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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
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