File size: 2,952 Bytes
1eb8bda |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: cc-by-nc-4.0
base_model: facebook/wav2vec2-large-uralic-voxpopuli-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-large-uralic-voxpopuli-v2-karelian-cs-w-rus
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. -->
# w2v2-large-uralic-voxpopuli-v2-karelian-cs-w-rus
This model is a fine-tuned version of [facebook/wav2vec2-large-uralic-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-large-uralic-voxpopuli-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5997
- Wer: 0.4401
- Cer: 0.1228
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 43
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 6.2662 | 0.48 | 500 | 1.8324 | 0.9998 | 0.6007 |
| 1.032 | 0.97 | 1000 | 0.7219 | 0.6129 | 0.1779 |
| 0.6635 | 1.45 | 1500 | 0.6393 | 0.5535 | 0.1572 |
| 0.5568 | 1.94 | 2000 | 0.5816 | 0.5192 | 0.1486 |
| 0.4728 | 2.42 | 2500 | 0.5838 | 0.5076 | 0.1444 |
| 0.427 | 2.9 | 3000 | 0.5746 | 0.4915 | 0.1377 |
| 0.3789 | 3.39 | 3500 | 0.5658 | 0.4781 | 0.1342 |
| 0.3543 | 3.87 | 4000 | 0.5859 | 0.4737 | 0.1349 |
| 0.3223 | 4.35 | 4500 | 0.5678 | 0.4708 | 0.1327 |
| 0.3053 | 4.84 | 5000 | 0.5572 | 0.4652 | 0.1311 |
| 0.2852 | 5.32 | 5500 | 0.5768 | 0.4594 | 0.1285 |
| 0.2663 | 5.81 | 6000 | 0.5687 | 0.4594 | 0.1282 |
| 0.2585 | 6.29 | 6500 | 0.5891 | 0.4572 | 0.1280 |
| 0.2477 | 6.77 | 7000 | 0.5930 | 0.4524 | 0.1257 |
| 0.2341 | 7.26 | 7500 | 0.5841 | 0.4456 | 0.1244 |
| 0.2325 | 7.74 | 8000 | 0.5973 | 0.4460 | 0.1242 |
| 0.2195 | 8.22 | 8500 | 0.6069 | 0.4403 | 0.1234 |
| 0.2187 | 8.71 | 9000 | 0.5899 | 0.4390 | 0.1229 |
| 0.2134 | 9.19 | 9500 | 0.5944 | 0.4398 | 0.1231 |
| 0.2111 | 9.68 | 10000 | 0.5997 | 0.4401 | 0.1228 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.5.1
- Datasets 2.15.0
- Tokenizers 0.13.3
|