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# wav2vec2-base-common-voice-persian-colab

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1446
- Wer: 0.6911

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.26  | 300  | 3.0670          | 1.0    |
| 3.3475        | 2.52  | 600  | 2.5530          | 1.0    |
| 3.3475        | 3.78  | 900  | 1.4598          | 0.9555 |
| 2.0348        | 5.04  | 1200 | 1.2189          | 0.8797 |
| 1.0817        | 6.3   | 1500 | 1.1242          | 0.8268 |
| 1.0817        | 7.56  | 1800 | 1.0764          | 0.7957 |
| 0.7973        | 8.82  | 2100 | 1.1023          | 0.7863 |
| 0.7973        | 10.08 | 2400 | 1.0583          | 0.7785 |
| 0.6514        | 11.34 | 2700 | 1.0963          | 0.7512 |
| 0.5878        | 12.61 | 3000 | 1.1200          | 0.7494 |
| 0.5878        | 13.87 | 3300 | 1.0396          | 0.7402 |
| 0.484         | 15.13 | 3600 | 1.1407          | 0.7340 |
| 0.484         | 16.39 | 3900 | 1.1534          | 0.7584 |
| 0.4384        | 17.65 | 4200 | 1.0973          | 0.7236 |
| 0.3966        | 18.91 | 4500 | 1.0623          | 0.7358 |
| 0.3966        | 20.17 | 4800 | 1.1655          | 0.7112 |
| 0.3408        | 21.43 | 5100 | 1.1825          | 0.7084 |
| 0.3408        | 22.69 | 5400 | 1.1436          | 0.7029 |
| 0.3274        | 23.95 | 5700 | 1.1077          | 0.6988 |
| 0.2948        | 25.21 | 6000 | 1.1454          | 0.7066 |
| 0.2948        | 26.47 | 6300 | 1.1411          | 0.6956 |
| 0.2545        | 27.73 | 6600 | 1.0952          | 0.6918 |
| 0.2545        | 28.99 | 6900 | 1.1446          | 0.6911 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-common-voice-persian-colab", "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-base-common-voice-persian-colab
========================================


This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1446
* Wer: 0.6911


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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* 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+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* 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+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* 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+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* 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+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3