
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wavlm-basic_s-r-5c_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: 0.9859
- Accuracy: 0.75
- F1: 0.7515

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.2767        | 0.33  | 78   | 2.3002          | 0.1      | 0.0182 |
| 2.0686        | 0.66  | 156  | 2.4001          | 0.1      | 0.0182 |
| 1.7043        | 0.99  | 234  | 2.1688          | 0.19     | 0.0875 |
| 1.6238        | 1.32  | 312  | 2.0125          | 0.2533   | 0.1313 |
| 1.4339        | 1.65  | 390  | 1.7132          | 0.4433   | 0.3567 |
| 1.2106        | 1.97  | 468  | 1.6403          | 0.5233   | 0.4524 |
| 1.0918        | 2.3   | 546  | 1.6254          | 0.58     | 0.5063 |
| 0.9621        | 2.63  | 624  | 1.3746          | 0.5967   | 0.5248 |
| 0.8272        | 2.96  | 702  | 1.1466          | 0.6333   | 0.5852 |
| 0.8004        | 3.29  | 780  | 1.0567          | 0.6633   | 0.5944 |
| 0.676         | 3.62  | 858  | 0.9788          | 0.6967   | 0.6457 |
| 0.6323        | 3.95  | 936  | 0.9743          | 0.7133   | 0.6946 |
| 0.609         | 4.28  | 1014 | 1.0422          | 0.6967   | 0.6768 |
| 0.6942        | 4.61  | 1092 | 1.1858          | 0.6833   | 0.6661 |
| 0.5759        | 4.94  | 1170 | 1.1483          | 0.7233   | 0.7183 |
| 0.4296        | 5.27  | 1248 | 1.0037          | 0.73     | 0.7224 |
| 0.4322        | 5.59  | 1326 | 0.7829          | 0.8067   | 0.8046 |
| 0.4092        | 5.92  | 1404 | 0.8609          | 0.7767   | 0.7743 |
| 0.352         | 6.25  | 1482 | 1.1247          | 0.72     | 0.7128 |
| 0.2858        | 6.58  | 1560 | 0.9369          | 0.76     | 0.7500 |
| 0.2945        | 6.91  | 1638 | 1.2018          | 0.7267   | 0.7083 |
| 0.329         | 7.24  | 1716 | 0.9690          | 0.7767   | 0.7786 |


### 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-r-5c_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-r-5c\_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: 0.9859
* Accuracy: 0.75
* F1: 0.7515


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