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# dgx1_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16

This model is a fine-tuned version of [patrickvonplaten/wavlm-libri-clean-100h-base-plus](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1396
- Wer: 0.0921

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2792        | 5.91  | 150  | 0.2173          | 0.1413 |
| 0.1681        | 11.81 | 300  | 0.1653          | 0.1146 |
| 0.1325        | 17.72 | 450  | 0.1510          | 0.1040 |
| 0.1148        | 23.63 | 600  | 0.1451          | 0.0992 |
| 0.1044        | 29.53 | 750  | 0.1418          | 0.0964 |
| 0.0969        | 35.44 | 900  | 0.1403          | 0.0947 |
| 0.0924        | 41.35 | 1050 | 0.1403          | 0.0929 |
| 0.0899        | 47.25 | 1200 | 0.1396          | 0.0921 |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "dgx1_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_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 

dgx1\_w2lm\_base\_plus\_finetune\_teacher\_noise\_libri360\_50\_epochs\_batch\_16
=================================================================================


This model is a fine-tuned version of patrickvonplaten/wavlm-libri-clean-100h-base-plus on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1396
* Wer: 0.0921


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 2.6.1
* Tokenizers 0.13.1



### 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 2.6.1
* Tokenizers 0.13.1
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 2.6.1
* Tokenizers 0.13.1