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# subh_w2lm_base_distill_noisy_teacher_mozilla_epochs_50_batch_16

This model is a fine-tuned version of [rohitp1/ws_w2lm_base_plus_finetune_teacher_noise_mozilla_100_epochs_batch_8](https://huggingface.co/rohitp1/ws_w2lm_base_plus_finetune_teacher_noise_mozilla_100_epochs_batch_8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4070
- Wer: 0.3226

## 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: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 4096
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1283        | 7.31  | 250  | 0.3295          | 0.3266 |
| 0.1111        | 14.63 | 500  | 0.3444          | 0.3236 |
| 0.0939        | 21.94 | 750  | 0.3664          | 0.3236 |
| 0.0826        | 29.26 | 1000 | 0.3828          | 0.3224 |
| 0.0751        | 36.57 | 1250 | 0.3977          | 0.3223 |
| 0.0703        | 43.89 | 1500 | 0.4070          | 0.3226 |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "subh_w2lm_base_distill_noisy_teacher_mozilla_epochs_50_batch_16", "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 

subh\_w2lm\_base\_distill\_noisy\_teacher\_mozilla\_epochs\_50\_batch\_16
=========================================================================


This model is a fine-tuned version of rohitp1/ws\_w2lm\_base\_plus\_finetune\_teacher\_noise\_mozilla\_100\_epochs\_batch\_8 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4070
* Wer: 0.3226


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: 16
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 4096
* 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 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 4096
* 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 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
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: 16
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 4096
* 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 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2