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# wav2vec2-xls-r-300m-en-ar-fr-es

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8565
- Wer: 0.4869

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.3938        | 0.59  | 400   | 3.3703          | 1.0    |
| 2.353         | 1.18  | 800   | 0.9696          | 0.7809 |
| 0.9859        | 1.77  | 1200  | 0.7031          | 0.6515 |
| 0.7685        | 2.35  | 1600  | 0.6575          | 0.6321 |
| 0.6892        | 2.94  | 2000  | 0.6030          | 0.5927 |
| 0.5866        | 3.53  | 2400  | 0.5552          | 0.5541 |
| 0.5496        | 4.12  | 2800  | 0.5805          | 0.5503 |
| 0.4897        | 4.71  | 3200  | 0.5526          | 0.5335 |
| 0.4671        | 5.3   | 3600  | 0.5622          | 0.5507 |
| 0.4346        | 5.89  | 4000  | 0.5641          | 0.5312 |
| 0.3859        | 6.48  | 4400  | 0.5685          | 0.5071 |
| 0.3728        | 7.06  | 4800  | 0.6106          | 0.5157 |
| 0.3243        | 7.65  | 5200  | 0.6782          | 0.5270 |
| 0.3073        | 8.24  | 5600  | 0.6121          | 0.5232 |
| 0.2748        | 8.83  | 6000  | 0.6318          | 0.5209 |
| 0.25          | 9.42  | 6400  | 0.6334          | 0.4906 |
| 0.2477        | 10.01 | 6800  | 0.6403          | 0.5169 |
| 0.2125        | 10.6  | 7200  | 0.6498          | 0.5080 |
| 0.1997        | 11.18 | 7600  | 0.7029          | 0.5153 |
| 0.1803        | 11.77 | 8000  | 0.6796          | 0.5193 |
| 0.1644        | 12.36 | 8400  | 0.7320          | 0.5080 |
| 0.1609        | 12.95 | 8800  | 0.6705          | 0.5081 |
| 0.1419        | 13.54 | 9200  | 0.7108          | 0.5120 |
| 0.1375        | 14.13 | 9600  | 0.7570          | 0.4909 |
| 0.1265        | 14.72 | 10000 | 0.7681          | 0.5044 |
| 0.1152        | 15.31 | 10400 | 0.8180          | 0.5011 |
| 0.1094        | 15.89 | 10800 | 0.7753          | 0.4947 |
| 0.0998        | 16.48 | 11200 | 0.8077          | 0.4972 |
| 0.1019        | 17.07 | 11600 | 0.8189          | 0.4921 |
| 0.0882        | 17.66 | 12000 | 0.8351          | 0.4922 |
| 0.0855        | 18.25 | 12400 | 0.8688          | 0.4902 |
| 0.0826        | 18.84 | 12800 | 0.8476          | 0.4916 |
| 0.0769        | 19.43 | 13200 | 0.8565          | 0.4869 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xls-r-300m-en-ar-fr-es", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice", "type": "common_voice", "config": "ar", "split": "test", "args": "ar"}, "metrics": [{"type": "wer", "value": 0.48692477711277227, "name": "Wer"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-xls-r-300m-en-ar-fr-es
===============================


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


* Loss: 0.8565
* Wer: 0.4869


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: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3