681        | 1.35  | 17500 | 0.3243          | 0.1497 |
| 0.0666        | 1.38  | 18000 | 0.2056          | 0.1150 |
| 0.0667        | 1.42  | 18500 | 0.4678          | 0.1252 |
| 0.0656        | 1.46  | 19000 | 0.1603          | 0.1138 |
| 0.0662        | 1.5   | 19500 | 0.1554          | 0.1115 |
| 0.0669        | 1.54  | 20000 | 0.1215          | 0.1101 |
| 0.0681        | 1.58  | 20500 | 0.1118          | 0.1083 |
| 0.0708        | 1.62  | 21000 | 0.1743          | 0.1146 |
| 0.0673        | 1.65  | 21500 | 0.1509          | 0.1109 |
| 0.0667        | 1.69  | 22000 | 0.3411          | 0.1495 |
| 0.065         | 1.73  | 22500 | 0.1045          | 0.1067 |
| 0.0644        | 1.77  | 23000 | 0.0999          | 0.1075 |
| 0.0643        | 1.81  | 23500 | 0.1019          | 0.1073 |
| 0.0675        | 1.85  | 24000 | 0.1196          | 0.1073 |
| 0.0618        | 1.88  | 24500 | 0.1092          | 0.1086 |
| 0.0626        | 1.92  | 25000 | 0.1256          | 0.1070 |
| 0.0635        | 1.96  | 25500 | 0.1183          | 0.1069 |
| 0.0621        | 2.0   | 26000 | 0.1180          | 0.1091 |
| 0.0548        | 2.04  | 26500 | 0.1199          | 0.1048 |
| 0.0548        | 2.08  | 27000 | 0.1215          | 0.1057 |
| 0.0531        | 2.12  | 27500 | 0.1086          | 0.1036 |
| 0.0548        | 2.15  | 28000 | 0.1103          | 0.1043 |
| 0.054         | 2.19  | 28500 | 0.1078          | 0.1048 |
| 0.0521        | 2.23  | 29000 | 0.1094          | 0.1039 |
| 0.0534        | 2.27  | 29500 | 0.1058          | 0.1037 |
| 0.0539        | 2.31  | 30000 | 0.1035          | 0.1026 |
| 0.0516        | 2.35  | 30500 | 0.1009          | 0.1027 |
| 0.0525        | 2.38  | 31000 | 0.1292          | 0.1056 |
| 0.0501        | 2.42  | 31500 | 0.1124          | 0.1033 |
| 0.052         | 2.46  | 32000 | 0.1020          | 0.1028 |
| 0.0519        | 2.5   | 32500 | 0.1131          | 0.1038 |
| 0.0498        | 2.54  | 33000 | 0.1036          | 0.1031 |
| 0.0525        | 2.58  | 33500 | 0.0994          | 0.1005 |
| 0.0506        | 2.61  | 34000 | 0.1093          | 0.1015 |
| 0.0484        | 2.65  | 34500 | 0.1048          | 0.1005 |
| 0.0493        | 2.69  | 35000 | 0.1192          | 0.1028 |
| 0.048         | 2.73  | 35500 | 0.1208          | 0.1020 |
| 0.0473        | 2.77  | 36000 | 0.1410          | 0.1042 |
| 0.0472        | 2.81  | 36500 | 0.1382          | 0.1052 |
| 0.0467        | 2.85  | 37000 | 0.1118          | 0.1012 |
| 0.0473        | 2.88  | 37500 | 0.1032          | 0.1002 |
| 0.0466        | 2.92  | 38000 | 0.1041          | 0.1004 |
| 0.0455        | 2.96  | 38500 | 0.1056          | 0.1004 |
| 0.0483        | 3.0   | 39000 | 0.1091          | 0.0995 |
| 0.0408        | 3.04  | 39500 | 0.1170          | 0.1012 |
| 0.0395        | 3.08  | 40000 | 0.1106          | 0.0995 |
| 0.0407        | 3.11  | 40500 | 0.1075          | 0.0998 |
| 0.0403        | 3.15  | 41000 | 0.1129          | 0.1000 |
| 0.0397        | 3.19  | 41500 | 0.1062          | 0.0993 |
| 0.0389        | 3.23  | 42000 | 0.1072          | 0.0990 |
| 0.0385        | 3.27  | 42500 | 0.1032          | 0.0985 |
| 0.0389        | 3.31  | 43000 | 0.0989          | 0.0973 |
| 0.0404        | 3.35  | 43500 | 0.1031          | 0.0973 |
| 0.0387        | 3.38  | 44000 | 0.0998          | 0.0974 |
| 0.0391        | 3.42  | 44500 | 0.1000          | 0.0969 |
| 0.0387        | 3.46  | 45000 | 0.0982          | 0.0968 |
| 0.0407        | 3.5   | 45500 | 0.1057          | 0.0979 |
| 0.038         | 3.54  | 46000 | 0.1026          | 0.0974 |
| 0.0399        | 3.58  | 46500 | 0.1020          | 0.0970 |
| 0.0387        | 3.61  | 47000 | 0.1022          | 0.0968 |
| 0.0379        | 3.65  | 47500 | 0.1016          | 0.0961 |
| 0.0369        | 3.69  | 48000 | 0.1012          | 0.0957 |
| 0.0372        | 3.73  | 48500 | 0.0993          | 0.0956 |
| 0.0361        | 3.77  | 49000 | 0.1013          | 0.0951 |
| 0.0366        | 3.81  | 49500 | 0.1020          | 0.0956 |
| 0.0377        | 3.85  | 50000 | 0.1014          | 0.0961 |
| 0.0363        | 3.88  | 50500 | 0.1019          | 0.0962 |
| 0.0368        | 3.92  | 51000 | 0.1033          | 0.0963 |
| 0.0381        | 3.96  | 51500 | 0.1026          | 0.0960 |
| 0.0364        | 4.0   | 52000 | 0.1024          | 0.0959 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-libri-train360_2-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\_2-colab
================================


This model is a fine-tuned version of GW12/wav2vec2-libri-train360-colab on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1024
* Wer: 0.0959


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: 8
* eval\_batch\_size: 8
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.6.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.6.1
* Tokenizers 0.11.0
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: 8
* eval\_batch\_size: 8
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.6.1
* Tokenizers 0.11.0
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: 8
* eval\_batch\_size: 8
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.6.1
* Tokenizers 0.11.0