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# wav2vec2-10epochs-3e5

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3076
- Wer: 0.2157

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1184        | 0.36  | 100  | 0.4169          | 0.3008 |
| 0.0747        | 0.72  | 200  | 0.3413          | 0.2677 |
| 0.046         | 1.08  | 300  | 0.3245          | 0.2496 |
| 0.0263        | 1.44  | 400  | 0.3094          | 0.2450 |
| 0.0326        | 1.8   | 500  | 0.3031          | 0.2366 |
| 0.0179        | 2.16  | 600  | 0.2966          | 0.2324 |
| 0.0209        | 2.52  | 700  | 0.2920          | 0.2287 |
| 0.0095        | 2.88  | 800  | 0.2966          | 0.2267 |
| 0.0113        | 3.24  | 900  | 0.2997          | 0.2259 |
| 0.0107        | 3.6   | 1000 | 0.3011          | 0.2221 |
| 0.009         | 3.96  | 1100 | 0.3050          | 0.2219 |
| 0.0131        | 4.32  | 1200 | 0.3034          | 0.2195 |
| 0.0147        | 4.68  | 1300 | 0.3042          | 0.2204 |
| 0.0195        | 5.04  | 1400 | 0.3187          | 0.2204 |
| 0.0087        | 5.4   | 1500 | 0.3205          | 0.2198 |
| 0.0083        | 5.76  | 1600 | 0.3177          | 0.2197 |
| 0.016         | 6.12  | 1700 | 0.3110          | 0.2175 |
| 0.0152        | 6.47  | 1800 | 0.3078          | 0.2171 |
| 0.0173        | 6.83  | 1900 | 0.3134          | 0.2169 |
| 0.046         | 7.19  | 2000 | 0.3134          | 0.2164 |
| 0.0264        | 7.55  | 2100 | 0.3095          | 0.2174 |
| 0.0143        | 7.91  | 2200 | 0.3066          | 0.2170 |
| 0.0186        | 8.27  | 2300 | 0.3086          | 0.2181 |
| 0.0308        | 8.63  | 2400 | 0.3103          | 0.2175 |
| 0.0246        | 8.99  | 2500 | 0.3083          | 0.2157 |
| 0.0126        | 9.35  | 2600 | 0.3073          | 0.2154 |
| 0.0134        | 9.71  | 2700 | 0.3076          | 0.2157 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 2.9.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-10epochs-3e5", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-10epochs-3e5
=====================


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3076
* Wer: 0.2157


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: 3e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.1
* Datasets 2.9.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.1
* Datasets 2.9.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.1
* Datasets 2.9.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.1
* Datasets 2.9.0
* Tokenizers 0.10.3