
<!-- 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. -->

# faiq-wav2vec2-large-xlsr-indo-demo-v100-newparameter

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: 0.2880
- Wer: 0.3926

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 11.1998       | 0.73  | 100  | 4.4160          | 1.0    |
| 3.166         | 1.46  | 200  | 2.9133          | 1.0    |
| 2.89          | 2.19  | 300  | 2.8775          | 1.0    |
| 2.8188        | 2.92  | 400  | 2.7385          | 1.0007 |
| 2.656         | 3.65  | 500  | 1.9852          | 1.0208 |
| 1.5392        | 4.38  | 600  | 0.6224          | 0.7274 |
| 1.0493        | 5.11  | 700  | 0.4720          | 0.6435 |
| 0.9097        | 5.84  | 800  | 0.4157          | 0.5892 |
| 0.8133        | 6.57  | 900  | 0.3720          | 0.5668 |
| 0.7818        | 7.3   | 1000 | 0.3570          | 0.5416 |
| 0.7376        | 8.03  | 1100 | 0.3346          | 0.5209 |
| 0.7131        | 8.76  | 1200 | 0.3234          | 0.5124 |
| 0.6624        | 9.49  | 1300 | 0.3313          | 0.5005 |
| 0.6609        | 10.22 | 1400 | 0.3162          | 0.4882 |
| 0.6331        | 10.95 | 1500 | 0.3257          | 0.4850 |
| 0.6123        | 11.68 | 1600 | 0.3271          | 0.4804 |
| 0.6018        | 12.41 | 1700 | 0.3052          | 0.4729 |
| 0.5822        | 13.14 | 1800 | 0.3048          | 0.4642 |
| 0.5724        | 13.87 | 1900 | 0.3058          | 0.4698 |
| 0.5519        | 14.6  | 2000 | 0.2952          | 0.4601 |
| 0.54          | 15.33 | 2100 | 0.2889          | 0.4582 |
| 0.5393        | 16.06 | 2200 | 0.2875          | 0.4491 |
| 0.5273        | 16.79 | 2300 | 0.2843          | 0.4465 |
| 0.5145        | 17.52 | 2400 | 0.2782          | 0.4403 |
| 0.515         | 18.25 | 2500 | 0.2916          | 0.4457 |
| 0.5006        | 18.98 | 2600 | 0.2795          | 0.4418 |
| 0.5025        | 19.71 | 2700 | 0.2788          | 0.4360 |
| 0.491         | 20.44 | 2800 | 0.2903          | 0.4348 |
| 0.478         | 21.17 | 2900 | 0.2819          | 0.4264 |
| 0.4721        | 21.9  | 3000 | 0.2842          | 0.4258 |
| 0.4709        | 22.63 | 3100 | 0.2865          | 0.4316 |
| 0.4618        | 23.36 | 3200 | 0.2911          | 0.4414 |
| 0.4592        | 24.09 | 3300 | 0.2901          | 0.4267 |
| 0.4448        | 24.82 | 3400 | 0.2871          | 0.4247 |
| 0.4347        | 25.55 | 3500 | 0.2772          | 0.4225 |
| 0.4515        | 26.28 | 3600 | 0.2907          | 0.4203 |
| 0.4368        | 27.01 | 3700 | 0.2749          | 0.4175 |
| 0.4317        | 27.74 | 3800 | 0.2781          | 0.4204 |
| 0.4158        | 28.47 | 3900 | 0.2847          | 0.4214 |
| 0.4225        | 29.2  | 4000 | 0.2815          | 0.4160 |
| 0.4205        | 29.93 | 4100 | 0.2792          | 0.4088 |
| 0.4035        | 30.66 | 4200 | 0.2801          | 0.4097 |
| 0.405         | 31.39 | 4300 | 0.2853          | 0.4105 |
| 0.404         | 32.12 | 4400 | 0.2728          | 0.4083 |
| 0.3929        | 32.85 | 4500 | 0.2801          | 0.4118 |
| 0.4022        | 33.58 | 4600 | 0.2801          | 0.4058 |
| 0.3951        | 34.31 | 4700 | 0.2857          | 0.4095 |
| 0.3716        | 35.04 | 4800 | 0.2882          | 0.4055 |
| 0.3786        | 35.77 | 4900 | 0.2866          | 0.4056 |
| 0.3886        | 36.5  | 5000 | 0.2924          | 0.4090 |
| 0.3746        | 37.23 | 5100 | 0.2820          | 0.4066 |
| 0.3883        | 37.96 | 5200 | 0.2730          | 0.4006 |
| 0.374         | 38.69 | 5300 | 0.2825          | 0.3992 |
| 0.3741        | 39.42 | 5400 | 0.2950          | 0.4011 |
| 0.3709        | 40.15 | 5500 | 0.2930          | 0.4015 |
| 0.3579        | 40.88 | 5600 | 0.2919          | 0.4015 |
| 0.3704        | 41.61 | 5700 | 0.2826          | 0.4014 |
| 0.3648        | 42.34 | 5800 | 0.2847          | 0.4001 |
| 0.3549        | 43.07 | 5900 | 0.2934          | 0.4020 |
| 0.3522        | 43.8  | 6000 | 0.2846          | 0.4004 |
| 0.3575        | 44.53 | 6100 | 0.2892          | 0.3986 |
| 0.3512        | 45.26 | 6200 | 0.2952          | 0.4008 |
| 0.3525        | 45.99 | 6300 | 0.2918          | 0.3961 |
| 0.3414        | 46.72 | 6400 | 0.2884          | 0.3945 |
| 0.3458        | 47.45 | 6500 | 0.2918          | 0.3979 |
| 0.337         | 48.18 | 6600 | 0.2838          | 0.3933 |
| 0.3352        | 48.91 | 6700 | 0.2872          | 0.3939 |
| 0.3374        | 49.64 | 6800 | 0.2860          | 0.3941 |
| 0.327         | 50.36 | 6900 | 0.2820          | 0.3920 |
| 0.3396        | 51.09 | 7000 | 0.2884          | 0.3946 |
| 0.3246        | 51.82 | 7100 | 0.2960          | 0.3930 |
| 0.322         | 52.55 | 7200 | 0.2881          | 0.3949 |
| 0.331         | 53.28 | 7300 | 0.2927          | 0.3930 |
| 0.3406        | 54.01 | 7400 | 0.2940          | 0.3954 |
| 0.3292        | 54.74 | 7500 | 0.2873          | 0.3946 |
| 0.3209        | 55.47 | 7600 | 0.2881          | 0.3915 |
| 0.3275        | 56.2  | 7700 | 0.2921          | 0.3922 |
| 0.3311        | 56.93 | 7800 | 0.2877          | 0.3915 |
| 0.3282        | 57.66 | 7900 | 0.2866          | 0.3931 |
| 0.3255        | 58.39 | 8000 | 0.2863          | 0.3917 |
| 0.3182        | 59.12 | 8100 | 0.2878          | 0.3931 |
| 0.3321        | 59.85 | 8200 | 0.2880          | 0.3926 |


### 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-v100-newparameter", "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": 0.3926294728025685, "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-v100-newparameter
====================================================


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: 0.2880
* Wer: 0.3926


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