wav2vec2-large-mms-1b-nhi-adapterft-orig-ortho_fold1

README.md

@@ -1,199 +1,137 @@
 ---
-library_name: transformers
-tags: []
+license: cc-by-nc-4.0
+base_model: facebook/mms-1b-all
+tags:
+- generated_from_trainer
+datasets:
+- audiofolder
+metrics:
+- wer
+model-index:
+- name: wav2vec2-large-mms-1b-nhi-adapterft-orig-ortho_fold1
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: audiofolder
+      type: audiofolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.4133971291866029
 ---
 
-# Model Card for Model ID
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+<!-- 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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