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# hubert-large-ll60k-librispeech-clean-100h-demo-dist

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1361
- Wer: 0.9769

## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.4699        | 0.15  | 100  | 2.0098          | 1.0    |
| 1.1           | 0.3   | 200  | 1.0910          | 0.9769 |
| 0.6468        | 0.45  | 300  | 1.5089          | 0.9769 |
| 0.6928        | 0.61  | 400  | 1.6171          | 1.0    |
| 0.698         | 0.76  | 500  | 1.2406          | 0.9769 |
| 1.0461        | 0.91  | 600  | 1.3760          | 1.0    |
| 0.6363        | 1.06  | 700  | 2.1654          | 1.0    |
| 0.6743        | 1.21  | 800  | 1.7481          | 0.9769 |
| 0.565         | 1.36  | 900  | 2.1965          | 1.0    |
| 0.5761        | 1.51  | 1000 | 1.8223          | 0.9769 |
| 0.6179        | 1.66  | 1100 | 1.9976          | 0.9769 |
| 0.5052        | 1.82  | 1200 | 1.5585          | 0.9769 |
| 0.5434        | 1.97  | 1300 | 2.0349          | 0.9769 |
| 0.4997        | 2.12  | 1400 | 2.4083          | 0.9769 |
| 0.489         | 2.27  | 1500 | 2.4164          | 0.9769 |
| 0.5104        | 2.42  | 1600 | 2.4970          | 0.9769 |
| 0.5324        | 2.57  | 1700 | 2.3352          | 0.9769 |
| 0.5207        | 2.72  | 1800 | 2.2009          | 0.9769 |
| 0.5224        | 2.87  | 1900 | 2.1035          | 0.9769 |


### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "hubert-large-ll60k-librispeech-clean-100h-demo-dist", "results": []}]}
transformers pytorch tensorboard hubert automatic-speech-recognition librispeech_asr generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

hubert-large-ll60k-librispeech-clean-100h-demo-dist
===================================================


This model is a fine-tuned version of facebook/hubert-base-ls960 on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1361
* Wer: 0.9769


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: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* 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 4.21.0.dev0
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* 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 4.21.0.dev0
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* 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 4.21.0.dev0
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* 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 4.21.0.dev0
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1