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# Speech3

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: 2.9613
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 2.9722        | 0.19  | 100  | 3.0022          | 1   |
| 2.9665        | 0.38  | 200  | 2.9838          | 1   |
| 2.9826        | 0.56  | 300  | 2.9780          | 1   |
| 2.975         | 0.75  | 400  | 3.0293          | 1   |
| 3.0458        | 0.94  | 500  | 3.0058          | 1   |
| 2.9819        | 1.13  | 600  | 3.0088          | 1   |
| 3.0267        | 1.31  | 700  | 3.0086          | 1   |
| 2.945         | 1.5   | 800  | 2.9810          | 1   |
| 2.9501        | 1.69  | 900  | 2.9702          | 1   |
| 3.0021        | 1.88  | 1000 | 2.9810          | 1   |
| 2.9721        | 2.06  | 1100 | 3.0104          | 1   |
| 2.9669        | 2.25  | 1200 | 3.0150          | 1   |
| 2.9694        | 2.44  | 1300 | 2.9685          | 1   |
| 2.968         | 2.63  | 1400 | 3.0010          | 1   |
| 2.9604        | 2.81  | 1500 | 2.9753          | 1   |
| 3.0178        | 3.0   | 1600 | 3.2719          | 1   |
| 2.9699        | 3.19  | 1700 | 2.9617          | 1   |
| 2.9871        | 3.38  | 1800 | 2.9613          | 1   |
| 2.9551        | 3.56  | 1900 | 2.9946          | 1   |
| 2.9597        | 3.75  | 2000 | 2.9652          | 1   |
| 2.9573        | 3.94  | 2100 | 3.1862          | 1   |
| 2.9652        | 4.13  | 2200 | 3.0658          | 1   |
| 2.9627        | 4.32  | 2300 | 3.2517          | 1   |
| 2.9382        | 4.5   | 2400 | 2.9681          | 1   |
| 2.9273        | 4.69  | 2500 | 2.9715          | 1   |
| 2.9459        | 4.88  | 2600 | 3.0109          | 1   |
| 3.0296        | 5.07  | 2700 | 2.9807          | 1   |
| 3.0574        | 5.25  | 2800 | 2.9737          | 1   |
| 3.0118        | 5.44  | 2900 | 2.9675          | 1   |
| 2.9799        | 5.63  | 3000 | 2.9804          | 1   |
| 2.9839        | 5.82  | 3100 | 3.0447          | 1   |
| 2.9565        | 6.0   | 3200 | 2.9867          | 1   |
| 2.9251        | 6.19  | 3300 | 2.9778          | 1   |
| 2.9881        | 6.38  | 3400 | 3.2389          | 1   |
| 2.9388        | 6.57  | 3500 | 2.9655          | 1   |
| 2.9832        | 6.75  | 3600 | 3.0652          | 1   |
| 2.9741        | 6.94  | 3700 | 2.9748          | 1   |
| 2.9425        | 7.13  | 3800 | 2.9657          | 1   |
| 2.9116        | 7.32  | 3900 | 2.9609          | 1   |
| 2.9413        | 7.5   | 4000 | 2.9629          | 1   |
| 2.8981        | 7.69  | 4100 | 2.9748          | 1   |
| 2.9369        | 7.88  | 4200 | 2.9799          | 1   |
| 2.9941        | 8.07  | 4300 | 2.9553          | 1   |
| 2.97          | 8.26  | 4400 | 2.9607          | 1   |
| 2.9205        | 8.44  | 4500 | 2.9552          | 1   |
| 2.9585        | 8.63  | 4600 | 2.9680          | 1   |
| 2.9763        | 8.82  | 4700 | 2.9646          | 1   |
| 2.9206        | 9.01  | 4800 | 2.9615          | 1   |
| 2.9236        | 9.19  | 4900 | 2.9655          | 1   |
| 2.8985        | 9.38  | 5000 | 2.9613          | 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": "Speech3", "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 

Speech3
=======


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: 2.9613
* 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