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# libri-alpha-0.75-Temp-1-attention-3-layers-distil-with-6-layers-loss-att-take-2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 26.4101
- Wer: 0.2791

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 202.4293      | 0.45  | 200  | 26.7777         | 0.2779 |
| 197.6471      | 0.9   | 400  | 25.8300         | 0.2760 |
| 204.8931      | 1.35  | 600  | 25.6774         | 0.2747 |
| 193.3182      | 1.79  | 800  | 25.6049         | 0.2737 |
| 205.2241      | 2.24  | 1000 | 25.5552         | 0.2739 |
| 186.0407      | 2.69  | 1200 | 25.4364         | 0.2737 |
| 191.7055      | 3.14  | 1400 | 25.7949         | 0.2764 |
| 185.0721      | 3.59  | 1600 | 26.1202         | 0.2753 |
| 198.8579      | 4.04  | 1800 | 25.8496         | 0.2763 |
| 185.7877      | 4.48  | 2000 | 27.0753         | 0.2731 |
| 194.9394      | 4.93  | 2200 | 25.6920         | 0.2775 |
| 188.2296      | 5.38  | 2400 | 25.7362         | 0.2742 |
| 188.0202      | 5.83  | 2600 | 25.9170         | 0.2755 |
| 191.5541      | 6.28  | 2800 | 26.8590         | 0.2771 |
| 198.2817      | 6.73  | 3000 | 26.4101         | 0.2791 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.0
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "libri-alpha-0.75-Temp-1-attention-3-layers-distil-with-6-layers-loss-att-take-2", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 

libri-alpha-0.75-Temp-1-attention-3-layers-distil-with-6-layers-loss-att-take-2
===============================================================================


This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 26.4101
* Wer: 0.2791


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: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 40
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.12.1
* Datasets 2.7.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.12.1
* Datasets 2.7.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.12.1
* Datasets 2.7.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 40
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 1.12.1
* Datasets 2.7.0
* Tokenizers 0.11.0