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# ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8

This model is a fine-tuned version of [rohitp1/kkkh_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16](https://huggingface.co/rohitp1/kkkh_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0945
- Wer: 0.1041

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0562        | 2.46  | 250  | 0.0741          | 0.1135 |
| 0.0538        | 4.92  | 500  | 0.0736          | 0.1126 |
| 0.0506        | 7.38  | 750  | 0.0751          | 0.1116 |
| 0.0465        | 9.84  | 1000 | 0.0752          | 0.1099 |
| 0.0424        | 12.31 | 1250 | 0.0762          | 0.1089 |
| 0.0385        | 14.77 | 1500 | 0.0790          | 0.1078 |
| 0.0355        | 17.23 | 1750 | 0.0788          | 0.1062 |
| 0.0335        | 19.69 | 2000 | 0.0795          | 0.1053 |
| 0.0314        | 22.15 | 2250 | 0.0825          | 0.1052 |
| 0.0298        | 24.61 | 2500 | 0.0837          | 0.1055 |
| 0.0285        | 27.07 | 2750 | 0.0873          | 0.1049 |
| 0.0274        | 29.53 | 3000 | 0.0868          | 0.1043 |
| 0.0266        | 32.0  | 3250 | 0.0891          | 0.1044 |
| 0.0256        | 34.46 | 3500 | 0.0902          | 0.1044 |
| 0.0251        | 36.92 | 3750 | 0.0911          | 0.1044 |
| 0.0247        | 39.38 | 4000 | 0.0926          | 0.1042 |
| 0.0242        | 41.84 | 4250 | 0.0936          | 0.1042 |
| 0.0238        | 44.3  | 4500 | 0.0940          | 0.1042 |
| 0.0235        | 46.76 | 4750 | 0.0938          | 0.1042 |
| 0.0233        | 49.22 | 5000 | 0.0945          | 0.1041 |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.7.1
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8", "results": []}]}
transformers pytorch tensorboard wavlm automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 

ws\_w2lm\_base\_distill\_noisy\_teacher\_libri\_epochs\_50\_batch\_8
====================================================================


This model is a fine-tuned version of rohitp1/kkkh\_w2lm\_base\_plus\_finetune\_teacher\_noise\_libri360\_50\_epochs\_batch\_16 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0945
* Wer: 0.1041


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* 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 4.29.2
* Pytorch 1.13.1
* Datasets 2.7.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* 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 4.29.2
* Pytorch 1.13.1
* Datasets 2.7.1
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* 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 4.29.2
* Pytorch 1.13.1
* Datasets 2.7.1
* Tokenizers 0.11.0