---
library_name: transformers
license: apache-2.0
base_model: facebook/hubert-large-ls960-ft
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-large-timit-upsample-decoder
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hubert-large-timit-upsample-decoder

This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4975
- Wer: 0.9749

## 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: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 83.048        | 2.4752  | 500  | 48.3229         | 0.9459 |
| 2.1777        | 4.9505  | 1000 | 3.3841          | 0.9775 |
| 2.5735        | 7.4257  | 1500 | 2.1042          | 0.9698 |
| 5.3683        | 9.9010  | 2000 | 1.5244          | 0.9699 |
| 1.906         | 12.3762 | 2500 | 1.3064          | 0.9128 |
| 1.9468        | 14.8515 | 3000 | 1.3597          | 0.9174 |
| 1.6598        | 17.3267 | 3500 | 1.1801          | 0.9093 |
| 1.2808        | 19.8020 | 4000 | 1.6481          | 0.9181 |
| 2.0953        | 22.2772 | 4500 | 3.1021          | 0.9602 |
| 0.5282        | 24.7525 | 5000 | 0.5278          | 0.9755 |
| 7.1607        | 27.2277 | 5500 | 0.9557          | 0.9823 |
| 4.1975        | 29.7030 | 6000 | 13.0365         | 0.9301 |
| 0.5248        | 32.1782 | 6500 | 0.5075          | 0.9840 |
| 0.5065        | 34.6535 | 7000 | 0.5001          | 0.9834 |
| 0.4997        | 37.1287 | 7500 | 0.5032          | 0.9793 |
| 0.5072        | 39.6040 | 8000 | 0.4975          | 0.9749 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.2.1
- Datasets 3.6.0
- Tokenizers 0.21.1