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# dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_50_try2

This model is a fine-tuned version of [rohitp1/dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20](https://huggingface.co/rohitp1/dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 41.1845
- Wer: 0.3475

## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 325.2839      | 2.2   | 150  | 27.1160         | 0.3780 |
| 316.7718      | 4.41  | 300  | 27.4142         | 0.3772 |
| 299.5312      | 6.61  | 450  | 28.4325         | 0.3764 |
| 286.0595      | 8.82  | 600  | 28.6401         | 0.3746 |
| 274.714       | 11.03 | 750  | 28.8407         | 0.3752 |
| 257.4291      | 13.23 | 900  | 30.4966         | 0.3708 |
| 246.298       | 15.44 | 1050 | 32.1811         | 0.3665 |
| 231.0542      | 17.64 | 1200 | 32.0769         | 0.3721 |
| 217.6621      | 19.85 | 1350 | 33.4042         | 0.3621 |
| 209.504       | 22.06 | 1500 | 31.9015         | 0.3636 |
| 198.3204      | 24.26 | 1650 | 34.1675         | 0.3606 |
| 188.921       | 26.47 | 1800 | 35.2466         | 0.3625 |
| 181.8529      | 28.67 | 1950 | 34.7835         | 0.3574 |
| 174.2548      | 30.88 | 2100 | 36.6986         | 0.3605 |
| 166.8866      | 33.09 | 2250 | 38.0683         | 0.3584 |
| 160.1716      | 35.29 | 2400 | 35.6336         | 0.3552 |
| 154.5262      | 37.5  | 2550 | 39.7696         | 0.3541 |
| 150.0273      | 39.7  | 2700 | 39.7674         | 0.3542 |
| 149.0675      | 41.91 | 2850 | 41.1694         | 0.3532 |
| 142.0086      | 44.12 | 3000 | 41.1995         | 0.3593 |
| 139.7779      | 46.32 | 3150 | 40.9558         | 0.3459 |
| 133.4296      | 48.53 | 3300 | 41.1845         | 0.3475 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_50_try2", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 

dgx1\_distil\_w2v2\_base\_mozilla\_12\_to\_6\_batch\_16\_epoch\_50\_try2
========================================================================


This model is a fine-tuned version of rohitp1/dgx1\_distil\_w2v2\_base\_mozilla\_12\_to\_6\_batch\_16\_epoch\_20 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 41.1845
* Wer: 0.3475


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: 0.0005
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2