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# wav2vec2-large-voxrex-swedish-coscan-no-region

This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex-swedish](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) on the coscan-speech2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0151
- Accuracy: 0.8773
- F1: 0.8773
- Precision: 0.8773
- Recall: 0.8773

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1651        | 1.0   | 6468  | 0.5657          | 0.8650   | 0.8650 | 0.8650    | 0.8650 |
| 0.1217        | 2.0   | 12936 | 0.9411          | 0.8487   | 0.8487 | 0.8487    | 0.8487 |
| 0.0013        | 3.0   | 19404 | 0.9991          | 0.8617   | 0.8617 | 0.8617    | 0.8617 |
| 0.0652        | 4.0   | 25872 | 1.0151          | 0.8773   | 0.8773 | 0.8773    | 0.8773 |
| 0.0001        | 5.0   | 32340 | 1.1031          | 0.8700   | 0.8700 | 0.8700    | 0.8700 |


### Classification report on Coscan Speech (test set)

```
                         precision    recall  f1-score   support

Bergen og Ytre Vestland       0.65      0.97      0.78      1809
     Hedmark og Oppland       0.12      0.06      0.08      2302
               Nordland       0.97      0.47      0.63      2195
           Oslo-området       0.78      0.42      0.55      6957
               Sunnmøre       0.94      0.71      0.81      2636
         Sør-Vestlandet       0.96      0.46      0.62      2860
              Sørlandet       0.62      0.81      0.70      2490
                  Troms       0.67      1.00      0.80      2867
              Trøndelag       0.52      0.94      0.67      2666
         Voss og omland       0.70      0.71      0.71      2641
         Ytre Oslofjord       0.20      0.49      0.29      1678

               accuracy                           0.62     31101
              macro avg       0.65      0.64      0.60     31101
           weighted avg       0.68      0.62      0.61     31101

```


### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 2.4.1.dev0
- Tokenizers 0.12.1
{"license": "cc0-1.0", "tags": ["generated_from_trainer"], "datasets": ["coscan-speech2"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "wav2vec2-large-voxrex-swedish-coscan-no-region", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "Coscan Speech", "type": "NbAiLab/coscan-speech2"}, "metrics": [{"type": "accuracy", "value": 0.6155107552811807, "name": "Test Accuracy on Coscan Speech"}, {"type": "accuracy", "value": 0.8773432861141742, "name": "Validation Accuracy on Coscan Speech"}, {"type": "f1", "value": 0.6155107552811807, "name": "Test F1 (micro) on Coscan Speech"}, {"type": "f1", "value": 0.8773432861141742, "name": "Validation F1 (micro) on Coscan Speech"}]}]}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer dataset:coscan-speech2 license:cc0-1.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-coscan-speech2 #license-cc0-1.0 #model-index #endpoints_compatible #region-us 

wav2vec2-large-voxrex-swedish-coscan-no-region
==============================================


This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex-swedish on the coscan-speech2 dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0151
* Accuracy: 0.8773
* F1: 0.8773
* Precision: 0.8773
* Recall: 0.8773


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: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5


### Training results



### Classification report on Coscan Speech (test set)


### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Classification report on Coscan Speech (test set) ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-coscan-speech2 #license-cc0-1.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Classification report on Coscan Speech (test set) ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-coscan-speech2 #license-cc0-1.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5### Training results### Classification report on Coscan Speech (test set)### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1