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# hubert-base-libri-clean-ft100h

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1324
- Wer: 0.1597

## 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: 8
- eval_batch_size: 16
- 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: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.14  | 250  | 4.1508          | 1.0000 |
| 4.4345        | 0.28  | 500  | 3.8766          | 1.0000 |
| 4.4345        | 0.42  | 750  | 3.4376          | 1.0000 |
| 2.8475        | 0.56  | 1000 | 2.7380          | 1.0    |
| 2.8475        | 0.7   | 1250 | 0.8803          | 0.6766 |
| 1.1877        | 0.84  | 1500 | 0.5671          | 0.5102 |
| 1.1877        | 0.98  | 1750 | 0.4537          | 0.4388 |
| 0.5802        | 1.12  | 2000 | 0.3566          | 0.3740 |
| 0.5802        | 1.26  | 2250 | 0.2925          | 0.3209 |
| 0.4301        | 1.4   | 2500 | 0.2613          | 0.2952 |
| 0.4301        | 1.54  | 2750 | 0.2363          | 0.2715 |
| 0.3591        | 1.68  | 3000 | 0.2155          | 0.2552 |
| 0.3591        | 1.82  | 3250 | 0.2062          | 0.2418 |
| 0.3015        | 1.96  | 3500 | 0.1951          | 0.2308 |
| 0.3015        | 2.1   | 3750 | 0.1842          | 0.2207 |
| 0.2698        | 2.24  | 4000 | 0.1900          | 0.2112 |
| 0.2698        | 2.38  | 4250 | 0.1745          | 0.2048 |
| 0.2561        | 2.52  | 4500 | 0.1718          | 0.2040 |
| 0.2561        | 2.66  | 4750 | 0.1625          | 0.1939 |
| 0.2348        | 2.8   | 5000 | 0.1568          | 0.1867 |
| 0.2348        | 2.94  | 5250 | 0.1517          | 0.1855 |
| 0.2278        | 3.08  | 5500 | 0.1501          | 0.1807 |
| 0.2278        | 3.22  | 5750 | 0.1445          | 0.1772 |
| 0.2166        | 3.36  | 6000 | 0.1422          | 0.1752 |
| 0.2166        | 3.5   | 6250 | 0.1418          | 0.1741 |
| 0.2017        | 3.64  | 6500 | 0.1404          | 0.1695 |
| 0.2017        | 3.78  | 6750 | 0.1356          | 0.1674 |
| 0.1922        | 3.92  | 7000 | 0.1350          | 0.1688 |
| 0.1922        | 4.06  | 7250 | 0.1346          | 0.1638 |
| 0.1979        | 4.2   | 7500 | 0.1359          | 0.1638 |
| 0.1979        | 4.34  | 7750 | 0.1336          | 0.1612 |
| 0.1836        | 4.48  | 8000 | 0.1324          | 0.1613 |
| 0.1836        | 4.62  | 8250 | 0.1320          | 0.1606 |
| 0.1891        | 4.76  | 8500 | 0.1325          | 0.1598 |
| 0.1891        | 4.9   | 8750 | 0.1324          | 0.1597 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "hubert-base-libri-clean-ft100h", "results": []}]}
transformers pytorch tensorboard hubert automatic-speech-recognition generated_from_trainer dataset:librispeech_asr license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us 

hubert-base-libri-clean-ft100h
==============================


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


* Loss: 0.1324
* Wer: 0.1597


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: 8
* eval\_batch\_size: 16
* 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: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* 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: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* 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: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* 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: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1