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# microsoft-wavlm-fleurs-ur

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7294
- Wer: 0.4026

## 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: 2
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.911         | 0.35  | 100  | 3.7784          | 1.0    |
| 3.0833        | 0.71  | 200  | 3.0964          | 1.0    |
| 3.028         | 1.06  | 300  | 3.0377          | 1.0    |
| 2.5114        | 1.41  | 400  | 2.4941          | 0.9922 |
| 1.0583        | 1.77  | 500  | 1.0753          | 0.7579 |
| 0.715         | 2.12  | 600  | 0.8524          | 0.6410 |
| 0.6779        | 2.47  | 700  | 0.7711          | 0.6063 |
| 0.6123        | 2.83  | 800  | 0.7170          | 0.5706 |
| 0.8183        | 3.18  | 900  | 0.6897          | 0.5368 |
| 0.5195        | 3.53  | 1000 | 0.6586          | 0.5303 |
| 0.4774        | 3.89  | 1100 | 0.6306          | 0.5014 |
| 0.4242        | 4.24  | 1200 | 0.6138          | 0.4817 |
| 0.4549        | 4.59  | 1300 | 0.6027          | 0.4678 |
| 0.2576        | 4.95  | 1400 | 0.5878          | 0.4600 |
| 0.1578        | 5.3   | 1500 | 0.6144          | 0.4585 |
| 0.3556        | 5.65  | 1600 | 0.5884          | 0.4582 |
| 0.2427        | 6.01  | 1700 | 0.6071          | 0.4572 |
| 0.267         | 6.36  | 1800 | 0.6303          | 0.4514 |
| 0.2468        | 6.71  | 1900 | 0.6358          | 0.4495 |
| 0.159         | 7.07  | 2000 | 0.6242          | 0.4312 |
| 0.1527        | 7.42  | 2100 | 0.6372          | 0.4400 |
| 0.1401        | 7.77  | 2200 | 0.6252          | 0.4292 |
| 0.1211        | 8.13  | 2300 | 0.6358          | 0.4251 |
| 0.1022        | 8.48  | 2400 | 0.6529          | 0.4356 |
| 0.0818        | 8.83  | 2500 | 0.6773          | 0.4200 |
| 0.0918        | 9.19  | 2600 | 0.6879          | 0.4267 |
| 0.119         | 9.54  | 2700 | 0.6948          | 0.4254 |
| 0.1615        | 9.89  | 2800 | 0.6920          | 0.4259 |
| 0.0953        | 10.25 | 2900 | 0.7019          | 0.4218 |
| 0.1008        | 10.6  | 3000 | 0.6933          | 0.4133 |
| 0.0729        | 10.95 | 3100 | 0.6950          | 0.4164 |
| 0.0636        | 11.31 | 3200 | 0.7151          | 0.4121 |
| 0.0395        | 11.66 | 3300 | 0.7053          | 0.4098 |
| 0.0391        | 12.01 | 3400 | 0.7081          | 0.3984 |
| 0.0507        | 12.37 | 3500 | 0.7012          | 0.4111 |
| 0.0598        | 12.72 | 3600 | 0.7169          | 0.4035 |
| 0.0515        | 13.07 | 3700 | 0.7358          | 0.4102 |
| 0.0429        | 13.43 | 3800 | 0.7236          | 0.4013 |
| 0.0398        | 13.78 | 3900 | 0.7404          | 0.4026 |
| 0.0946        | 14.13 | 4000 | 0.7285          | 0.4029 |
| 0.0428        | 14.49 | 4100 | 0.7271          | 0.3991 |
| 0.0329        | 14.84 | 4200 | 0.7294          | 0.4026 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["fleurs"], "metrics": ["wer"], "model-index": [{"name": "microsoft-wavlm-fleurs-ur", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "fleurs", "type": "fleurs", "config": "ur_pk", "split": "test", "args": "ur_pk"}, "metrics": [{"type": "wer", "value": 0.4026467344688151, "name": "Wer"}]}]}]}
transformers pytorch tensorboard wavlm automatic-speech-recognition generated_from_trainer dataset:fleurs model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #model-index #endpoints_compatible #region-us 

microsoft-wavlm-fleurs-ur
=========================


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


* Loss: 0.7294
* Wer: 0.4026


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: 2
* total\_train\_batch\_size: 8
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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: 2
* total\_train\_batch\_size: 8
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #model-index #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: 2
* total\_train\_batch\_size: 8
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #model-index #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: 2
* total\_train\_batch\_size: 8
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2