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# wav2vec2-large-xls-r-300m-urdu-colab-cv8

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: 1.4651
- Wer: 0.7

## 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: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 20.3271       | 1.27  | 32   | 20.3487         | 1.0    |
| 11.0206       | 2.55  | 64   | 7.7343          | 1.0    |
| 5.8023        | 3.82  | 96   | 5.4188          | 1.0    |
| 4.5872        | 5.12  | 128  | 4.1428          | 1.0    |
| 3.6691        | 6.39  | 160  | 3.4557          | 1.0    |
| 3.3143        | 7.67  | 192  | 3.2663          | 1.0    |
| 3.1689        | 8.94  | 224  | 3.1022          | 0.9982 |
| 3.1472        | 10.24 | 256  | 3.0544          | 0.9993 |
| 3.1091        | 11.51 | 288  | 3.0327          | 0.9978 |
| 3.0437        | 12.78 | 320  | 3.0288          | 1.0    |
| 2.9981        | 14.08 | 352  | 2.8645          | 1.0    |
| 2.5244        | 15.35 | 384  | 2.0238          | 0.9686 |
| 1.4962        | 16.63 | 416  | 1.5885          | 0.9118 |
| 1.0138        | 17.9  | 448  | 1.3656          | 0.8155 |
| 0.7655        | 19.2  | 480  | 1.4592          | 0.8125 |
| 0.6267        | 20.47 | 512  | 1.4170          | 0.7867 |
| 0.5127        | 21.75 | 544  | 1.3200          | 0.7716 |
| 0.4422        | 23.04 | 576  | 1.4082          | 0.7727 |
| 0.3482        | 24.31 | 608  | 1.3932          | 0.7432 |
| 0.3128        | 25.59 | 640  | 1.4059          | 0.7432 |
| 0.2762        | 26.86 | 672  | 1.4689          | 0.7336 |
| 0.2451        | 28.16 | 704  | 1.4318          | 0.7207 |
| 0.2104        | 29.43 | 736  | 1.4304          | 0.7399 |
| 0.1858        | 30.71 | 768  | 1.4586          | 0.7225 |
| 0.1779        | 31.98 | 800  | 1.4948          | 0.7284 |
| 0.1546        | 33.27 | 832  | 1.4960          | 0.7173 |
| 0.1457        | 34.55 | 864  | 1.4949          | 0.7077 |
| 0.1333        | 35.82 | 896  | 1.4656          | 0.7085 |
| 0.1212        | 37.12 | 928  | 1.5061          | 0.7033 |
| 0.1162        | 38.39 | 960  | 1.4653          | 0.7055 |
| 0.1043        | 39.67 | 992  | 1.4651          | 0.7    |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-urdu-colab-cv8", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-urdu-colab-cv8
========================================


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: 1.4651
* Wer: 0.7


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: 40
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* 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: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #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: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #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: 40
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.12.1