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# wav2vec2-libri-train500-colab

This model is a fine-tuned version of [GW12/wav2vec2-libri-train360_2-colab](https://huggingface.co/GW12/wav2vec2-libri-train360_2-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2348
- Wer: 0.1600

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1452        | 0.03  | 500   | 0.2791          | 0.1920 |
| 0.1544        | 0.05  | 1000  | 0.2897          | 0.1980 |
| 0.1654        | 0.08  | 1500  | 0.3320          | 0.2121 |
| 0.1807        | 0.11  | 2000  | 0.3317          | 0.2182 |
| 0.1697        | 0.13  | 2500  | 0.3250          | 0.2124 |
| 0.1761        | 0.16  | 3000  | 0.3158          | 0.2102 |
| 0.1697        | 0.19  | 3500  | 0.3335          | 0.2114 |
| 0.169         | 0.22  | 4000  | 0.3160          | 0.2168 |
| 0.1715        | 0.24  | 4500  | 0.2950          | 0.2105 |
| 0.1666        | 0.27  | 5000  | 0.2936          | 0.2079 |
| 0.1734        | 0.3   | 5500  | 0.3106          | 0.2098 |
| 0.1664        | 0.32  | 6000  | 0.2997          | 0.2048 |
| 0.1688        | 0.35  | 6500  | 0.3352          | 0.2123 |
| 0.1641        | 0.38  | 7000  | 0.3181          | 0.2069 |
| 0.1709        | 0.4   | 7500  | 0.2950          | 0.2049 |
| 0.1601        | 0.43  | 8000  | 0.2976          | 0.2041 |
| 0.1616        | 0.46  | 8500  | 0.3217          | 0.2051 |
| 0.158         | 0.48  | 9000  | 0.3133          | 0.2110 |
| 0.1635        | 0.51  | 9500  | 0.3158          | 0.2081 |
| 0.1555        | 0.54  | 10000 | 0.3234          | 0.2130 |
| 0.166         | 0.56  | 10500 | 0.3494          | 0.2183 |
| 0.1606        | 0.59  | 11000 | 0.2910          | 0.2020 |
| 0.1607        | 0.62  | 11500 | 0.3231          | 0.2103 |
| 0.1629        | 0.65  | 12000 | 0.2999          | 0.2048 |
| 0.1604        | 0.67  | 12500 | 0.3150          | 0.2029 |
| 0.1555        | 0.7   | 13000 | 0.3163          | 0.2062 |
| 0.1577        | 0.73  | 13500 | 0.3695          | 0.2138 |
| 0.1609        | 0.75  | 14000 | 0.3334          | 0.2048 |
| 0.155         | 0.78  | 14500 | 0.2887          | 0.2004 |
| 0.1549        | 0.81  | 15000 | 0.3375          | 0.2069 |
| 0.1537        | 0.83  | 15500 | 0.2979          | 0.2038 |
| 0.1515        | 0.86  | 16000 | 0.2683          | 0.1984 |
| 0.1548        | 0.89  | 16500 | 0.2873          | 0.2016 |
| 0.152         | 0.91  | 17000 | 0.3142          | 0.2010 |
| 0.1501        | 0.94  | 17500 | 0.2751          | 0.1963 |
| 0.1511        | 0.97  | 18000 | 0.2951          | 0.1988 |
| 0.1465        | 1.0   | 18500 | 0.2909          | 0.1969 |
| 0.1333        | 1.02  | 19000 | 0.2910          | 0.1951 |
| 0.1289        | 1.05  | 19500 | 0.3146          | 0.1931 |
| 0.1283        | 1.08  | 20000 | 0.2859          | 0.1916 |
| 0.1306        | 1.1   | 20500 | 0.2870          | 0.1923 |
| 0.131         | 1.13  | 21000 | 0.2864          | 0.1929 |
| 0.1268        | 1.16  | 21500 | 0.2850          | 0.1929 |
| 0.1298        | 1.18  | 22000 | 0.2780          | 0.1908 |
| 0.1253        | 1.21  | 22500 | 0.2775          | 0.1924 |
| 0.1285        | 1.24  | 23000 | 0.3637          | 0.2051 |
| 0.1238        | 1.26  | 23500 | 0.2821          | 0.1914 |
| 0.13          | 1.29  | 24000 | 0.3083          | 0.1954 |
| 0.1333        | 1.32  | 24500 | 0.3033          | 0.1937 |
| 0.1281        | 1.35  | 25000 | 0.2995          | 0.1947 |
| 0.122         | 1.37  | 25500 | 0.3041          | 0.1935 |
| 0.127         | 1.4   | 26000 | 0.3235          | 0.1976 |
| 0.1254        | 1.43  | 26500 | 0.3084          | 0.1917 |
| 0.128         | 1.45  | 27000 | 0.3172          | 0.1952 |
| 0.1212        | 1.48  | 27500 | 0.2766          | 0.1898 |
| 0.1248        | 1.51  | 28000 | 0.2936          | 0.1905 |
| 0.1225        | 1.53  | 28500 | 0.2746          | 0.1872 |
| 0.1245        | 1.56  | 29000 | 0.2790          | 0.1886 |
| 0.1278        | 1.59  | 29500 | 0.2785          | 0.1895 |
| 0.1251        | 1.61  | 30000 | 0.2583          | 0.1867 |
| 0.1186        | 1.64  | 30500 | 0.2899          | 0.1894 |
| 0.1244        | 1.67  | 31000 | 0.2671          | 0.1850 |
| 0.1221        | 1.69  | 31500 | 0.2681          | 0.1877 |
| 0.1202        | 1.72  | 32000 | 0.2763          | 0.1893 |
| 0.1275        | 1.75  | 32500 | 0.2701          | 0.1880 |
| 0.1221        | 1.78  | 33000 | 0.2593          | 0.1855 |
| 0.1214        | 1.8   | 33500 | 0.2548          | 0.1819 |
| 0.1247        | 1.83  | 34000 | 0.2758          | 0.1856 |
| 0.1216        | 1.86  | 34500 | 0.2635          | 0.1878 |
| 0.1185        | 1.88  | 35000 | 0.2645          | 0.1824 |
| 0.1193        | 1.91  | 35500 | 0.2583          | 0.1830 |
| 0.1192        | 1.94  | 36000 | 0.2548          | 0.1828 |
| 0.1168        | 1.96  | 36500 | 0.2534          | 0.1849 |
| 0.1195        | 1.99  | 37000 | 0.2717          | 0.1888 |
| 0.1174        | 2.02  | 37500 | 0.2632          | 0.1843 |
| 0.1005        | 2.04  | 38000 | 0.2620          | 0.1852 |
| 0.1021        | 2.07  | 38500 | 0.2776          | 0.1837 |
| 0.0989        | 2.1   | 39000 | 0.2785          | 0.1830 |
| 0.098         | 2.13  | 39500 | 0.2670          | 0.1822 |
| 0.1018        | 2.15  | 40000 | 0.2623          | 0.1795 |
| 0.1029        | 2.18  | 40500 | 0.2639          | 0.1800 |
| 0.1082        | 2.21  | 41000 | 0.2699          | 0.1807 |
| 0.1101        | 2.23  | 41500 | 0.2800          | 0.1847 |
| 0.1021        | 2.26  | 42000 | 0.2768          | 0.1839 |
| 0.1029        | 2.29  | 42500 | 0.2942          | 0.1844 |
| 0.1027        | 2.31  | 43000 | 0.3192          | 0.1859 |
| 0.1024        | 2.34  | 43500 | 0.3340          | 0.1874 |
| 0.1031        | 2.37  | 44000 | 0.2795          | 0.1830 |
| 0.1012        | 2.39  | 44500 | 0.2775          | 0.1830 |
| 0.1003        | 2.42  | 45000 | 0.2723          | 0.1838 |
| 0.0961        | 2.45  | 45500 | 0.2869          | 0.1878 |
| 0.1038        | 2.47  | 46000 | 0.2918          | 0.1840 |
| 0.0984        | 2.5   | 46500 | 0.2986          | 0.1844 |
| 0.0965        | 2.53  | 47000 | 0.2746          | 0.1826 |
| 0.0963        | 2.56  | 47500 | 0.2885          | 0.1844 |
| 0.097         | 2.58  | 48000 | 0.2803          | 0.1821 |
| 0.0992        | 2.61  | 48500 | 0.2800          | 0.1797 |
| 0.0941        | 2.64  | 49000 | 0.2614          | 0.1789 |
| 0.1064        | 2.66  | 49500 | 0.2454          | 0.1784 |
| 0.0981        | 2.69  | 50000 | 0.2455          | 0.1769 |
| 0.0984        | 2.72  | 50500 | 0.2692          | 0.1799 |
| 0.0982        | 2.74  | 51000 | 0.2425          | 0.1783 |
| 0.0984        | 2.77  | 51500 | 0.2459          | 0.1763 |
| 0.0985        | 2.8   | 52000 | 0.2561          | 0.1771 |
| 0.0984        | 2.82  | 52500 | 0.2516          | 0.1754 |
| 0.098         | 2.85  | 53000 | 0.2452          | 0.1754 |
| 0.0931        | 2.88  | 53500 | 0.2487          | 0.1771 |
| 0.0985        | 2.91  | 54000 | 0.2424          | 0.1746 |
| 0.0923        | 2.93  | 54500 | 0.2479          | 0.1734 |
| 0.0986        | 2.96  | 55000 | 0.2488          | 0.1756 |
| 0.0936        | 2.99  | 55500 | 0.2400          | 0.1745 |
| 0.087         | 3.01  | 56000 | 0.2444          | 0.1735 |
| 0.0817        | 3.04  | 56500 | 0.2434          | 0.1730 |
| 0.0806        | 3.07  | 57000 | 0.2526          | 0.1756 |
| 0.0774        | 3.09  | 57500 | 0.2596          | 0.1742 |
| 0.0827        | 3.12  | 58000 | 0.2394          | 0.1728 |
| 0.0832        | 3.15  | 58500 | 0.2499          | 0.1722 |
| 0.0886        | 3.17  | 59000 | 0.2841          | 0.1780 |
| 0.0817        | 3.2   | 59500 | 0.2702          | 0.1786 |
| 0.0881        | 3.23  | 60000 | 0.2594          | 0.1763 |
| 0.0809        | 3.26  | 60500 | 0.2565          | 0.1747 |
| 0.087         | 3.28  | 61000 | 0.2543          | 0.1730 |
| 0.0861        | 3.31  | 61500 | 0.2639          | 0.1760 |
| 0.085         | 3.34  | 62000 | 0.2565          | 0.1727 |
| 0.078         | 3.36  | 62500 | 0.2595          | 0.1712 |
| 0.081         | 3.39  | 63000 | 0.2508          | 0.1746 |
| 0.0824        | 3.42  | 63500 | 0.2525          | 0.1739 |
| 0.0792        | 3.44  | 64000 | 0.2664          | 0.1755 |
| 0.081         | 3.47  | 64500 | 0.3069          | 0.1901 |
| 0.0822        | 3.5   | 65000 | 0.2479          | 0.1705 |
| 0.0794        | 3.52  | 65500 | 0.2430          | 0.1732 |
| 0.0792        | 3.55  | 66000 | 0.2476          | 0.1718 |
| 0.0809        | 3.58  | 66500 | 0.2540          | 0.1714 |
| 0.0788        | 3.6   | 67000 | 0.2549          | 0.1722 |
| 0.0779        | 3.63  | 67500 | 0.2574          | 0.1724 |
| 0.0764        | 3.66  | 68000 | 0.2524          | 0.1712 |
| 0.0761        | 3.69  | 68500 | 0.2583          | 0.1712 |
| 0.0737        | 3.71  | 69000 | 0.2484          | 0.1675 |
| 0.0821        | 3.74  | 69500 | 0.2408          | 0.1669 |
| 0.0766        | 3.77  | 70000 | 0.2417          | 0.1673 |
| 0.0775        | 3.79  | 70500 | 0.2362          | 0.1676 |
| 0.0749        | 3.82  | 71000 | 0.2393          | 0.1666 |
| 0.0751        | 3.85  | 71500 | 0.2331          | 0.1667 |
| 0.0737        | 3.87  | 72000 | 0.2415          | 0.1659 |
| 0.0753        | 3.9   | 72500 | 0.2395          | 0.1680 |
| 0.0757        | 3.93  | 73000 | 0.2439          | 0.1689 |
| 0.0745        | 3.95  | 73500 | 0.2390          | 0.1668 |
| 0.0748        | 3.98  | 74000 | 0.2344          | 0.1662 |
| 0.072         | 4.01  | 74500 | 0.2522          | 0.1673 |
| 0.0673        | 4.04  | 75000 | 0.2469          | 0.1668 |
| 0.0633        | 4.06  | 75500 | 0.2511          | 0.1661 |
| 0.0625        | 4.09  | 76000 | 0.2428          | 0.1660 |
| 0.063         | 4.12  | 76500 | 0.2447          | 0.1644 |
| 0.0653        | 4.14  | 77000 | 0.2490          | 0.1647 |
| 0.0641        | 4.17  | 77500 | 0.2467          | 0.1656 |
| 0.0658        | 4.2   | 78000 | 0.2436          | 0.1641 |
| 0.0608        | 4.22  | 78500 | 0.2439          | 0.1645 |
| 0.0653        | 4.25  | 79000 | 0.2472          | 0.1653 |
| 0.0625        | 4.28  | 79500 | 0.2496          | 0.1633 |
| 0.0634        | 4.3   | 80000 | 0.2511          | 0.1654 |
| 0.061         | 4.33  | 80500 | 0.2445          | 0.1639 |
| 0.0626        | 4.36  | 81000 | 0.2462          | 0.1648 |
| 0.062         | 4.39  | 81500 | 0.2486          | 0.1649 |
| 0.0625        | 4.41  | 82000 | 0.2473          | 0.1632 |
| 0.0644        | 4.44  | 82500 | 0.2444          | 0.1634 |
| 0.0603        | 4.47  | 83000 | 0.2456          | 0.1634 |
| 0.0613        | 4.49  | 83500 | 0.2434          | 0.1634 |
| 0.0648        | 4.52  | 84000 | 0.2399          | 0.1630 |
| 0.0611        | 4.55  | 84500 | 0.2436          | 0.1627 |
| 0.0625        | 4.57  | 85000 | 0.2408          | 0.1619 |
| 0.0604        | 4.6   | 85500 | 0.2376          | 0.1615 |
| 0.0624        | 4.63  | 86000 | 0.2380          | 0.1618 |
| 0.0626        | 4.65  | 86500 | 0.2359          | 0.1617 |
| 0.0614        | 4.68  | 87000 | 0.2332          | 0.1610 |
| 0.0618        | 4.71  | 87500 | 0.2342          | 0.1612 |
| 0.0654        | 4.73  | 88000 | 0.2341          | 0.1605 |
| 0.0603        | 4.76  | 88500 | 0.2347          | 0.1603 |
| 0.0608        | 4.79  | 89000 | 0.2372          | 0.1602 |
| 0.0608        | 4.82  | 89500 | 0.2357          | 0.1601 |
| 0.0607        | 4.84  | 90000 | 0.2348          | 0.1602 |
| 0.0601        | 4.87  | 90500 | 0.2366          | 0.1603 |
| 0.0605        | 4.9   | 91000 | 0.2356          | 0.1599 |
| 0.0604        | 4.92  | 91500 | 0.2353          | 0.1597 |
| 0.0616        | 4.95  | 92000 | 0.2337          | 0.1598 |
| 0.0572        | 4.98  | 92500 | 0.2348          | 0.1600 |


### 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-train500-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-train500-colab
=============================


This model is a fine-tuned version of GW12/wav2vec2-libri-train360\_2-colab on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2348
* Wer: 0.1600


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: 5
* 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: 5
* 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: 5
* 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: 5
* 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