🚀 Whisper Large Northern Sámi
This model is a fine - tuned version of [openai/whisper - large - v2](https://huggingface.co/openai/whisper - large - v2) on the audiofolder dataset. It offers high - quality automatic speech recognition for Northern Sámi, achieving notable results on the evaluation set.
🚀 Quick Start
This section is not provided in the original README, so it is skipped.
✨ Features
This model is a fine - tuned version of [openai/whisper - large - v2](https://huggingface.co/openai/whisper - large - v2) on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5559
- Wer: 24.9143
📚 Documentation
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: 12
- eval_batch_size: 6
- seed: 42
- distributed_type: multi - GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 60000
- mixed_precision_training: Native AMP
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
0.4665 |
58.0 |
1000 |
0.8572 |
54.5143 |
0.3041 |
117.0 |
2000 |
0.6711 |
44.1143 |
0.2671 |
176.0 |
3000 |
0.5794 |
39.7714 |
0.1761 |
235.0 |
4000 |
0.5357 |
35.0857 |
0.2089 |
294.0 |
5000 |
0.5094 |
33.6 |
0.1456 |
352.0 |
6000 |
0.4959 |
33.0286 |
0.1514 |
411.0 |
7000 |
0.4864 |
32.5714 |
0.1203 |
470.0 |
8000 |
0.4625 |
31.4286 |
0.0879 |
529.0 |
9000 |
0.4916 |
45.4857 |
0.0825 |
588.0 |
10000 |
0.4962 |
30.6286 |
0.0753 |
647.0 |
11000 |
0.4723 |
31.2 |
0.0812 |
705.0 |
12000 |
0.4574 |
28.6857 |
0.062 |
764.0 |
13000 |
0.4628 |
28.8000 |
0.0604 |
823.0 |
14000 |
0.4668 |
28.0000 |
0.0666 |
882.0 |
15000 |
0.4697 |
28.6857 |
0.0405 |
941.0 |
16000 |
0.4908 |
54.6286 |
0.0349 |
999.0 |
17000 |
0.4728 |
28.4571 |
0.0409 |
1058.0 |
18000 |
0.4884 |
28.4571 |
0.0292 |
1117.0 |
19000 |
0.4576 |
27.3143 |
0.0247 |
1176.0 |
20000 |
0.4734 |
28.9143 |
0.0229 |
1235.0 |
21000 |
0.4899 |
29.9429 |
0.0271 |
1294.0 |
22000 |
0.4790 |
28.1143 |
0.0271 |
1352.0 |
23000 |
0.5012 |
30.1714 |
0.0184 |
1411.0 |
24000 |
0.5008 |
27.3143 |
0.0211 |
1470.0 |
25000 |
0.5118 |
27.6571 |
0.0183 |
1529.0 |
26000 |
0.5398 |
30.0571 |
0.0164 |
1588.0 |
27000 |
0.5006 |
27.3143 |
0.0169 |
1647.0 |
28000 |
0.5059 |
27.0857 |
0.0147 |
1705.0 |
29000 |
0.5325 |
27.7714 |
0.0104 |
1764.0 |
30000 |
0.4818 |
26.1714 |
0.0128 |
1823.0 |
31000 |
0.5259 |
28.3429 |
0.0145 |
1882.0 |
32000 |
0.5299 |
26.2857 |
0.0075 |
1941.0 |
33000 |
0.5082 |
27.4286 |
0.0087 |
1999.0 |
34000 |
0.5144 |
26.6286 |
0.005 |
2058.0 |
35000 |
0.5590 |
27.0857 |
0.0099 |
2117.0 |
36000 |
0.5546 |
28.9143 |
0.007 |
2176.0 |
37000 |
0.5364 |
26.8571 |
0.0045 |
2235.0 |
38000 |
0.5574 |
27.2000 |
0.0064 |
2294.0 |
39000 |
0.5051 |
25.7143 |
0.0079 |
2352.0 |
40000 |
0.5247 |
25.9429 |
0.0083 |
2411.0 |
41000 |
0.5514 |
25.6 |
0.0101 |
2470.0 |
42000 |
0.5710 |
25.6 |
0.0062 |
2529.0 |
43000 |
0.5830 |
28.0000 |
0.0046 |
2588.0 |
44000 |
0.5828 |
26.8571 |
0.0053 |
2647.0 |
45000 |
0.5621 |
27.4286 |
0.0047 |
2705.0 |
46000 |
0.5673 |
25.9429 |
0.0045 |
2764.0 |
47000 |
0.5220 |
25.6 |
0.0065 |
2823.0 |
48000 |
0.5704 |
27.7714 |
0.0039 |
2882.0 |
49000 |
0.5741 |
27.7714 |
0.0027 |
2941.0 |
50000 |
0.5762 |
26.0571 |
0.0019 |
2999.0 |
51000 |
0.5559 |
24.9143 |
0.0015 |
3058.0 |
52000 |
0.5777 |
28.5714 |
0.0026 |
3117.0 |
53000 |
0.5589 |
25.2571 |
0.0032 |
3176.0 |
54000 |
0.6061 |
26.9714 |
0.0025 |
3235.0 |
55000 |
0.5776 |
25.1429 |
0.0046 |
3294.0 |
56000 |
0.5753 |
27.3143 |
0.0015 |
3352.0 |
57000 |
0.5736 |
27.2000 |
0.003 |
3411.0 |
58000 |
0.5933 |
25.6 |
0.002 |
3470.0 |
59000 |
0.6036 |
25.6 |
0.0007 |
58.0 |
60000 |
0.5975 |
25.2571 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.11.0
📄 License
The model is licensed under the Apache - 2.0 license.