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# nick_asr_v2

This model is a fine-tuned version of [ntoldalagi/nick_asr_v2](https://huggingface.co/ntoldalagi/nick_asr_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4562
- Wer: 0.6422
- Cer: 0.2409

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:------:|:---------------:|:------:|
| 0.2616        | 0.44  | 300   | 0.2905 | 1.2200          | 0.7496 |
| 0.441         | 0.87  | 600   | 0.2866 | 1.1936          | 0.7385 |
| 0.4366        | 1.31  | 900   | 0.2795 | 1.1584          | 0.7274 |
| 0.3982        | 1.75  | 1200  | 0.2808 | 1.2033          | 0.7274 |
| 0.3891        | 2.18  | 1500  | 0.2753 | 1.2044          | 0.7166 |
| 0.3508        | 2.91  | 2000  | 1.2382 | 0.7220          | 0.2743 |
| 0.2783        | 4.37  | 3000  | 1.3327 | 0.7177          | 0.2705 |
| 0.2495        | 5.82  | 4000  | 1.2286 | 0.6749          | 0.2638 |
| 0.1982        | 7.28  | 5000  | 1.3073 | 0.6721          | 0.2585 |
| 0.1717        | 8.73  | 6000  | 1.2941 | 0.6627          | 0.2500 |
| 0.1508        | 10.19 | 7000  | 1.3625 | 0.6584          | 0.2490 |
| 0.1329        | 11.64 | 8000  | 1.3863 | 0.6584          | 0.2474 |
| 0.1303        | 13.1  | 9000  | 1.3714 | 0.6534          | 0.2449 |
| 0.1159        | 14.56 | 10000 | 1.4043 | 0.6473          | 0.2442 |
| 0.1015        | 16.01 | 11000 | 1.4245 | 0.6498          | 0.2419 |
| 0.098         | 17.47 | 12000 | 1.4410 | 0.6440          | 0.2425 |
| 0.0869        | 18.92 | 13000 | 1.4562 | 0.6422          | 0.2409 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "model-index": [{"name": "nick_asr_v2", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 

nick\_asr\_v2
=============


This model is a fine-tuned version of ntoldalagi/nick\_asr\_v2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4562
* Wer: 0.6422
* Cer: 0.2409


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: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.1