
<!-- 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-f-o_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: 1.3331
- Accuracy: 0.8
- F1: 0.8022

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.2668        | 1.0   | 131  | 2.1265          | 0.2833   | 0.1955 |
| 1.3925        | 2.0   | 262  | 1.3972          | 0.4833   | 0.4566 |
| 0.9643        | 2.99  | 393  | 1.0938          | 0.65     | 0.6166 |
| 0.6244        | 4.0   | 525  | 0.9165          | 0.75     | 0.7322 |
| 0.3692        | 5.0   | 656  | 0.9714          | 0.7333   | 0.7207 |
| 0.3284        | 6.0   | 787  | 0.5999          | 0.85     | 0.8515 |
| 0.2096        | 6.99  | 918  | 0.7120          | 0.8      | 0.8005 |
| 0.1974        | 8.0   | 1050 | 0.6648          | 0.8667   | 0.8671 |
| 0.1349        | 9.0   | 1181 | 0.7068          | 0.8333   | 0.8337 |
| 0.221         | 10.0  | 1312 | 1.2068          | 0.7667   | 0.7577 |
| 0.1403        | 10.99 | 1443 | 0.9815          | 0.85     | 0.8480 |
| 0.1211        | 12.0  | 1575 | 1.0742          | 0.7833   | 0.7890 |
| 0.0901        | 13.0  | 1706 | 0.8772          | 0.8167   | 0.8150 |


### 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-o_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-o\_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: 1.3331
* Accuracy: 0.8
* F1: 0.8022


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