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# wav2vec2-large-xls-r-300m-Arabic-phoneme-undicretics

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: 0.2356
- Per: 0.2966

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 250
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Per    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.4047        | 1.0   | 102  | 2.1371          | 1.0    |
| 2.0311        | 2.0   | 204  | 2.0476          | 1.0    |
| 2.0117        | 2.99  | 306  | 2.0150          | 0.9943 |
| 1.9689        | 4.0   | 409  | 1.9943          | 0.9943 |
| 2.0006        | 5.0   | 511  | 2.0440          | 0.9943 |
| 1.9945        | 6.0   | 613  | 2.0736          | 0.9943 |
| 1.9959        | 6.99  | 715  | 2.0254          | 0.9942 |
| 1.9911        | 8.0   | 818  | 2.0101          | 0.9827 |
| 2.0827        | 9.0   | 920  | 2.0403          | 0.9943 |
| 1.986         | 10.0  | 1022 | 1.9916          | 0.9980 |
| 1.9595        | 10.99 | 1124 | 1.9558          | 0.9828 |
| 1.9381        | 12.0  | 1227 | 1.8902          | 0.9966 |
| 1.7657        | 13.0  | 1329 | 1.6801          | 0.9946 |
| 1.6872        | 14.0  | 1431 | 1.6852          | 0.9943 |
| 1.6566        | 14.99 | 1533 | 1.6286          | 0.9942 |
| 1.6135        | 16.0  | 1636 | 1.6017          | 0.9927 |
| 1.5798        | 17.0  | 1738 | 1.5355          | 0.9852 |
| 1.5259        | 18.0  | 1840 | 1.4729          | 0.9856 |
| 1.4441        | 18.99 | 1942 | 1.3440          | 0.9807 |
| 1.3541        | 20.0  | 2045 | 1.2208          | 0.9755 |
| 1.2349        | 21.0  | 2147 | 1.0838          | 0.9706 |
| 1.1087        | 22.0  | 2249 | 0.8575          | 0.9623 |
| 0.8635        | 22.99 | 2351 | 0.7175          | 0.9015 |
| 0.7591        | 24.0  | 2454 | 0.6055          | 0.8330 |
| 0.6543        | 25.0  | 2556 | 0.4737          | 0.7052 |
| 0.5586        | 26.0  | 2658 | 0.3902          | 0.5189 |
| 0.4762        | 26.99 | 2760 | 0.3190          | 0.3832 |
| 0.4417        | 28.0  | 2863 | 0.2760          | 0.3200 |
| 0.3994        | 29.0  | 2965 | 0.2448          | 0.2687 |
| 0.3836        | 29.93 | 3060 | 0.2356          | 0.2966 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-Arabic-phoneme-undicretics", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-Arabic-phoneme-undicretics
====================================================


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: 0.2356
* Per: 0.2966


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: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 250
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 250
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 250
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 250
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3