
<!-- 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. -->

# wav2vec2-10epochs-3e3

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: 0.7024
- Wer: 0.6481

## 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: 0.003
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0938        | 0.36  | 100  | 0.4842          | 0.3227 |
| 0.67          | 0.72  | 200  | 0.7219          | 0.5669 |
| 0.7133        | 1.08  | 300  | 1.0698          | 0.7080 |
| 1.0312        | 1.44  | 400  | 1.2692          | 0.8953 |
| 1.2162        | 1.8   | 500  | 1.4763          | 1.0443 |
| 1.2401        | 2.16  | 600  | 1.4906          | 0.8694 |
| 1.2022        | 2.52  | 700  | 1.3686          | 0.9518 |
| 1.154         | 2.88  | 800  | 1.1618          | 0.9109 |
| 1.0467        | 3.24  | 900  | 1.2007          | 0.8602 |
| 1.1785        | 3.6   | 1000 | 1.2000          | 0.9160 |
| 0.979         | 3.96  | 1100 | 1.1464          | 0.8852 |
| 1.1421        | 4.32  | 1200 | 1.1117          | 0.9018 |
| 0.9622        | 4.68  | 1300 | 1.0976          | 0.8602 |
| 1.0939        | 5.04  | 1400 | 1.1126          | 0.8831 |
| 0.9414        | 5.4   | 1500 | 1.0134          | 0.8448 |
| 0.9433        | 5.76  | 1600 | 0.9320          | 0.7977 |
| 0.8389        | 6.12  | 1700 | 0.9013          | 0.7742 |
| 0.8838        | 6.47  | 1800 | 0.9088          | 0.7509 |
| 0.7907        | 6.83  | 1900 | 0.8581          | 0.7382 |
| 0.7704        | 7.19  | 2000 | 0.8300          | 0.7481 |
| 0.667         | 7.55  | 2100 | 0.8221          | 0.7349 |
| 0.6111        | 7.91  | 2200 | 0.7803          | 0.7102 |
| 0.5555        | 8.27  | 2300 | 0.8198          | 0.7314 |
| 0.4947        | 8.63  | 2400 | 0.8127          | 0.7036 |
| 0.4697        | 8.99  | 2500 | 0.7514          | 0.6805 |
| 0.402         | 9.35  | 2600 | 0.7348          | 0.6606 |
| 0.3682        | 9.71  | 2700 | 0.7024          | 0.6481 |


### 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-3e3", "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-3e3
=====================


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: 0.7024
* Wer: 0.6481


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: 0.003
* 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: 0.003
* 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: 0.003
* 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: 0.003
* 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