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# 20220517-150219

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2426
- Wer: 0.2344
- Cer: 0.0434

## 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: 8
- seed: 1339
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 5.3867        | 0.02  | 200   | 3.2171          | 1.0    | 1.0    |
| 3.1288        | 0.04  | 400   | 2.9394          | 1.0    | 1.0    |
| 1.8298        | 0.06  | 600   | 0.9138          | 0.8416 | 0.2039 |
| 0.9751        | 0.07  | 800   | 0.6568          | 0.6928 | 0.1566 |
| 0.7934        | 0.09  | 1000  | 0.5314          | 0.6225 | 0.1277 |
| 0.663         | 0.11  | 1200  | 0.4759          | 0.5730 | 0.1174 |
| 0.617         | 0.13  | 1400  | 0.4515          | 0.5578 | 0.1118 |
| 0.5473        | 0.15  | 1600  | 0.4017          | 0.5157 | 0.1004 |
| 0.5283        | 0.17  | 1800  | 0.3872          | 0.5094 | 0.0982 |
| 0.4893        | 0.18  | 2000  | 0.3725          | 0.4860 | 0.0932 |
| 0.495         | 0.2   | 2200  | 0.3580          | 0.4542 | 0.0878 |
| 0.4438        | 0.22  | 2400  | 0.3443          | 0.4366 | 0.0858 |
| 0.4425        | 0.24  | 2600  | 0.3428          | 0.4284 | 0.0865 |
| 0.4293        | 0.26  | 2800  | 0.3329          | 0.4221 | 0.0819 |
| 0.3779        | 0.28  | 3000  | 0.3278          | 0.4146 | 0.0794 |
| 0.4116        | 0.29  | 3200  | 0.3242          | 0.4107 | 0.0757 |
| 0.3912        | 0.31  | 3400  | 0.3217          | 0.4040 | 0.0776 |
| 0.391         | 0.33  | 3600  | 0.3127          | 0.3955 | 0.0764 |
| 0.3696        | 0.35  | 3800  | 0.3153          | 0.3892 | 0.0748 |
| 0.3576        | 0.37  | 4000  | 0.3156          | 0.3846 | 0.0737 |
| 0.3553        | 0.39  | 4200  | 0.3024          | 0.3814 | 0.0726 |
| 0.3394        | 0.4   | 4400  | 0.3022          | 0.3637 | 0.0685 |
| 0.3345        | 0.42  | 4600  | 0.3130          | 0.3641 | 0.0698 |
| 0.3357        | 0.44  | 4800  | 0.2913          | 0.3602 | 0.0701 |
| 0.3411        | 0.46  | 5000  | 0.2941          | 0.3514 | 0.0674 |
| 0.3031        | 0.48  | 5200  | 0.3043          | 0.3613 | 0.0685 |
| 0.3305        | 0.5   | 5400  | 0.2967          | 0.3468 | 0.0657 |
| 0.3004        | 0.51  | 5600  | 0.2723          | 0.3309 | 0.0616 |
| 0.31          | 0.53  | 5800  | 0.2835          | 0.3404 | 0.0648 |
| 0.3224        | 0.55  | 6000  | 0.2743          | 0.3358 | 0.0622 |
| 0.3261        | 0.57  | 6200  | 0.2803          | 0.3358 | 0.0620 |
| 0.305         | 0.59  | 6400  | 0.2835          | 0.3397 | 0.0629 |
| 0.3025        | 0.61  | 6600  | 0.2684          | 0.3340 | 0.0639 |
| 0.2952        | 0.62  | 6800  | 0.2654          | 0.3256 | 0.0617 |
| 0.2903        | 0.64  | 7000  | 0.2588          | 0.3174 | 0.0596 |
| 0.2907        | 0.66  | 7200  | 0.2789          | 0.3256 | 0.0623 |
| 0.2887        | 0.68  | 7400  | 0.2634          | 0.3142 | 0.0605 |
| 0.291         | 0.7   | 7600  | 0.2644          | 0.3097 | 0.0582 |
| 0.2646        | 0.72  | 7800  | 0.2753          | 0.3089 | 0.0582 |
| 0.2683        | 0.73  | 8000  | 0.2703          | 0.3036 | 0.0574 |
| 0.2808        | 0.75  | 8200  | 0.2544          | 0.2994 | 0.0561 |
| 0.2724        | 0.77  | 8400  | 0.2584          | 0.3051 | 0.0592 |
| 0.2516        | 0.79  | 8600  | 0.2575          | 0.2959 | 0.0557 |
| 0.2561        | 0.81  | 8800  | 0.2594          | 0.2945 | 0.0552 |
| 0.264         | 0.83  | 9000  | 0.2607          | 0.2987 | 0.0552 |
| 0.2383        | 0.84  | 9200  | 0.2641          | 0.2983 | 0.0546 |
| 0.2548        | 0.86  | 9400  | 0.2714          | 0.2930 | 0.0538 |
| 0.2284        | 0.88  | 9600  | 0.2542          | 0.2945 | 0.0555 |
| 0.2354        | 0.9   | 9800  | 0.2564          | 0.2937 | 0.0551 |
| 0.2624        | 0.92  | 10000 | 0.2466          | 0.2891 | 0.0542 |
| 0.24          | 0.94  | 10200 | 0.2404          | 0.2895 | 0.0528 |
| 0.2372        | 0.95  | 10400 | 0.2590          | 0.2782 | 0.0518 |
| 0.2357        | 0.97  | 10600 | 0.2629          | 0.2867 | 0.0531 |
| 0.2439        | 0.99  | 10800 | 0.2722          | 0.2902 | 0.0556 |
| 0.2204        | 1.01  | 11000 | 0.2618          | 0.2856 | 0.0535 |
| 0.2043        | 1.03  | 11200 | 0.2662          | 0.2789 | 0.0520 |
| 0.2081        | 1.05  | 11400 | 0.2744          | 0.2831 | 0.0532 |
| 0.199         | 1.06  | 11600 | 0.2586          | 0.2800 | 0.0519 |
| 0.2063        | 1.08  | 11800 | 0.2711          | 0.2842 | 0.0531 |
| 0.2116        | 1.1   | 12000 | 0.2463          | 0.2782 | 0.0529 |
| 0.2095        | 1.12  | 12200 | 0.2371          | 0.2757 | 0.0510 |
| 0.1786        | 1.14  | 12400 | 0.2693          | 0.2768 | 0.0520 |
| 0.1999        | 1.16  | 12600 | 0.2625          | 0.2793 | 0.0513 |
| 0.1985        | 1.17  | 12800 | 0.2734          | 0.2796 | 0.0532 |
| 0.187         | 1.19  | 13000 | 0.2654          | 0.2676 | 0.0514 |
| 0.188         | 1.21  | 13200 | 0.2548          | 0.2648 | 0.0489 |
| 0.1853        | 1.23  | 13400 | 0.2684          | 0.2641 | 0.0509 |
| 0.197         | 1.25  | 13600 | 0.2589          | 0.2662 | 0.0507 |
| 0.1873        | 1.27  | 13800 | 0.2633          | 0.2686 | 0.0516 |
| 0.179         | 1.28  | 14000 | 0.2682          | 0.2598 | 0.0508 |
| 0.2008        | 1.3   | 14200 | 0.2505          | 0.2609 | 0.0493 |
| 0.1802        | 1.32  | 14400 | 0.2470          | 0.2598 | 0.0493 |
| 0.1903        | 1.34  | 14600 | 0.2572          | 0.2672 | 0.0500 |
| 0.1852        | 1.36  | 14800 | 0.2576          | 0.2633 | 0.0491 |
| 0.1933        | 1.38  | 15000 | 0.2649          | 0.2602 | 0.0493 |
| 0.191         | 1.4   | 15200 | 0.2578          | 0.2612 | 0.0484 |
| 0.1863        | 1.41  | 15400 | 0.2572          | 0.2566 | 0.0488 |
| 0.1785        | 1.43  | 15600 | 0.2661          | 0.2520 | 0.0478 |
| 0.1755        | 1.45  | 15800 | 0.2637          | 0.2605 | 0.0485 |
| 0.1677        | 1.47  | 16000 | 0.2481          | 0.2559 | 0.0478 |
| 0.1633        | 1.49  | 16200 | 0.2584          | 0.2531 | 0.0476 |
| 0.166         | 1.51  | 16400 | 0.2576          | 0.2595 | 0.0487 |
| 0.1798        | 1.52  | 16600 | 0.2517          | 0.2570 | 0.0488 |
| 0.1879        | 1.54  | 16800 | 0.2555          | 0.2531 | 0.0479 |
| 0.1636        | 1.56  | 17000 | 0.2419          | 0.2467 | 0.0464 |
| 0.1706        | 1.58  | 17200 | 0.2426          | 0.2457 | 0.0463 |
| 0.1763        | 1.6   | 17400 | 0.2427          | 0.2496 | 0.0467 |
| 0.1687        | 1.62  | 17600 | 0.2507          | 0.2496 | 0.0467 |
| 0.1662        | 1.63  | 17800 | 0.2553          | 0.2474 | 0.0466 |
| 0.1637        | 1.65  | 18000 | 0.2576          | 0.2450 | 0.0461 |
| 0.1744        | 1.67  | 18200 | 0.2394          | 0.2414 | 0.0454 |
| 0.1597        | 1.69  | 18400 | 0.2442          | 0.2443 | 0.0452 |
| 0.1606        | 1.71  | 18600 | 0.2488          | 0.2435 | 0.0453 |
| 0.1558        | 1.73  | 18800 | 0.2563          | 0.2464 | 0.0464 |
| 0.172         | 1.74  | 19000 | 0.2501          | 0.2411 | 0.0452 |
| 0.1594        | 1.76  | 19200 | 0.2481          | 0.2460 | 0.0458 |
| 0.1732        | 1.78  | 19400 | 0.2427          | 0.2414 | 0.0443 |
| 0.1706        | 1.8   | 19600 | 0.2367          | 0.2418 | 0.0446 |
| 0.1724        | 1.82  | 19800 | 0.2376          | 0.2390 | 0.0444 |
| 0.1621        | 1.84  | 20000 | 0.2430          | 0.2382 | 0.0438 |
| 0.1501        | 1.85  | 20200 | 0.2445          | 0.2404 | 0.0438 |
| 0.1526        | 1.87  | 20400 | 0.2472          | 0.2361 | 0.0436 |
| 0.1756        | 1.89  | 20600 | 0.2431          | 0.2400 | 0.0437 |
| 0.1598        | 1.91  | 20800 | 0.2472          | 0.2368 | 0.0439 |
| 0.1554        | 1.93  | 21000 | 0.2431          | 0.2347 | 0.0435 |
| 0.1354        | 1.95  | 21200 | 0.2427          | 0.2354 | 0.0438 |
| 0.1587        | 1.96  | 21400 | 0.2427          | 0.2347 | 0.0435 |
| 0.1541        | 1.98  | 21600 | 0.2426          | 0.2344 | 0.0434 |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "20220517-150219", "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 

20220517-150219
===============


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2426
* Wer: 0.2344
* Cer: 0.0434


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: 8
* seed: 1339
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 1339
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.11.6
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: 8
* seed: 1339
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.11.6
passage: 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: 8
* seed: 1339
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 2### Training results### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.11.6