
<!-- 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-large-xls-r-300m-spanish-small

This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3763
- Wer: 0.1791

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2277        | 0.26  | 400   | 0.2601          | 0.2291 |
| 0.2932        | 0.53  | 800   | 0.2950          | 0.2670 |
| 0.3019        | 0.79  | 1200  | 0.3247          | 0.2766 |
| 0.2987        | 1.05  | 1600  | 0.3031          | 0.2606 |
| 0.261         | 1.32  | 2000  | 0.2994          | 0.2620 |
| 0.2651        | 1.58  | 2400  | 0.3134          | 0.2700 |
| 0.264         | 1.85  | 2800  | 0.3016          | 0.2641 |
| 0.2475        | 2.11  | 3200  | 0.3135          | 0.2661 |
| 0.2269        | 2.37  | 3600  | 0.3029          | 0.2562 |
| 0.2389        | 2.64  | 4000  | 0.3035          | 0.2549 |
| 0.2319        | 2.9   | 4400  | 0.3022          | 0.2551 |
| 0.2123        | 3.16  | 4800  | 0.3256          | 0.2638 |
| 0.2094        | 3.43  | 5200  | 0.3227          | 0.2712 |
| 0.2121        | 3.69  | 5600  | 0.3085          | 0.2596 |
| 0.207         | 3.96  | 6000  | 0.3041          | 0.2597 |
| 0.1809        | 4.22  | 6400  | 0.3122          | 0.2524 |
| 0.1846        | 4.48  | 6800  | 0.3254          | 0.2579 |
| 0.1885        | 4.75  | 7200  | 0.2958          | 0.2437 |
| 0.1923        | 5.01  | 7600  | 0.3136          | 0.2502 |
| 0.1626        | 5.27  | 8000  | 0.3059          | 0.2488 |
| 0.1704        | 5.54  | 8400  | 0.3082          | 0.2515 |
| 0.1674        | 5.8   | 8800  | 0.3196          | 0.2509 |
| 0.1691        | 6.06  | 9200  | 0.3193          | 0.25   |
| 0.1499        | 6.33  | 9600  | 0.3529          | 0.2635 |
| 0.1568        | 6.59  | 10000 | 0.3241          | 0.2481 |
| 0.1538        | 6.86  | 10400 | 0.3354          | 0.2476 |
| 0.1503        | 7.12  | 10800 | 0.3180          | 0.2402 |
| 0.136         | 7.38  | 11200 | 0.3230          | 0.2397 |
| 0.1413        | 7.65  | 11600 | 0.3178          | 0.2451 |
| 0.147         | 7.91  | 12000 | 0.3170          | 0.2389 |
| 0.1341        | 8.17  | 12400 | 0.3380          | 0.2501 |
| 0.1329        | 8.44  | 12800 | 0.3265          | 0.2414 |
| 0.1314        | 8.7   | 13200 | 0.3281          | 0.2482 |
| 0.1312        | 8.97  | 13600 | 0.3259          | 0.2539 |
| 0.12          | 9.23  | 14000 | 0.3291          | 0.2424 |
| 0.1193        | 9.49  | 14400 | 0.3302          | 0.2412 |
| 0.1189        | 9.76  | 14800 | 0.3376          | 0.2407 |
| 0.1217        | 10.02 | 15200 | 0.3334          | 0.2400 |
| 0.1118        | 10.28 | 15600 | 0.3359          | 0.2368 |
| 0.1139        | 10.55 | 16000 | 0.3239          | 0.2335 |
| 0.1106        | 10.81 | 16400 | 0.3374          | 0.2352 |
| 0.1081        | 11.07 | 16800 | 0.3585          | 0.2434 |
| 0.1063        | 11.34 | 17200 | 0.3639          | 0.2472 |
| 0.1041        | 11.6  | 17600 | 0.3399          | 0.2423 |
| 0.1062        | 11.87 | 18000 | 0.3410          | 0.2388 |
| 0.1012        | 12.13 | 18400 | 0.3597          | 0.2413 |
| 0.0953        | 12.39 | 18800 | 0.3440          | 0.2296 |
| 0.097         | 12.66 | 19200 | 0.3440          | 0.2269 |
| 0.0968        | 12.92 | 19600 | 0.3498          | 0.2333 |
| 0.0902        | 13.18 | 20000 | 0.3471          | 0.2290 |
| 0.0868        | 13.45 | 20400 | 0.3462          | 0.2266 |
| 0.0892        | 13.71 | 20800 | 0.3373          | 0.2227 |
| 0.0902        | 13.97 | 21200 | 0.3377          | 0.2240 |
| 0.0846        | 14.24 | 21600 | 0.3484          | 0.2237 |
| 0.0839        | 14.5  | 22000 | 0.3706          | 0.2260 |
| 0.0834        | 14.77 | 22400 | 0.3430          | 0.2268 |
| 0.0841        | 15.03 | 22800 | 0.3489          | 0.2259 |
| 0.076         | 15.29 | 23200 | 0.3626          | 0.2281 |
| 0.0771        | 15.56 | 23600 | 0.3624          | 0.2268 |
| 0.0773        | 15.82 | 24000 | 0.3440          | 0.2252 |
| 0.0759        | 16.08 | 24400 | 0.3532          | 0.2170 |
| 0.0745        | 16.35 | 24800 | 0.3686          | 0.2188 |
| 0.0713        | 16.61 | 25200 | 0.3691          | 0.2195 |
| 0.0718        | 16.88 | 25600 | 0.3470          | 0.2108 |
| 0.0685        | 17.14 | 26000 | 0.3756          | 0.2179 |
| 0.0689        | 17.4  | 26400 | 0.3542          | 0.2149 |
| 0.0671        | 17.67 | 26800 | 0.3461          | 0.2165 |
| 0.0737        | 17.93 | 27200 | 0.3473          | 0.2238 |
| 0.0669        | 18.19 | 27600 | 0.3441          | 0.2138 |
| 0.0629        | 18.46 | 28000 | 0.3721          | 0.2155 |
| 0.0632        | 18.72 | 28400 | 0.3667          | 0.2126 |
| 0.0647        | 18.98 | 28800 | 0.3579          | 0.2097 |
| 0.0603        | 19.25 | 29200 | 0.3670          | 0.2130 |
| 0.0604        | 19.51 | 29600 | 0.3750          | 0.2142 |
| 0.0619        | 19.78 | 30000 | 0.3804          | 0.2160 |
| 0.0603        | 20.04 | 30400 | 0.3764          | 0.2124 |
| 0.0577        | 20.3  | 30800 | 0.3858          | 0.2097 |
| 0.0583        | 20.57 | 31200 | 0.3520          | 0.2089 |
| 0.0561        | 20.83 | 31600 | 0.3615          | 0.2079 |
| 0.0545        | 21.09 | 32000 | 0.3824          | 0.2032 |
| 0.0525        | 21.36 | 32400 | 0.3858          | 0.2091 |
| 0.0524        | 21.62 | 32800 | 0.3956          | 0.2099 |
| 0.0527        | 21.89 | 33200 | 0.3667          | 0.2025 |
| 0.0514        | 22.15 | 33600 | 0.3708          | 0.2032 |
| 0.0506        | 22.41 | 34000 | 0.3815          | 0.2053 |
| 0.0478        | 22.68 | 34400 | 0.3671          | 0.2007 |
| 0.049         | 22.94 | 34800 | 0.3758          | 0.2003 |
| 0.0477        | 23.2  | 35200 | 0.3786          | 0.2014 |
| 0.045         | 23.47 | 35600 | 0.3732          | 0.1998 |
| 0.0426        | 23.73 | 36000 | 0.3737          | 0.2010 |
| 0.0444        | 23.99 | 36400 | 0.3600          | 0.1990 |
| 0.0433        | 24.26 | 36800 | 0.3689          | 0.1976 |
| 0.0442        | 24.52 | 37200 | 0.3787          | 0.1968 |
| 0.0419        | 24.79 | 37600 | 0.3652          | 0.1961 |
| 0.042         | 25.05 | 38000 | 0.3820          | 0.1964 |
| 0.0419        | 25.31 | 38400 | 0.3786          | 0.1919 |
| 0.0376        | 25.58 | 38800 | 0.3842          | 0.1934 |
| 0.0385        | 25.84 | 39200 | 0.3767          | 0.1900 |
| 0.0396        | 26.1  | 39600 | 0.3688          | 0.1888 |
| 0.0371        | 26.37 | 40000 | 0.3815          | 0.1894 |
| 0.0363        | 26.63 | 40400 | 0.3748          | 0.1878 |
| 0.0377        | 26.9  | 40800 | 0.3713          | 0.1852 |
| 0.0352        | 27.16 | 41200 | 0.3734          | 0.1851 |
| 0.0355        | 27.42 | 41600 | 0.3776          | 0.1874 |
| 0.0333        | 27.69 | 42000 | 0.3867          | 0.1841 |
| 0.0348        | 27.95 | 42400 | 0.3823          | 0.1839 |
| 0.0329        | 28.21 | 42800 | 0.3795          | 0.1822 |
| 0.0325        | 28.48 | 43200 | 0.3711          | 0.1813 |
| 0.0328        | 28.74 | 43600 | 0.3721          | 0.1781 |
| 0.0312        | 29.0  | 44000 | 0.3803          | 0.1816 |
| 0.0318        | 29.27 | 44400 | 0.3758          | 0.1794 |
| 0.0302        | 29.53 | 44800 | 0.3792          | 0.1784 |
| 0.0339        | 29.8  | 45200 | 0.3763          | 0.1791 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-small", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-spanish-small
=======================================


This model is a fine-tuned version of jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom on the common\_voice dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3763
* Wer: 0.1791


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: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0