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# wav2vec2-xlsr-korean-speech-emotion-recognition

This model is a fine-tuned version of [jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15](https://huggingface.co/jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6651
- Accuracy: 0.7667

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7098        | 0.02  | 20   | 1.6849          | 0.1986   |
| 1.6093        | 0.05  | 40   | 1.6102          | 0.2237   |
| 1.5673        | 0.07  | 60   | 1.5126          | 0.3696   |
| 1.554         | 0.1   | 80   | 1.4260          | 0.3624   |
| 1.3676        | 0.12  | 100  | 1.2504          | 0.4761   |
| 1.3763        | 0.14  | 120  | 1.3997          | 0.4091   |
| 1.2727        | 0.17  | 140  | 1.3728          | 0.4211   |
| 1.4632        | 0.19  | 160  | 1.2427          | 0.4653   |
| 1.2167        | 0.22  | 180  | 1.1379          | 0.5371   |
| 1.1789        | 0.24  | 200  | 1.1165          | 0.5514   |
| 1.1639        | 0.26  | 220  | 1.0029          | 0.5813   |
| 1.1664        | 0.29  | 240  | 1.1682          | 0.4844   |
| 1.1223        | 0.31  | 260  | 1.0946          | 0.5526   |
| 1.2748        | 0.33  | 280  | 1.0023          | 0.5837   |
| 1.1422        | 0.36  | 300  | 1.0873          | 0.5215   |
| 0.9999        | 0.38  | 320  | 1.0378          | 0.5742   |
| 1.0444        | 0.41  | 340  | 1.0049          | 0.5921   |
| 1.0099        | 0.43  | 360  | 0.9367          | 0.6160   |
| 0.9251        | 0.45  | 380  | 1.0681          | 0.5694   |
| 0.8257        | 0.48  | 400  | 1.0516          | 0.5813   |
| 1.3777        | 0.5   | 420  | 1.0250          | 0.6280   |
| 0.9415        | 0.53  | 440  | 0.9033          | 0.6316   |
| 0.9927        | 0.55  | 460  | 0.9360          | 0.6184   |
| 1.0245        | 0.57  | 480  | 0.9133          | 0.6232   |
| 1.145         | 0.6   | 500  | 0.8876          | 0.6459   |
| 0.916         | 0.62  | 520  | 0.9421          | 0.6256   |
| 0.9833        | 0.65  | 540  | 0.8567          | 0.6400   |
| 0.7927        | 0.67  | 560  | 0.9217          | 0.6579   |
| 1.0793        | 0.69  | 580  | 0.8519          | 0.6663   |
| 0.9642        | 0.72  | 600  | 0.8192          | 0.6758   |
| 0.9433        | 0.74  | 620  | 0.8412          | 0.6627   |
| 0.5469        | 0.77  | 640  | 0.8177          | 0.6794   |
| 0.9734        | 0.79  | 660  | 0.8130          | 0.6794   |
| 0.7071        | 0.81  | 680  | 0.9579          | 0.6579   |
| 0.9347        | 0.84  | 700  | 0.7870          | 0.6878   |
| 0.8024        | 0.86  | 720  | 0.9395          | 0.6411   |
| 0.9428        | 0.88  | 740  | 0.8817          | 0.6663   |
| 0.8024        | 0.91  | 760  | 0.7953          | 0.6974   |
| 0.7028        | 0.93  | 780  | 0.8279          | 0.6890   |
| 0.8357        | 0.96  | 800  | 0.8291          | 0.6950   |
| 0.8304        | 0.98  | 820  | 0.7778          | 0.6914   |
| 0.8157        | 1.0   | 840  | 0.7726          | 0.7081   |
| 0.5431        | 1.03  | 860  | 0.7628          | 0.7117   |
| 0.6816        | 1.05  | 880  | 0.7928          | 0.7057   |
| 0.7498        | 1.08  | 900  | 0.7732          | 0.7069   |
| 0.6785        | 1.1   | 920  | 0.8298          | 0.7033   |
| 0.5951        | 1.12  | 940  | 0.8020          | 0.7225   |
| 0.4782        | 1.15  | 960  | 0.7541          | 0.7297   |
| 0.6825        | 1.17  | 980  | 0.9647          | 0.6687   |
| 0.5793        | 1.2   | 1000 | 0.9230          | 0.7057   |
| 0.4872        | 1.22  | 1020 | 0.8361          | 0.7141   |
| 0.5422        | 1.24  | 1040 | 0.8629          | 0.7225   |
| 0.5671        | 1.27  | 1060 | 0.8376          | 0.7261   |
| 0.6285        | 1.29  | 1080 | 0.7478          | 0.7524   |
| 0.6647        | 1.32  | 1100 | 0.8001          | 0.7261   |
| 0.7846        | 1.34  | 1120 | 0.7344          | 0.7428   |
| 0.6362        | 1.36  | 1140 | 0.6980          | 0.75     |
| 0.4498        | 1.39  | 1160 | 0.7186          | 0.7536   |
| 0.5002        | 1.41  | 1180 | 0.7003          | 0.7656   |
| 0.5366        | 1.44  | 1200 | 0.7670          | 0.7404   |
| 0.6324        | 1.46  | 1220 | 0.7095          | 0.7536   |
| 0.6089        | 1.48  | 1240 | 0.7791          | 0.7428   |
| 0.5368        | 1.51  | 1260 | 0.6889          | 0.7679   |
| 0.6087        | 1.53  | 1280 | 0.7078          | 0.7620   |
| 0.5514        | 1.55  | 1300 | 0.7207          | 0.7644   |
| 0.6472        | 1.58  | 1320 | 0.6864          | 0.7691   |
| 0.5265        | 1.6   | 1340 | 0.7005          | 0.7679   |
| 0.5339        | 1.63  | 1360 | 0.6983          | 0.7667   |
| 0.5886        | 1.65  | 1380 | 0.7075          | 0.7739   |
| 0.4888        | 1.67  | 1400 | 0.6967          | 0.7715   |
| 0.5152        | 1.7   | 1420 | 0.6864          | 0.7679   |
| 0.3835        | 1.72  | 1440 | 0.7277          | 0.7608   |
| 0.5798        | 1.75  | 1460 | 0.7023          | 0.7644   |
| 0.4918        | 1.77  | 1480 | 0.7007          | 0.7667   |
| 0.6233        | 1.79  | 1500 | 0.7429          | 0.7596   |
| 0.5679        | 1.82  | 1520 | 0.6910          | 0.7488   |
| 0.6393        | 1.84  | 1540 | 0.7151          | 0.7512   |
| 0.6167        | 1.87  | 1560 | 0.6984          | 0.7512   |
| 0.4379        | 1.89  | 1580 | 0.6685          | 0.7656   |
| 0.4348        | 1.91  | 1600 | 0.6662          | 0.7703   |
| 0.5685        | 1.94  | 1620 | 0.6648          | 0.7691   |
| 0.4126        | 1.96  | 1640 | 0.6656          | 0.7667   |
| 0.4846        | 1.99  | 1660 | 0.6651          | 0.7667   |


### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.1.dev0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-xlsr-korean-speech-emotion-recognition", "results": []}]}
transformers pytorch tensorboard wav2vec2 generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-xlsr-korean-speech-emotion-recognition
===============================================


This model is a fine-tuned version of jungjongho/wav2vec2-large-xlsr-korean-demo-colab\_epoch15 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6651
* Accuracy: 0.7667


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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.4.1.dev0
* Tokenizers 0.12.1