
<!-- 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-urdu-CV_8_0-and-PRUS_v2

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: 1.3541
- Wer: 0.6532

## 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: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 14.8521       | 0.52  | 32   | 20.0617         | 1.0    |
| 9.2152        | 1.05  | 64   | 7.8943          | 1.0    |
| 4.8598        | 1.57  | 96   | 5.1558          | 1.0    |
| 3.866         | 2.1   | 128  | 3.9680          | 1.0    |
| 3.3517        | 2.62  | 160  | 3.4201          | 1.0    |
| 3.2029        | 3.15  | 192  | 3.2355          | 1.0    |
| 3.1509        | 3.67  | 224  | 3.2337          | 1.0    |
| 3.1399        | 4.2   | 256  | 3.1627          | 1.0    |
| 3.0848        | 4.72  | 288  | 3.0550          | 1.0    |
| 2.9806        | 5.25  | 320  | 2.8343          | 0.9996 |
| 2.3814        | 5.77  | 352  | 2.0685          | 0.9523 |
| 1.2936        | 6.3   | 384  | 1.5907          | 0.8657 |
| 0.8656        | 6.82  | 416  | 1.3810          | 0.8235 |
| 0.7014        | 7.34  | 448  | 1.3838          | 0.7920 |
| 0.6015        | 7.87  | 480  | 1.3479          | 0.8046 |
| 0.5341        | 8.39  | 512  | 1.2613          | 0.7757 |
| 0.5031        | 8.92  | 544  | 1.2818          | 0.7890 |
| 0.4349        | 9.44  | 576  | 1.3171          | 0.7739 |
| 0.4198        | 9.97  | 608  | 1.2420          | 0.7750 |
| 0.3593        | 10.49 | 640  | 1.2991          | 0.7587 |
| 0.3252        | 11.02 | 672  | 1.2653          | 0.7228 |
| 0.2715        | 11.54 | 704  | 1.2488          | 0.7350 |
| 0.2733        | 12.07 | 736  | 1.2639          | 0.7110 |
| 0.2338        | 12.59 | 768  | 1.3733          | 0.7454 |
| 0.2403        | 13.11 | 800  | 1.3908          | 0.7228 |
| 0.2106        | 13.64 | 832  | 1.3384          | 0.7224 |
| 0.2041        | 14.16 | 864  | 1.3770          | 0.7050 |
| 0.1814        | 14.69 | 896  | 1.3526          | 0.6932 |
| 0.1742        | 15.21 | 928  | 1.3486          | 0.6895 |
| 0.1658        | 15.74 | 960  | 1.3210          | 0.6936 |
| 0.1455        | 16.26 | 992  | 1.3292          | 0.6858 |
| 0.1399        | 16.79 | 1024 | 1.3521          | 0.6828 |
| 0.1325        | 17.31 | 1056 | 1.3339          | 0.6876 |
| 0.1256        | 17.84 | 1088 | 1.3389          | 0.6836 |
| 0.1219        | 18.36 | 1120 | 1.3496          | 0.6769 |
| 0.1212        | 18.89 | 1152 | 1.3277          | 0.6776 |
| 0.1097        | 19.41 | 1184 | 1.3594          | 0.6762 |
| 0.1129        | 19.93 | 1216 | 1.3448          | 0.6688 |
| 0.1036        | 20.46 | 1248 | 1.3295          | 0.6710 |
| 0.1035        | 20.98 | 1280 | 1.3243          | 0.6577 |
| 0.094         | 21.51 | 1312 | 1.3832          | 0.6591 |
| 0.0912        | 22.03 | 1344 | 1.3857          | 0.6584 |
| 0.0815        | 22.56 | 1376 | 1.3739          | 0.6547 |
| 0.0864        | 23.08 | 1408 | 1.3649          | 0.6554 |
| 0.0772        | 23.61 | 1440 | 1.3791          | 0.6458 |
| 0.0894        | 24.13 | 1472 | 1.3630          | 0.6488 |
| 0.0776        | 24.66 | 1504 | 1.3541          | 0.6532 |


### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-urdu-CV_8_0-and-PRUS_v2", "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-urdu-CV\_8\_0-and-PRUS\_v2
====================================================


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: 1.3541
* Wer: 0.6532


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: 25
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1



### 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: 25
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
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: 0.0003
* 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: 25
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
passage: 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: 0.0003
* 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: 25
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1