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# fine-tune-Wav2Vec2-XLS-R-300M-Indonesia

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7924
- Wer: 0.4307

## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8421        | 6.33  | 500  | 0.7758          | 0.7386 |
| 0.1875        | 12.66 | 1000 | 0.6039          | 0.5712 |
| 0.1138        | 18.99 | 1500 | 0.6463          | 0.5170 |
| 0.0774        | 25.32 | 2000 | 0.6814          | 0.4914 |
| 0.0655        | 31.65 | 2500 | 0.7010          | 0.4778 |
| 0.0536        | 37.97 | 3000 | 0.7267          | 0.4761 |
| 0.0471        | 44.3  | 3500 | 0.7552          | 0.4693 |
| 0.0402        | 50.63 | 4000 | 0.7474          | 0.4612 |
| 0.0366        | 56.96 | 4500 | 0.7705          | 0.4560 |
| 0.0324        | 63.29 | 5000 | 0.7253          | 0.4472 |
| 0.0293        | 69.62 | 5500 | 0.7660          | 0.4441 |
| 0.0254        | 75.95 | 6000 | 0.7816          | 0.4398 |
| 0.0226        | 82.28 | 6500 | 0.7988          | 0.4378 |
| 0.0202        | 88.61 | 7000 | 0.8009          | 0.4305 |
| 0.0185        | 94.94 | 7500 | 0.7924          | 0.4307 |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_10_0"], "model-index": [{"name": "fine-tune-Wav2Vec2-XLS-R-300M-Indonesia", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_10_0 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us 

fine-tune-Wav2Vec2-XLS-R-300M-Indonesia
=======================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_10\_0 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7924
* Wer: 0.4307


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: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.22.2
* Pytorch 1.10.0+cu113
* Datasets 2.5.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.22.2
* Pytorch 1.10.0+cu113
* Datasets 2.5.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.22.2
* Pytorch 1.10.0+cu113
* Datasets 2.5.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.22.2
* Pytorch 1.10.0+cu113
* Datasets 2.5.2
* Tokenizers 0.12.1