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# wavlm-libri-clean-100h-base

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0829
- Wer: 0.0675

## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8805        | 0.34  | 300  | 2.8686          | 1.0    |
| 0.2459        | 0.67  | 600  | 0.1858          | 0.1554 |
| 0.1114        | 1.01  | 900  | 0.1379          | 0.1191 |
| 0.0867        | 1.35  | 1200 | 0.1130          | 0.0961 |
| 0.0698        | 1.68  | 1500 | 0.1032          | 0.0877 |
| 0.0663        | 2.02  | 1800 | 0.0959          | 0.0785 |
| 0.0451        | 2.35  | 2100 | 0.0887          | 0.0748 |
| 0.0392        | 2.69  | 2400 | 0.0859          | 0.0698 |


### Framework versions

- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3

{"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "wavlm_libri_finetune"], "model-index": [{"name": "wavlm-libri-clean-100h-base", "results": []}]}
transformers pytorch tensorboard wavlm automatic-speech-recognition librispeech_asr generated_from_trainer wavlm_libri_finetune endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us 

wavlm-libri-clean-100h-base
===========================


This model is a fine-tuned version of microsoft/wavlm-base on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0829
* Wer: 0.0675


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: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.15.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.15.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.15.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #wavlm_libri_finetune #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.15.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3