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# wav2vec2-large-xls-r-300m-irish-colab_test

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 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7839
- Wer: 0.6220

## 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: 100
- num_epochs: 90
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.0428       | 2.94  | 50   | 4.1311          | 1.0    |
| 3.2917        | 5.88  | 100  | 3.1468          | 1.0    |
| 3.0221        | 8.82  | 150  | 2.9848          | 1.0    |
| 2.9795        | 11.76 | 200  | 2.9567          | 1.0    |
| 2.9379        | 14.71 | 250  | 2.9463          | 1.0    |
| 2.9068        | 17.65 | 300  | 2.8330          | 1.0    |
| 2.5088        | 20.59 | 350  | 1.9807          | 0.9535 |
| 1.6188        | 23.53 | 400  | 1.4254          | 0.8398 |
| 1.0435        | 26.47 | 450  | 1.3668          | 0.7807 |
| 0.7212        | 29.41 | 500  | 1.3914          | 0.7476 |
| 0.5456        | 32.35 | 550  | 1.5495          | 0.7470 |
| 0.4297        | 35.29 | 600  | 1.4751          | 0.6960 |
| 0.3533        | 38.24 | 650  | 1.5157          | 0.6909 |
| 0.2899        | 41.18 | 700  | 1.5394          | 0.6879 |
| 0.2529        | 44.12 | 750  | 1.6186          | 0.6903 |
| 0.2413        | 47.06 | 800  | 1.6386          | 0.6954 |
| 0.2113        | 50.0  | 850  | 1.6906          | 0.6778 |
| 0.1769        | 52.94 | 900  | 1.6918          | 0.6575 |
| 0.1622        | 55.88 | 950  | 1.7313          | 0.6572 |
| 0.1564        | 58.82 | 1000 | 1.7701          | 0.6510 |
| 0.1637        | 61.76 | 1050 | 1.6800          | 0.6444 |
| 0.148         | 64.71 | 1100 | 1.7306          | 0.6477 |
| 0.1385        | 67.65 | 1150 | 1.7605          | 0.6408 |
| 0.1264        | 70.59 | 1200 | 1.7534          | 0.6244 |
| 0.1157        | 73.53 | 1250 | 1.7906          | 0.6381 |
| 0.1027        | 76.47 | 1300 | 1.7803          | 0.6265 |
| 0.1061        | 79.41 | 1350 | 1.7617          | 0.6259 |
| 0.0934        | 82.35 | 1400 | 1.7649          | 0.6253 |
| 0.0904        | 85.29 | 1450 | 1.7713          | 0.6187 |
| 0.0911        | 88.24 | 1500 | 1.7839          | 0.6220 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3

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

wav2vec2-large-xls-r-300m-irish-colab\_test
===========================================


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


* Loss: 1.7839
* Wer: 0.6220


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: 100
* num\_epochs: 90
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.10.3



### 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: 100
* num\_epochs: 90
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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: 100
* num\_epochs: 90
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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: 100
* num\_epochs: 90
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.10.3