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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: hubert-base-libri-clean-ft100h
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-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
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