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# 

This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 12.2365
- Wer: 1.0

## 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: 0.0001
- 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: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 6.5774        | 0.28  | 500  | 10.5449         | 1.0 |
| 6.706         | 0.56  | 1000 | 9.4411          | 1.0 |
| 6.9182        | 0.84  | 1500 | 10.9554         | 1.0 |
| 6.7416        | 1.12  | 2000 | 10.0801         | 1.0 |
| 6.8778        | 1.4   | 2500 | 9.8569          | 1.0 |
| 6.7694        | 1.68  | 3000 | 10.4234         | 1.0 |
| 6.7415        | 1.96  | 3500 | 10.6545         | 1.0 |
| 6.5997        | 2.24  | 4000 | 10.4268         | 1.0 |
| 6.7672        | 2.52  | 4500 | 11.1929         | 1.0 |
| 6.5254        | 2.8   | 5000 | 12.2365         | 1.0 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]}
transformers pytorch tensorboard speech-encoder-decoder automatic-speech-recognition generated_from_trainer dataset:librispeech_asr endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us 


This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:


* Loss: 12.2365
* Wer: 1.0


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: 0.0001
* 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: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 500
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0