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# wavlm-basic_s-f-c_8batch_5sec_0.0001lr_unfrozen

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8095
- Accuracy: 0.85
- F1: 0.8383

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.003
- num_epochs: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.2489        | 0.99  | 47   | 2.3092          | 0.1      | 0.0182 |
| 1.8953        | 2.0   | 95   | 2.1986          | 0.2      | 0.0807 |
| 1.6269        | 2.99  | 142  | 2.0505          | 0.2667   | 0.1554 |
| 1.4844        | 4.0   | 190  | 1.7348          | 0.4333   | 0.3482 |
| 1.2047        | 4.99  | 237  | 1.3970          | 0.5833   | 0.4907 |
| 1.005         | 6.0   | 285  | 1.3947          | 0.6      | 0.4957 |
| 0.8541        | 6.99  | 332  | 1.0432          | 0.65     | 0.5830 |
| 0.7027        | 8.0   | 380  | 1.0033          | 0.7333   | 0.6992 |
| 0.72          | 8.99  | 427  | 0.9982          | 0.7833   | 0.7657 |
| 0.5461        | 10.0  | 475  | 1.1170          | 0.6833   | 0.6571 |
| 0.4415        | 10.99 | 522  | 0.9240          | 0.75     | 0.7402 |
| 0.4022        | 12.0  | 570  | 0.9522          | 0.7667   | 0.7488 |
| 0.3664        | 12.99 | 617  | 0.8290          | 0.8333   | 0.8253 |
| 0.3592        | 14.0  | 665  | 1.0270          | 0.75     | 0.7313 |
| 0.2985        | 14.99 | 712  | 1.0835          | 0.7667   | 0.7591 |
| 0.2565        | 16.0  | 760  | 0.9175          | 0.8167   | 0.8090 |
| 0.2887        | 16.99 | 807  | 0.8095          | 0.85     | 0.8383 |
| 0.3038        | 18.0  | 855  | 0.8871          | 0.7833   | 0.7763 |
| 0.242         | 18.99 | 902  | 0.8786          | 0.8      | 0.7875 |
| 0.1994        | 20.0  | 950  | 1.0309          | 0.7833   | 0.7656 |
| 0.1569        | 20.99 | 997  | 1.0706          | 0.8      | 0.7886 |
| 0.1637        | 22.0  | 1045 | 0.9650          | 0.8333   | 0.8249 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wavlm-basic_s-f-c_8batch_5sec_0.0001lr_unfrozen", "results": []}]}
transformers pytorch tensorboard wavlm audio-classification generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wavlm #audio-classification #generated_from_trainer #endpoints_compatible #region-us 

wavlm-basic\_s-f-c\_8batch\_5sec\_0.0001lr\_unfrozen
====================================================


This model is a fine-tuned version of microsoft/wavlm-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8095
* Accuracy: 0.85
* F1: 0.8383


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wavlm #audio-classification #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wavlm #audio-classification #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.003
* num\_epochs: 1000### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3