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# xlsr-53-ur

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6860
- Wer: 0.3451

## 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: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 12
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0396        | 1.59  | 300  | 3.0179          | 1.0    |
| 0.4976        | 3.17  | 600  | 0.7037          | 0.5447 |
| 0.3062        | 4.76  | 900  | 0.5557          | 0.4036 |
| 0.2287        | 6.35  | 1200 | 0.5620          | 0.3935 |
| 0.2504        | 7.94  | 1500 | 0.5907          | 0.3677 |
| 0.0633        | 9.52  | 1800 | 0.6239          | 0.3773 |
| 0.0456        | 11.11 | 2100 | 0.6748          | 0.3604 |
| 0.0774        | 12.7  | 2400 | 0.6747          | 0.3552 |
| 0.058         | 14.29 | 2700 | 0.6860          | 0.3451 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fleurs"], "metrics": ["wer"], "model-index": [{"name": "xlsr-53-ur", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "fleurs", "type": "fleurs", "config": "ur_pk", "split": "test", "args": "ur_pk"}, "metrics": [{"type": "wer", "value": 0.3450557529714496, "name": "Wer"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:fleurs license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 

xlsr-53-ur
==========


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the fleurs dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6860
* Wer: 0.3451


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: 6
* eval\_batch\_size: 6
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 12
* total\_eval\_batch\_size: 12
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 12
* total\_eval\_batch\_size: 12
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #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: 6
* eval\_batch\_size: 6
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 12
* total\_eval\_batch\_size: 12
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #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: 6
* eval\_batch\_size: 6
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 12
* total\_eval\_batch\_size: 12
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2