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# wavlm-libri-clean-100h-large

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0601
- Wer: 0.0491

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8069        | 0.34  | 300  | 0.7510          | 0.5809 |
| 0.2483        | 0.67  | 600  | 0.2023          | 0.1929 |
| 0.1033        | 1.01  | 900  | 0.1123          | 0.1028 |
| 0.0742        | 1.35  | 1200 | 0.0858          | 0.0771 |
| 0.057         | 1.68  | 1500 | 0.0722          | 0.0663 |
| 0.0421        | 2.02  | 1800 | 0.0682          | 0.0582 |
| 0.0839        | 2.35  | 2100 | 0.0630          | 0.0534 |
| 0.0307        | 2.69  | 2400 | 0.0603          | 0.0508 |


### 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-librispeech-clean-100h-dist", "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-large
============================


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


* Loss: 0.0601
* Wer: 0.0491


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: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* 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