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# wav2vec2-large-xls-r-300m-pt-colab

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_9_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2975
- Wer: 0.1736

## 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: 0.0003
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.179         | 0.49  | 400   | 1.4554          | 0.9349 |
| 0.7545        | 0.98  | 800   | 0.5594          | 0.5174 |
| 0.4485        | 1.47  | 1200  | 0.3964          | 0.3749 |
| 0.4118        | 1.96  | 1600  | 0.3547          | 0.3172 |
| 0.3282        | 2.45  | 2000  | 0.3372          | 0.3061 |
| 0.3199        | 2.94  | 2400  | 0.3466          | 0.2910 |
| 0.2847        | 3.44  | 2800  | 0.3651          | 0.3310 |
| 0.2713        | 3.93  | 3200  | 0.3509          | 0.3016 |
| 0.2414        | 4.42  | 3600  | 0.3451          | 0.2908 |
| 0.2473        | 4.91  | 4000  | 0.3253          | 0.2747 |
| 0.2168        | 5.4   | 4400  | 0.3243          | 0.2680 |
| 0.219         | 5.89  | 4800  | 0.3067          | 0.2540 |
| 0.196         | 6.38  | 5200  | 0.3268          | 0.2824 |
| 0.1934        | 6.87  | 5600  | 0.3252          | 0.2736 |
| 0.1808        | 7.36  | 6000  | 0.3422          | 0.2737 |
| 0.177         | 7.85  | 6400  | 0.3292          | 0.2707 |
| 0.1626        | 8.34  | 6800  | 0.3089          | 0.2524 |
| 0.1605        | 8.83  | 7200  | 0.3062          | 0.2471 |
| 0.1505        | 9.32  | 7600  | 0.3229          | 0.2474 |
| 0.1491        | 9.82  | 8000  | 0.3098          | 0.2491 |
| 0.1433        | 10.31 | 8400  | 0.3449          | 0.2681 |
| 0.1431        | 10.8  | 8800  | 0.3439          | 0.2532 |
| 0.1349        | 11.29 | 9200  | 0.3112          | 0.2413 |
| 0.1236        | 11.78 | 9600  | 0.3248          | 0.2378 |
| 0.1253        | 12.27 | 10000 | 0.3393          | 0.2394 |
| 0.1195        | 12.76 | 10400 | 0.3050          | 0.2336 |
| 0.1194        | 13.25 | 10800 | 0.3494          | 0.2550 |
| 0.1125        | 13.74 | 11200 | 0.3332          | 0.2395 |
| 0.1063        | 14.23 | 11600 | 0.3134          | 0.2365 |
| 0.1044        | 14.72 | 12000 | 0.3101          | 0.2303 |
| 0.0999        | 15.21 | 12400 | 0.3162          | 0.2248 |
| 0.0986        | 15.71 | 12800 | 0.3183          | 0.2260 |
| 0.0958        | 16.2  | 13200 | 0.3300          | 0.2279 |
| 0.0907        | 16.69 | 13600 | 0.3136          | 0.2260 |
| 0.0875        | 17.18 | 14000 | 0.3492          | 0.2203 |
| 0.0823        | 17.67 | 14400 | 0.3214          | 0.2259 |
| 0.0839        | 18.16 | 14800 | 0.3194          | 0.2145 |
| 0.0783        | 18.65 | 15200 | 0.3122          | 0.2180 |
| 0.0789        | 19.14 | 15600 | 0.3158          | 0.2127 |
| 0.0732        | 19.63 | 16000 | 0.3076          | 0.2109 |
| 0.0715        | 20.12 | 16400 | 0.3216          | 0.2150 |
| 0.0649        | 20.61 | 16800 | 0.2958          | 0.2051 |
| 0.0647        | 21.1  | 17200 | 0.3022          | 0.2014 |
| 0.0649        | 21.59 | 17600 | 0.3045          | 0.2033 |
| 0.0621        | 22.09 | 18000 | 0.3194          | 0.2035 |
| 0.0561        | 22.58 | 18400 | 0.3197          | 0.2022 |
| 0.0582        | 23.07 | 18800 | 0.3109          | 0.1978 |
| 0.0533        | 23.56 | 19200 | 0.3121          | 0.1932 |
| 0.0515        | 24.05 | 19600 | 0.3125          | 0.1939 |
| 0.0484        | 24.54 | 20000 | 0.3081          | 0.1908 |
| 0.0485        | 25.03 | 20400 | 0.3042          | 0.1896 |
| 0.0444        | 25.52 | 20800 | 0.3038          | 0.1886 |
| 0.0426        | 26.01 | 21200 | 0.2985          | 0.1868 |
| 0.0415        | 26.5  | 21600 | 0.3066          | 0.1858 |
| 0.0398        | 26.99 | 22000 | 0.3117          | 0.1828 |
| 0.0397        | 27.48 | 22400 | 0.2980          | 0.1795 |
| 0.0394        | 27.97 | 22800 | 0.2950          | 0.1791 |
| 0.0364        | 28.47 | 23200 | 0.3025          | 0.1773 |
| 0.0365        | 28.96 | 23600 | 0.3022          | 0.1747 |
| 0.0376        | 29.45 | 24000 | 0.2978          | 0.1738 |
| 0.0344        | 29.94 | 24400 | 0.2975          | 0.1736 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_9_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pt-colab", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_9_0 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_9_0 #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-pt-colab
==================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_9\_0 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2975
* Wer: 0.1736


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: 0.0003
* 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: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_9_0 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_9_0 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.0+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1