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# wav2vec2-large-xls-r-300m-pt-colab

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: 0.3637
- Wer: 0.2982

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.591         | 1.15  | 400   | 0.9128          | 0.6517 |
| 0.5049        | 2.31  | 800   | 0.4596          | 0.4437 |
| 0.2871        | 3.46  | 1200  | 0.3964          | 0.3905 |
| 0.2077        | 4.61  | 1600  | 0.3958          | 0.3744 |
| 0.1695        | 5.76  | 2000  | 0.4040          | 0.3720 |
| 0.1478        | 6.92  | 2400  | 0.3866          | 0.3651 |
| 0.1282        | 8.07  | 2800  | 0.3987          | 0.3674 |
| 0.1134        | 9.22  | 3200  | 0.4128          | 0.3688 |
| 0.1048        | 10.37 | 3600  | 0.3928          | 0.3561 |
| 0.0938        | 11.53 | 4000  | 0.4048          | 0.3619 |
| 0.0848        | 12.68 | 4400  | 0.4229          | 0.3555 |
| 0.0798        | 13.83 | 4800  | 0.3974          | 0.3468 |
| 0.0688        | 14.98 | 5200  | 0.3870          | 0.3503 |
| 0.0658        | 16.14 | 5600  | 0.3875          | 0.3351 |
| 0.061         | 17.29 | 6000  | 0.4133          | 0.3417 |
| 0.0569        | 18.44 | 6400  | 0.3915          | 0.3414 |
| 0.0526        | 19.6  | 6800  | 0.3957          | 0.3231 |
| 0.0468        | 20.75 | 7200  | 0.4110          | 0.3301 |
| 0.0407        | 21.9  | 7600  | 0.3866          | 0.3186 |
| 0.0384        | 23.05 | 8000  | 0.3976          | 0.3193 |
| 0.0363        | 24.21 | 8400  | 0.3910          | 0.3177 |
| 0.0313        | 25.36 | 8800  | 0.3656          | 0.3109 |
| 0.0293        | 26.51 | 9200  | 0.3712          | 0.3092 |
| 0.0277        | 27.66 | 9600  | 0.3613          | 0.3054 |
| 0.0249        | 28.82 | 10000 | 0.3783          | 0.3015 |
| 0.0234        | 29.97 | 10400 | 0.3637          | 0.2982 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pt-colab", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-pt-colab
==================================


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: 0.3637
* Wer: 0.2982


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: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3



### 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: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #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: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #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: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3