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---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
base_model: facebook/mms-1b-all
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-nhi-adapterft-orig-ortho_fold1
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: test
args: default
metrics:
- type: wer
value: 0.4133971291866029
name: Wer
---
<!-- 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. -->
# wav2vec2-large-mms-1b-nhi-adapterft-orig-ortho_fold1
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6127
- Wer: 0.4134
- Cer: 0.1235
## 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: 0.001
- train_batch_size: 20
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 1.0469 | 1.6529 | 200 | 0.7880 | 0.6456 | 0.2068 |
| 0.8396 | 3.3058 | 400 | 0.6866 | 0.6118 | 0.1870 |
| 0.7648 | 4.9587 | 600 | 0.6500 | 0.5656 | 0.1752 |
| 0.71 | 6.6116 | 800 | 0.6277 | 0.5477 | 0.1686 |
| 0.6709 | 8.2645 | 1000 | 0.6158 | 0.5349 | 0.1585 |
| 0.6492 | 9.9174 | 1200 | 0.5821 | 0.5033 | 0.1501 |
| 0.6284 | 11.5702 | 1400 | 0.5741 | 0.5145 | 0.1573 |
| 0.6054 | 13.2231 | 1600 | 0.5814 | 0.4912 | 0.1494 |
| 0.5808 | 14.8760 | 1800 | 0.5456 | 0.4896 | 0.1444 |
| 0.5585 | 16.5289 | 2000 | 0.5647 | 0.4884 | 0.1465 |
| 0.5546 | 18.1818 | 2200 | 0.5639 | 0.4890 | 0.1475 |
| 0.5288 | 19.8347 | 2400 | 0.5573 | 0.4903 | 0.1475 |
| 0.5307 | 21.4876 | 2600 | 0.5480 | 0.4657 | 0.1397 |
| 0.5172 | 23.1405 | 2800 | 0.5427 | 0.4622 | 0.1386 |
| 0.5041 | 24.7934 | 3000 | 0.5377 | 0.4549 | 0.1344 |
| 0.4748 | 26.4463 | 3200 | 0.5483 | 0.4635 | 0.1370 |
| 0.4754 | 28.0992 | 3400 | 0.5447 | 0.4699 | 0.1429 |
| 0.4602 | 29.7521 | 3600 | 0.5495 | 0.4523 | 0.1353 |
| 0.4502 | 31.4050 | 3800 | 0.5457 | 0.4329 | 0.1286 |
| 0.4413 | 33.0579 | 4000 | 0.5515 | 0.4501 | 0.1325 |
| 0.4391 | 34.7107 | 4200 | 0.5263 | 0.4545 | 0.1320 |
| 0.4097 | 36.3636 | 4400 | 0.5485 | 0.4574 | 0.1365 |
| 0.4208 | 38.0165 | 4600 | 0.5394 | 0.4542 | 0.1336 |
| 0.4086 | 39.6694 | 4800 | 0.5392 | 0.4357 | 0.1294 |
| 0.3956 | 41.3223 | 5000 | 0.5579 | 0.4332 | 0.1304 |
| 0.4036 | 42.9752 | 5200 | 0.5475 | 0.4376 | 0.1307 |
| 0.3984 | 44.6281 | 5400 | 0.5492 | 0.4297 | 0.1295 |
| 0.3769 | 46.2810 | 5600 | 0.5503 | 0.4348 | 0.1289 |
| 0.3699 | 47.9339 | 5800 | 0.5330 | 0.4357 | 0.1284 |
| 0.3611 | 49.5868 | 6000 | 0.5682 | 0.4380 | 0.1308 |
| 0.3619 | 51.2397 | 6200 | 0.5661 | 0.4316 | 0.1276 |
| 0.3387 | 52.8926 | 6400 | 0.5512 | 0.4287 | 0.1282 |
| 0.3392 | 54.5455 | 6600 | 0.5834 | 0.4351 | 0.1291 |
| 0.3365 | 56.1983 | 6800 | 0.5710 | 0.4335 | 0.1276 |
| 0.3288 | 57.8512 | 7000 | 0.5631 | 0.4262 | 0.1287 |
| 0.3244 | 59.5041 | 7200 | 0.5605 | 0.4281 | 0.1272 |
| 0.3187 | 61.1570 | 7400 | 0.5695 | 0.4332 | 0.1275 |
| 0.3258 | 62.8099 | 7600 | 0.5684 | 0.4265 | 0.1268 |
| 0.3035 | 64.4628 | 7800 | 0.5924 | 0.4185 | 0.1254 |
| 0.3051 | 66.1157 | 8000 | 0.5732 | 0.4319 | 0.1279 |
| 0.2968 | 67.7686 | 8200 | 0.5773 | 0.4204 | 0.1249 |
| 0.2982 | 69.4215 | 8400 | 0.5819 | 0.4140 | 0.1243 |
| 0.297 | 71.0744 | 8600 | 0.5941 | 0.4159 | 0.1240 |
| 0.2922 | 72.7273 | 8800 | 0.5836 | 0.4201 | 0.1229 |
| 0.2798 | 74.3802 | 9000 | 0.5951 | 0.4201 | 0.1243 |
| 0.2692 | 76.0331 | 9200 | 0.5820 | 0.4220 | 0.1255 |
| 0.2704 | 77.6860 | 9400 | 0.5954 | 0.4230 | 0.1251 |
| 0.271 | 79.3388 | 9600 | 0.6022 | 0.4172 | 0.1254 |
| 0.2633 | 80.9917 | 9800 | 0.5975 | 0.4182 | 0.1248 |
| 0.2554 | 82.6446 | 10000 | 0.6114 | 0.4124 | 0.1242 |
| 0.2575 | 84.2975 | 10200 | 0.6084 | 0.4153 | 0.1235 |
| 0.2554 | 85.9504 | 10400 | 0.6007 | 0.4156 | 0.1243 |
| 0.2595 | 87.6033 | 10600 | 0.6010 | 0.4166 | 0.1240 |
| 0.2544 | 89.2562 | 10800 | 0.6080 | 0.4217 | 0.1251 |
| 0.2555 | 90.9091 | 11000 | 0.6076 | 0.4156 | 0.1246 |
| 0.247 | 92.5620 | 11200 | 0.6151 | 0.4150 | 0.1239 |
| 0.2465 | 94.2149 | 11400 | 0.6113 | 0.4121 | 0.1240 |
| 0.2376 | 95.8678 | 11600 | 0.6136 | 0.4153 | 0.1237 |
| 0.2464 | 97.5207 | 11800 | 0.6121 | 0.4137 | 0.1235 |
| 0.2433 | 99.1736 | 12000 | 0.6127 | 0.4134 | 0.1235 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.4.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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