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# wav2vec2-base-common-voice-40p-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: 1.1805
- Wer: 0.6024

## 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: 0.00018
- 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: 2000
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9643        | 1.05  | 200  | 3.0107          | 1.0    |
| 2.7552        | 2.11  | 400  | 2.7370          | 0.9997 |
| 1.9144        | 3.16  | 600  | 1.8266          | 0.9703 |
| 1.502         | 4.21  | 800  | 1.3981          | 0.8996 |
| 1.3155        | 5.26  | 1000 | 1.2148          | 0.8507 |
| 0.9471        | 6.32  | 1200 | 1.1698          | 0.7860 |
| 0.8391        | 7.37  | 1400 | 1.1106          | 0.7857 |
| 0.7986        | 8.42  | 1600 | 1.1858          | 0.7769 |
| 0.7692        | 9.47  | 1800 | 1.1227          | 0.7603 |
| 0.7871        | 10.53 | 2000 | 1.0626          | 0.7612 |
| 0.6795        | 11.58 | 2200 | 1.1249          | 0.7209 |
| 0.4842        | 12.63 | 2400 | 1.1626          | 0.7336 |
| 0.492         | 13.68 | 2600 | 1.0995          | 0.7212 |
| 0.5117        | 14.74 | 2800 | 1.1406          | 0.7105 |
| 0.5649        | 15.79 | 3000 | 1.0603          | 0.6819 |
| 0.3232        | 16.84 | 3200 | 1.1781          | 0.7070 |
| 0.4098        | 17.89 | 3400 | 1.1182          | 0.6764 |
| 0.3917        | 18.95 | 3600 | 1.1320          | 0.6750 |
| 0.3712        | 20.0  | 3800 | 1.1920          | 0.6724 |
| 0.3157        | 21.05 | 4000 | 1.1102          | 0.6786 |
| 0.2397        | 22.11 | 4200 | 1.1924          | 0.6519 |
| 0.2751        | 23.16 | 4400 | 1.1497          | 0.6468 |
| 0.2279        | 24.21 | 4600 | 1.2274          | 0.6400 |
| 0.393         | 25.26 | 4800 | 1.1741          | 0.6436 |
| 0.1748        | 26.32 | 5000 | 1.2038          | 0.6327 |
| 0.1727        | 27.37 | 5200 | 1.1639          | 0.6347 |
| 0.255         | 28.42 | 5400 | 1.1948          | 0.6367 |
| 0.2261        | 29.47 | 5600 | 1.1560          | 0.6362 |
| 0.2359        | 30.53 | 5800 | 1.1227          | 0.6269 |
| 0.1668        | 31.58 | 6000 | 1.1861          | 0.6295 |
| 0.1699        | 32.63 | 6200 | 1.2442          | 0.6314 |
| 0.14          | 33.68 | 6400 | 1.1340          | 0.6277 |
| 0.1919        | 34.74 | 6600 | 1.1691          | 0.6139 |
| 0.2527        | 35.79 | 6800 | 1.1511          | 0.6110 |
| 0.1219        | 36.84 | 7000 | 1.2062          | 0.6139 |
| 0.1389        | 37.89 | 7200 | 1.2142          | 0.6072 |
| 0.135         | 38.95 | 7400 | 1.1967          | 0.6040 |
| 0.1563        | 40.0  | 7600 | 1.1805          | 0.6024 |


### 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-40p-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-40p-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: 1.1805
* Wer: 0.6024


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: 0.00018
* 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: 2000
* num\_epochs: 40
* 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: 0.00018
* 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: 2000
* num\_epochs: 40
* 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: 0.00018
* 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: 2000
* num\_epochs: 40
* 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: 0.00018
* 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: 2000
* num\_epochs: 40
* 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