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# faiq-wav2vec2-large-xlsr-indo-demo-t4-newparam

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2923
- Wer: 0.4000

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.5954       | 0.73  | 100  | 3.9916          | 1.0    |
| 3.1442        | 1.46  | 200  | 2.9161          | 1.0    |
| 2.8909        | 2.19  | 300  | 2.8833          | 1.0    |
| 2.8336        | 2.92  | 400  | 2.7413          | 1.0    |
| 2.4988        | 3.65  | 500  | 1.3723          | 1.0262 |
| 1.3458        | 4.38  | 600  | 0.5772          | 0.6982 |
| 1.006         | 5.11  | 700  | 0.4542          | 0.6162 |
| 0.8851        | 5.84  | 800  | 0.3988          | 0.5887 |
| 0.7914        | 6.57  | 900  | 0.3782          | 0.5601 |
| 0.7634        | 7.3   | 1000 | 0.3500          | 0.5309 |
| 0.7245        | 8.03  | 1100 | 0.3378          | 0.5198 |
| 0.7061        | 8.76  | 1200 | 0.3362          | 0.5198 |
| 0.6586        | 9.49  | 1300 | 0.3286          | 0.5094 |
| 0.6506        | 10.22 | 1400 | 0.3176          | 0.4910 |
| 0.6216        | 10.95 | 1500 | 0.3051          | 0.4804 |
| 0.6056        | 11.68 | 1600 | 0.3109          | 0.4787 |
| 0.5956        | 12.41 | 1700 | 0.2930          | 0.4748 |
| 0.5748        | 13.14 | 1800 | 0.3028          | 0.4604 |
| 0.5635        | 13.87 | 1900 | 0.3050          | 0.4671 |
| 0.5488        | 14.6  | 2000 | 0.3043          | 0.4570 |
| 0.5401        | 15.33 | 2100 | 0.3005          | 0.4605 |
| 0.5354        | 16.06 | 2200 | 0.2925          | 0.4508 |
| 0.5217        | 16.79 | 2300 | 0.2852          | 0.4440 |
| 0.5103        | 17.52 | 2400 | 0.3000          | 0.4479 |
| 0.5109        | 18.25 | 2500 | 0.2837          | 0.4470 |
| 0.4992        | 18.98 | 2600 | 0.2855          | 0.4464 |
| 0.4967        | 19.71 | 2700 | 0.2842          | 0.4379 |
| 0.4892        | 20.44 | 2800 | 0.2872          | 0.4359 |
| 0.4771        | 21.17 | 2900 | 0.2982          | 0.4350 |
| 0.4664        | 21.9  | 3000 | 0.2998          | 0.4317 |
| 0.4667        | 22.63 | 3100 | 0.2853          | 0.4340 |
| 0.4566        | 23.36 | 3200 | 0.2884          | 0.4302 |
| 0.4533        | 24.09 | 3300 | 0.2960          | 0.4267 |
| 0.441         | 24.82 | 3400 | 0.2921          | 0.4292 |
| 0.4241        | 25.55 | 3500 | 0.2805          | 0.4271 |
| 0.4488        | 26.28 | 3600 | 0.2907          | 0.4267 |
| 0.4348        | 27.01 | 3700 | 0.2712          | 0.4211 |
| 0.4266        | 27.74 | 3800 | 0.2783          | 0.4216 |
| 0.4133        | 28.47 | 3900 | 0.2763          | 0.4224 |
| 0.4224        | 29.2  | 4000 | 0.2829          | 0.4232 |
| 0.4134        | 29.93 | 4100 | 0.2836          | 0.4135 |
| 0.3982        | 30.66 | 4200 | 0.2913          | 0.4199 |
| 0.3998        | 31.39 | 4300 | 0.2914          | 0.4192 |
| 0.3971        | 32.12 | 4400 | 0.2873          | 0.4174 |
| 0.3908        | 32.85 | 4500 | 0.2885          | 0.4253 |
| 0.4           | 33.58 | 4600 | 0.2871          | 0.4209 |
| 0.3915        | 34.31 | 4700 | 0.2897          | 0.4156 |
| 0.3735        | 35.04 | 4800 | 0.2893          | 0.4136 |
| 0.375         | 35.77 | 4900 | 0.2998          | 0.4133 |
| 0.3847        | 36.5  | 5000 | 0.2820          | 0.4088 |
| 0.3723        | 37.23 | 5100 | 0.2859          | 0.4110 |
| 0.3832        | 37.96 | 5200 | 0.2836          | 0.4107 |
| 0.3714        | 38.69 | 5300 | 0.2798          | 0.4066 |
| 0.3735        | 39.42 | 5400 | 0.2839          | 0.4064 |
| 0.365         | 40.15 | 5500 | 0.2834          | 0.4067 |
| 0.3618        | 40.88 | 5600 | 0.2903          | 0.4100 |
| 0.3654        | 41.61 | 5700 | 0.2829          | 0.4067 |
| 0.3546        | 42.34 | 5800 | 0.3003          | 0.4071 |
| 0.3496        | 43.07 | 5900 | 0.2914          | 0.4080 |
| 0.3465        | 43.8  | 6000 | 0.2867          | 0.4029 |
| 0.352         | 44.53 | 6100 | 0.2927          | 0.4041 |
| 0.349         | 45.26 | 6200 | 0.2900          | 0.4057 |
| 0.3494        | 45.99 | 6300 | 0.3001          | 0.4047 |
| 0.3408        | 46.72 | 6400 | 0.2859          | 0.4054 |
| 0.3424        | 47.45 | 6500 | 0.2917          | 0.4063 |
| 0.3365        | 48.18 | 6600 | 0.2961          | 0.4044 |
| 0.334         | 48.91 | 6700 | 0.2913          | 0.4035 |
| 0.3378        | 49.64 | 6800 | 0.2945          | 0.4027 |
| 0.3251        | 50.36 | 6900 | 0.2902          | 0.4015 |
| 0.3401        | 51.09 | 7000 | 0.2892          | 0.4026 |
| 0.3248        | 51.82 | 7100 | 0.2931          | 0.4028 |
| 0.3205        | 52.55 | 7200 | 0.2895          | 0.4040 |
| 0.3254        | 53.28 | 7300 | 0.2969          | 0.4008 |
| 0.3414        | 54.01 | 7400 | 0.2971          | 0.4009 |
| 0.3252        | 54.74 | 7500 | 0.2946          | 0.4022 |
| 0.3166        | 55.47 | 7600 | 0.2953          | 0.4019 |
| 0.3299        | 56.2  | 7700 | 0.2949          | 0.4015 |
| 0.3275        | 56.93 | 7800 | 0.2897          | 0.4005 |
| 0.325         | 57.66 | 7900 | 0.2904          | 0.4002 |
| 0.3278        | 58.39 | 8000 | 0.2885          | 0.3994 |
| 0.3118        | 59.12 | 8100 | 0.2931          | 0.3999 |
| 0.3342        | 59.85 | 8200 | 0.2923          | 0.4000 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.6.1
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_11_0"], "metrics": ["wer"], "model-index": [{"name": "faiq-wav2vec2-large-xlsr-indo-demo-t4-newparam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice_11_0", "type": "common_voice_11_0", "config": "id", "split": "test", "args": "id"}, "metrics": [{"type": "wer", "value": 0.3999813875575822, "name": "Wer"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_11_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_11_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

faiq-wav2vec2-large-xlsr-indo-demo-t4-newparam
==============================================


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice\_11\_0 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2923
* Wer: 0.4000


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: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 60
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.6.1
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 60
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.6.1
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_11_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 60
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.6.1
* Tokenizers 0.13.3