mlmarian/wav2vec2-xlrs-finetuning

README.md

@@ -1,14 +1,29 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model: facebook/wav2vec2-large-xlsr-53
+base_model: facebook/wav2vec2-xls-r-300m
 tags:
 - generated_from_trainer
 datasets:
 - arrow
+metrics:
+- accuracy
 model-index:
 - name: wav2vec2-xlrs-finetuning
-  results: []
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: arrow
+      type: arrow
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.2802721088435374
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -16,7 +31,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 # wav2vec2-xlrs-finetuning
 
-This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the arrow dataset.
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the arrow dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.6930
+- Accuracy: 0.2803
+- F1 Score: 0.1655
+- Mse: 1.9782
+- Mae: 1.0558
+- Mae^m: 1.5570
 
 ## Model description
 
@@ -36,20 +58,74 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0003
-- train_batch_size: 16
+- train_batch_size: 9
 - eval_batch_size: 8
 - seed: 42
 - gradient_accumulation_steps: 2
-- total_train_batch_size: 32
+- total_train_batch_size: 18
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
 - num_epochs: 30
 - mixed_precision_training: Native AMP
 
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1 Score | Mse    | Mae    | Mae^m  |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:------:|:------:|
+| 1.7525        | 0.6116  | 100  | 1.7058          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.6944        | 1.2232  | 200  | 1.6854          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.7037        | 1.8349  | 300  | 1.7210          | 0.2367   | 0.0479   | 2.0190 | 1.1075 | 2.25   |
+| 1.7667        | 2.4465  | 400  | 1.6868          | 0.3646   | 0.0927   | 2.6707 | 1.1224 | 2.5934 |
+| 1.6859        | 3.0581  | 500  | 1.6833          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.6991        | 3.6697  | 600  | 1.6827          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.6404        | 4.2813  | 700  | 1.7078          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.7088        | 4.8930  | 800  | 1.6962          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.751         | 5.5046  | 900  | 1.6863          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.7813        | 6.1162  | 1000 | 1.6966          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.712         | 6.7278  | 1100 | 1.6783          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.6503        | 7.3394  | 1200 | 1.6816          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.5967        | 7.9511  | 1300 | 1.6588          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.6538        | 8.5627  | 1400 | 1.6229          | 0.3497   | 0.0648   | 3.0952 | 1.2231 | 2.75   |
+| 1.654         | 9.1743  | 1500 | 1.6118          | 0.3633   | 0.0919   | 2.5347 | 1.1061 | 2.2236 |
+| 1.5913        | 9.7859  | 1600 | 1.5914          | 0.3660   | 0.1215   | 2.1537 | 1.0218 | 2.1284 |
+| 1.5896        | 10.3976 | 1700 | 1.6029          | 0.3578   | 0.1333   | 1.9102 | 0.9796 | 1.9420 |
+| 1.5274        | 11.0092 | 1800 | 1.6201          | 0.3769   | 0.1124   | 2.3429 | 1.0558 | 2.1678 |
+| 1.4581        | 11.6208 | 1900 | 1.5739          | 0.3687   | 0.1036   | 2.3918 | 1.0748 | 2.2286 |
+| 1.4           | 12.2324 | 2000 | 1.6100          | 0.3442   | 0.1365   | 2.0395 | 1.0190 | 1.6647 |
+| 1.5475        | 12.8440 | 2100 | 1.5741          | 0.3524   | 0.1431   | 1.6735 | 0.9388 | 1.7980 |
+| 1.4724        | 13.4557 | 2200 | 1.5782          | 0.3660   | 0.1596   | 1.7537 | 0.9347 | 1.6576 |
+| 1.3411        | 14.0673 | 2300 | 1.5875          | 0.3810   | 0.1273   | 2.0503 | 0.9891 | 1.8447 |
+| 1.4884        | 14.6789 | 2400 | 1.5822          | 0.3537   | 0.1388   | 1.9293 | 0.9878 | 1.6427 |
+| 1.4326        | 15.2905 | 2500 | 1.5996          | 0.3456   | 0.1613   | 1.7320 | 0.9565 | 1.5640 |
+| 1.3412        | 15.9021 | 2600 | 1.6870          | 0.2952   | 0.1280   | 2.0231 | 1.0653 | 1.5219 |
+| 1.3139        | 16.5138 | 2700 | 1.6911          | 0.3361   | 0.1439   | 1.7605 | 0.9660 | 1.6466 |
+| 1.1558        | 17.1254 | 2800 | 1.6576          | 0.3469   | 0.1504   | 1.8340 | 0.9714 | 1.6081 |
+| 1.3099        | 17.7370 | 2900 | 1.6184          | 0.3156   | 0.1694   | 1.7075 | 0.9701 | 1.5880 |
+| 1.3763        | 18.3486 | 3000 | 1.7211          | 0.3333   | 0.1505   | 1.7578 | 0.9633 | 1.6180 |
+| 1.1732        | 18.9602 | 3100 | 1.7625          | 0.3401   | 0.1580   | 1.8082 | 0.9728 | 1.6791 |
+| 1.1137        | 19.5719 | 3200 | 1.8241          | 0.3252   | 0.1757   | 1.7946 | 0.9864 | 1.6387 |
+| 1.128         | 20.1835 | 3300 | 1.8824          | 0.3156   | 0.1727   | 1.7823 | 0.9932 | 1.6233 |
+| 1.0219        | 20.7951 | 3400 | 1.9237          | 0.3456   | 0.1630   | 1.7891 | 0.9619 | 1.5458 |
+| 1.0466        | 21.4067 | 3500 | 1.9450          | 0.3238   | 0.1610   | 1.6993 | 0.9619 | 1.6316 |
+| 1.1768        | 22.0183 | 3600 | 2.0642          | 0.3034   | 0.1720   | 1.9946 | 1.0503 | 1.4796 |
+| 1.1656        | 22.6300 | 3700 | 2.2870          | 0.2680   | 0.1696   | 2.1741 | 1.1156 | 1.4731 |
+| 0.8624        | 23.2416 | 3800 | 2.2612          | 0.3102   | 0.1749   | 1.8435 | 1.0    | 1.6763 |
+| 1.0267        | 23.8532 | 3900 | 2.2753          | 0.2939   | 0.1647   | 2.0150 | 1.0599 | 1.5093 |
+| 0.8059        | 24.4648 | 4000 | 2.2790          | 0.3197   | 0.1704   | 1.7714 | 0.9796 | 1.5151 |
+| 0.8924        | 25.0765 | 4100 | 2.3183          | 0.2898   | 0.1755   | 1.9048 | 1.0313 | 1.5539 |
+| 0.9581        | 25.6881 | 4200 | 2.3930          | 0.2803   | 0.1722   | 2.0082 | 1.0667 | 1.5516 |
+| 0.7513        | 26.2997 | 4300 | 2.5025          | 0.2789   | 0.1631   | 2.0367 | 1.0735 | 1.5718 |
+| 0.8617        | 26.9113 | 4400 | 2.5418          | 0.2966   | 0.1686   | 1.9959 | 1.0463 | 1.5909 |
+| 0.7768        | 27.5229 | 4500 | 2.6084          | 0.2898   | 0.1718   | 1.9551 | 1.0463 | 1.5532 |
+| 0.7316        | 28.1346 | 4600 | 2.6581          | 0.2844   | 0.1687   | 1.8803 | 1.0286 | 1.5414 |
+| 0.7436        | 28.7462 | 4700 | 2.6899          | 0.2667   | 0.1611   | 2.0503 | 1.0871 | 1.5357 |
+| 0.8487        | 29.3578 | 4800 | 2.6930          | 0.2803   | 0.1655   | 1.9782 | 1.0558 | 1.5570 |
+
+
 ### Framework versions
 
 - Transformers 4.44.2
-- Pytorch 2.4.0+cu121
+- Pytorch 2.4.1+cu121
 - Datasets 2.21.0
 - Tokenizers 0.19.1

model.safetensors

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runs/Sep10_14-17-19_92815d9860ee/events.out.tfevents.1725977841.92815d9860ee.825.0

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