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# Speech5

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: 2.9659
- 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: 0.01
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 2.9422        | 0.19  | 100  | 2.9742          | 1   |
| 3.006         | 0.38  | 200  | 2.9864          | 1   |
| 2.9665        | 0.56  | 300  | 2.9967          | 1   |
| 2.9411        | 0.75  | 400  | 2.9926          | 1   |
| 2.9937        | 0.94  | 500  | 3.0086          | 1   |
| 2.9476        | 1.13  | 600  | 3.0102          | 1   |
| 3.3385        | 1.31  | 700  | 2.9926          | 1   |
| 2.9979        | 1.5   | 800  | 2.9922          | 1   |
| 2.9534        | 1.69  | 900  | 3.0587          | 1   |
| 2.9544        | 1.88  | 1000 | 2.9880          | 1   |
| 2.9607        | 2.06  | 1100 | 2.9828          | 1   |
| 2.9926        | 2.25  | 1200 | 3.0249          | 1   |
| 2.9731        | 2.44  | 1300 | 2.9755          | 1   |
| 2.9438        | 2.63  | 1400 | 2.9707          | 1   |
| 2.9688        | 2.81  | 1500 | 3.0368          | 1   |
| 2.9335        | 3.0   | 1600 | 2.9695          | 1   |
| 2.9479        | 3.19  | 1700 | 3.0050          | 1   |
| 3.0182        | 3.38  | 1800 | 3.2477          | 1   |
| 2.9549        | 3.56  | 1900 | 2.9778          | 1   |
| 2.9212        | 3.75  | 2000 | 2.9697          | 1   |
| 2.9718        | 3.94  | 2100 | 2.9677          | 1   |
| 2.9288        | 4.13  | 2200 | 3.0418          | 1   |
| 2.9838        | 4.32  | 2300 | 2.9823          | 1   |
| 2.9812        | 4.5   | 2400 | 2.9902          | 1   |
| 2.9766        | 4.69  | 2500 | 2.9719          | 1   |
| 2.9479        | 4.88  | 2600 | 2.9805          | 1   |
| 2.9462        | 5.07  | 2700 | 3.1010          | 1   |
| 2.9998        | 5.25  | 2800 | 3.0566          | 1   |
| 2.9451        | 5.44  | 2900 | 3.1167          | 1   |
| 3.0465        | 5.63  | 3000 | 3.1573          | 1   |
| 2.9672        | 5.82  | 3100 | 2.9748          | 1   |
| 2.9555        | 6.0   | 3200 | 2.9929          | 1   |
| 2.9354        | 6.19  | 3300 | 2.9743          | 1   |
| 2.9281        | 6.38  | 3400 | 2.9746          | 1   |
| 3.0183        | 6.57  | 3500 | 2.9809          | 1   |
| 2.9602        | 6.75  | 3600 | 2.9662          | 1   |
| 2.9344        | 6.94  | 3700 | 3.0192          | 1   |
| 2.9748        | 7.13  | 3800 | 2.9653          | 1   |
| 3.0283        | 7.32  | 3900 | 3.0159          | 1   |
| 2.9367        | 7.5   | 4000 | 2.9780          | 1   |
| 2.9534        | 7.69  | 4100 | 2.9890          | 1   |
| 2.9481        | 7.88  | 4200 | 2.9701          | 1   |
| 2.89          | 8.07  | 4300 | 2.9625          | 1   |
| 2.9153        | 8.26  | 4400 | 2.9650          | 1   |
| 2.9648        | 8.44  | 4500 | 2.9726          | 1   |
| 2.9245        | 8.63  | 4600 | 3.0114          | 1   |
| 2.9608        | 8.82  | 4700 | 2.9645          | 1   |
| 2.9074        | 9.01  | 4800 | 2.9613          | 1   |
| 2.9059        | 9.19  | 4900 | 2.9639          | 1   |
| 2.9538        | 9.38  | 5000 | 2.9659          | 1   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "Speech5", "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 

Speech5
=======


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: 2.9659
* 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: 0.01
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.01
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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+cu118
* 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: 0.01
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3