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---
library_name: transformers
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Hubert-base-ASR-eu
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-ASR-eu
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1922
- Wer: 0.3154
- Cer: 0.0583
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 5.5577 | 0.1652 | 1000 | 3.5579 | 0.9999 | 0.9883 |
| 3.3467 | 0.3304 | 2000 | 2.9818 | 0.9999 | 0.9883 |
| 2.934 | 0.4955 | 3000 | 2.8354 | 0.9999 | 0.9883 |
| 2.8012 | 0.6607 | 4000 | 2.8179 | 0.9999 | 0.9883 |
| 2.7896 | 0.8259 | 5000 | 2.8299 | 0.9999 | 0.9883 |
| 1.4043 | 0.9911 | 6000 | 0.9904 | 0.9859 | 0.2788 |
| 0.903 | 1.1563 | 7000 | 0.5862 | 0.8110 | 0.1676 |
| 0.6955 | 1.3214 | 8000 | 0.4828 | 0.7192 | 0.1411 |
| 0.5764 | 1.4866 | 9000 | 0.4330 | 0.6535 | 0.1257 |
| 0.5195 | 1.6518 | 10000 | 0.3948 | 0.6083 | 0.1151 |
| 0.4949 | 1.8170 | 11000 | 0.3698 | 0.5676 | 0.1073 |
| 0.4481 | 1.9822 | 12000 | 0.3475 | 0.5343 | 0.1007 |
| 0.4251 | 2.1473 | 13000 | 0.3286 | 0.5162 | 0.0962 |
| 0.4257 | 2.3125 | 14000 | 0.3139 | 0.4933 | 0.0918 |
| 0.3982 | 2.4777 | 15000 | 0.3016 | 0.4780 | 0.0890 |
| 0.3672 | 2.6429 | 16000 | 0.2925 | 0.4616 | 0.0852 |
| 0.3741 | 2.8081 | 17000 | 0.2823 | 0.4504 | 0.0830 |
| 0.3991 | 2.9732 | 18000 | 0.2748 | 0.4372 | 0.0804 |
| 0.3624 | 3.1384 | 19000 | 0.2684 | 0.4293 | 0.0786 |
| 0.3362 | 3.3036 | 20000 | 0.2638 | 0.4180 | 0.0772 |
| 0.3209 | 3.4688 | 21000 | 0.2577 | 0.4072 | 0.0752 |
| 0.314 | 3.6340 | 22000 | 0.2498 | 0.4042 | 0.0744 |
| 0.3066 | 3.7991 | 23000 | 0.2452 | 0.3957 | 0.0730 |
| 0.3206 | 3.9643 | 24000 | 0.2466 | 0.3877 | 0.0717 |
| 0.3009 | 4.1295 | 25000 | 0.2396 | 0.3826 | 0.0708 |
| 0.3803 | 4.2947 | 26000 | 0.2391 | 0.3814 | 0.0705 |
| 0.2932 | 4.4599 | 27000 | 0.2334 | 0.3748 | 0.0692 |
| 0.2825 | 4.6250 | 28000 | 0.2292 | 0.3679 | 0.0679 |
| 0.2882 | 4.7902 | 29000 | 0.2253 | 0.3655 | 0.0675 |
| 0.2792 | 4.9554 | 30000 | 0.2233 | 0.3612 | 0.0667 |
| 0.2829 | 5.1206 | 31000 | 0.2224 | 0.3584 | 0.0664 |
| 0.2778 | 5.2858 | 32000 | 0.2221 | 0.3524 | 0.0652 |
| 0.2736 | 5.4509 | 33000 | 0.2160 | 0.3516 | 0.0647 |
| 0.2999 | 5.6161 | 34000 | 0.2156 | 0.3483 | 0.0646 |
| 0.2586 | 5.7813 | 35000 | 0.2149 | 0.3445 | 0.0634 |
| 0.2704 | 5.9465 | 36000 | 0.2132 | 0.3452 | 0.0635 |
| 0.2607 | 6.1117 | 37000 | 0.2112 | 0.3377 | 0.0626 |
| 0.2577 | 6.2768 | 38000 | 0.2074 | 0.3366 | 0.0620 |
| 0.2477 | 6.4420 | 39000 | 0.2064 | 0.3355 | 0.0617 |
| 0.2611 | 6.6072 | 40000 | 0.2062 | 0.3339 | 0.0615 |
| 0.2428 | 6.7724 | 41000 | 0.2044 | 0.3316 | 0.0611 |
| 0.246 | 6.9376 | 42000 | 0.2028 | 0.3314 | 0.0609 |
| 0.2664 | 7.1027 | 43000 | 0.2006 | 0.3295 | 0.0605 |
| 0.25 | 7.2679 | 44000 | 0.2027 | 0.3263 | 0.0601 |
| 0.2458 | 7.4331 | 45000 | 0.2009 | 0.3241 | 0.0599 |
| 0.2446 | 7.5983 | 46000 | 0.1995 | 0.3238 | 0.0598 |
| 0.2377 | 7.7635 | 47000 | 0.1997 | 0.3226 | 0.0594 |
| 0.2428 | 7.9286 | 48000 | 0.1981 | 0.3210 | 0.0592 |
| 0.2426 | 8.0938 | 49000 | 0.1964 | 0.3202 | 0.0591 |
| 0.253 | 8.2590 | 50000 | 0.1950 | 0.3191 | 0.0590 |
| 0.2635 | 8.4242 | 51000 | 0.1943 | 0.3191 | 0.0590 |
| 0.2789 | 8.5894 | 52000 | 0.1940 | 0.3175 | 0.0589 |
| 0.2608 | 8.7545 | 53000 | 0.1957 | 0.3173 | 0.0587 |
| 0.2231 | 8.9197 | 54000 | 0.1932 | 0.3167 | 0.0584 |
| 0.2406 | 9.0849 | 55000 | 0.1933 | 0.3164 | 0.0586 |
| 0.2347 | 9.2501 | 56000 | 0.1915 | 0.3161 | 0.0585 |
| 0.2502 | 9.4153 | 57000 | 0.1935 | 0.3153 | 0.0583 |
| 0.2353 | 9.5804 | 58000 | 0.1926 | 0.3154 | 0.0583 |
| 0.2358 | 9.7456 | 59000 | 0.1918 | 0.3156 | 0.0582 |
| 0.2395 | 9.9108 | 60000 | 0.1922 | 0.3154 | 0.0583 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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