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# assis

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8484
- Wer: 1

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 22.3611       | 0.45  | 100  | 25.5854         | 1   |
| 20.8274       | 0.9   | 200  | 20.4977         | 1   |
| 12.1089       | 1.35  | 300  | 11.0220         | 1   |
| 5.043         | 1.81  | 400  | 4.3838          | 1   |
| 3.788         | 2.26  | 500  | 3.5831          | 1   |
| 3.445         | 2.71  | 600  | 3.4112          | 1   |
| 3.3042        | 3.16  | 700  | 3.3104          | 1   |
| 3.221         | 3.61  | 800  | 3.2255          | 1   |
| 3.1628        | 4.06  | 900  | 3.1618          | 1   |
| 3.0645        | 4.51  | 1000 | 3.1010          | 1   |
| 3.0913        | 4.97  | 1100 | 3.0624          | 1   |
| 3.0819        | 5.42  | 1200 | 3.0136          | 1   |
| 2.9502        | 5.87  | 1300 | 2.9883          | 1   |
| 2.9611        | 6.32  | 1400 | 2.9651          | 1   |
| 2.9287        | 6.77  | 1500 | 2.9474          | 1   |
| 2.9461        | 7.22  | 1600 | 2.9280          | 1   |
| 2.9176        | 7.67  | 1700 | 2.9148          | 1   |
| 2.8986        | 8.13  | 1800 | 2.9138          | 1   |
| 2.8896        | 8.58  | 1900 | 2.9050          | 1   |
| 2.8879        | 9.03  | 2000 | 2.9093          | 1   |
| 2.9085        | 9.48  | 2100 | 2.8998          | 1   |
| 2.876         | 9.93  | 2200 | 2.8807          | 1   |
| 2.8649        | 10.38 | 2300 | 2.8734          | 1   |
| 2.8653        | 10.84 | 2400 | 2.8681          | 1   |
| 2.8683        | 11.29 | 2500 | 2.8596          | 1   |
| 2.8452        | 11.74 | 2600 | 2.8667          | 1   |
| 2.8468        | 12.19 | 2700 | 2.8514          | 1   |
| 2.846         | 12.64 | 2800 | 2.8541          | 1   |
| 2.8415        | 13.09 | 2900 | 2.8493          | 1   |
| 2.8195        | 13.54 | 3000 | 2.8472          | 1   |
| 2.8103        | 14.0  | 3100 | 2.8244          | 1   |
| 2.6495        | 14.45 | 3200 | 2.5809          | 1   |
| 2.3126        | 14.9  | 3300 | 2.1612          | 1   |
| 1.92          | 15.35 | 3400 | 1.7312          | 1   |
| 1.5734        | 15.8  | 3500 | 1.4245          | 1   |
| 1.4081        | 16.25 | 3600 | 1.2659          | 1   |
| 1.2573        | 16.7  | 3700 | 1.1694          | 1   |
| 1.194         | 17.16 | 3800 | 1.0930          | 1   |
| 1.1053        | 17.61 | 3900 | 1.0393          | 1   |
| 1.072         | 18.06 | 4000 | 0.9792          | 1   |
| 1.0148        | 18.51 | 4100 | 0.9468          | 1   |
| 0.9995        | 18.96 | 4200 | 0.9228          | 1   |
| 0.9688        | 19.41 | 4300 | 0.9071          | 1   |
| 0.956         | 19.86 | 4400 | 0.8950          | 1   |
| 0.9565        | 20.32 | 4500 | 0.8632          | 1   |
| 0.9215        | 20.77 | 4600 | 0.8673          | 1   |
| 0.9006        | 21.22 | 4700 | 0.8647          | 1   |
| 0.8645        | 21.67 | 4800 | 0.8566          | 1   |
| 0.8768        | 22.12 | 4900 | 0.8527          | 1   |
| 0.8809        | 22.57 | 5000 | 0.8484          | 1   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "assis", "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 

assis
=====


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


* Loss: 0.8484
* Wer: 1


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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 3000
* training\_steps: 5000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 3000
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 3000
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3