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# urdumodel

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4939
- Wer: 0.3698
- Cer: 0.1465

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used. 

## Training procedure
All the code is available here
https://github.com/talhaanwarch/Urdu-ASR
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

# Model score on test
When I train I got different WER and CER score on test set, but when I tested locally
I got WER of 0.27 without language model and 0.22 with language model.


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer", "cer"], "model-index": [{"name": "urdumodel", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 




# urdumodel

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4939
- Wer: 0.3698
- Cer: 0.1465

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used. 

## Training procedure
All the code is available here
URL
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

# Model score on test
When I train I got different WER and CER score on test set, but when I tested locally
I got WER of 0.27 without language model and 0.22 with language model.


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1

# urdumodel

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4939
- Wer: 0.3698
- Cer: 0.1465 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used. ## Training procedure
All the code is available here
URL ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP # Model score on test
When I train I got different WER and CER score on test set, but when I tested locally
I got WER of 0.27 without language model and 0.22 with language model. ### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # urdumodel

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4939
- Wer: 0.3698
- Cer: 0.1465 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used. ## Training procedure
All the code is available here
URL ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP # Model score on test
When I train I got different WER and CER score on test set, but when I tested locally
I got WER of 0.27 without language model and 0.22 with language model. ### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# urdumodel

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4939
- Wer: 0.3698
- Cer: 0.1465## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used.## Training procedure
All the code is available here
URL### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP# Model score on test
When I train I got different WER and CER score on test set, but when I tested locally
I got WER of 0.27 without language model and 0.22 with language model.### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1