susmitabhatt
/

xlsr-big-kcnnn

@@ -1,199 +1,141 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+license: apache-2.0
+base_model: facebook/wav2vec2-large-xlsr-53
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: xlsr-big-kcnnn
+  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. -->
+# xlsr-big-kcnnn
+This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0000
+- Wer: 0.0420
+## 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: 0.0004
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 132
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|
+| 2.213         | 1.2945  | 200   | 1.0902          | 0.8641 |
+| 0.7297        | 2.5890  | 400   | 0.1257          | 0.1338 |
+| 0.2259        | 3.8835  | 600   | 0.0404          | 0.0591 |
+| 0.1269        | 5.1780  | 800   | 0.0286          | 0.0332 |
+| 0.0854        | 6.4725  | 1000  | 0.0162          | 0.0289 |
+| 0.0716        | 7.7670  | 1200  | 0.0185          | 0.0358 |
+| 0.057         | 9.0615  | 1400  | 0.0130          | 0.0267 |
+| 0.0444        | 10.3560 | 1600  | 0.0031          | 0.0205 |
+| 0.0368        | 11.6505 | 1800  | 0.0036          | 0.0287 |
+| 0.0396        | 12.9450 | 2000  | 0.0069          | 0.0480 |
+| 0.0315        | 14.2395 | 2200  | 0.0030          | 0.0291 |
+| 0.0352        | 15.5340 | 2400  | 0.0074          | 0.0119 |
+| 0.0393        | 16.8285 | 2600  | 0.0013          | 0.0414 |
+| 0.0291        | 18.1230 | 2800  | 0.0012          | 0.0068 |
+| 0.0216        | 19.4175 | 3000  | 0.0035          | 0.0161 |
+| 0.0248        | 20.7120 | 3200  | 0.0023          | 0.0070 |
+| 0.0207        | 22.0065 | 3400  | 0.0015          | 0.0235 |
+| 0.0212        | 23.3010 | 3600  | 0.0137          | 0.0360 |
+| 0.0225        | 24.5955 | 3800  | 0.0008          | 0.0454 |
+| 0.019         | 25.8900 | 4000  | 0.0005          | 0.0125 |
+| 0.0195        | 27.1845 | 4200  | 0.0015          | 0.0316 |
+| 0.0175        | 28.4790 | 4400  | 0.0032          | 0.0050 |
+| 0.0196        | 29.7735 | 4600  | 0.0008          | 0.0056 |
+| 0.017         | 31.0680 | 4800  | 0.0009          | 0.0193 |
+| 0.0191        | 32.3625 | 5000  | 0.0002          | 0.0523 |
+| 0.0165        | 33.6570 | 5200  | 0.0016          | 0.0094 |
+| 0.0172        | 34.9515 | 5400  | 0.0030          | 0.0551 |
+| 0.0098        | 36.2460 | 5600  | 0.0014          | 0.0468 |
+| 0.0109        | 37.5405 | 5800  | 0.0012          | 0.0508 |
+| 0.0104        | 38.8350 | 6000  | 0.0007          | 0.0472 |
+| 0.0124        | 40.1294 | 6200  | 0.0008          | 0.0328 |
+| 0.0147        | 41.4239 | 6400  | 0.0008          | 0.0336 |
+| 0.0092        | 42.7184 | 6600  | 0.0010          | 0.0107 |
+| 0.0097        | 44.0129 | 6800  | 0.0008          | 0.0291 |
+| 0.0095        | 45.3074 | 7000  | 0.0002          | 0.0330 |
+| 0.0088        | 46.6019 | 7200  | 0.0020          | 0.0209 |
+| 0.0095        | 47.8964 | 7400  | 0.0006          | 0.0384 |
+| 0.0085        | 49.1909 | 7600  | 0.0002          | 0.0470 |
+| 0.0085        | 50.4854 | 7800  | 0.0001          | 0.0436 |
+| 0.0109        | 51.7799 | 8000  | 0.0010          | 0.0422 |
+| 0.0087        | 53.0744 | 8200  | 0.0012          | 0.0076 |
+| 0.0099        | 54.3689 | 8400  | 0.0009          | 0.0348 |
+| 0.0087        | 55.6634 | 8600  | 0.0002          | 0.0173 |
+| 0.0094        | 56.9579 | 8800  | 0.0016          | 0.0183 |
+| 0.006         | 58.2524 | 9000  | 0.0001          | 0.0105 |
+| 0.0064        | 59.5469 | 9200  | 0.0001          | 0.0342 |
+| 0.0054        | 60.8414 | 9400  | 0.0001          | 0.0394 |
+| 0.0055        | 62.1359 | 9600  | 0.0000          | 0.0295 |
+| 0.0058        | 63.4304 | 9800  | 0.0000          | 0.0289 |
+| 0.007         | 64.7249 | 10000 | 0.0001          | 0.0480 |
+| 0.0043        | 66.0194 | 10200 | 0.0000          | 0.0364 |
+| 0.0045        | 67.3139 | 10400 | 0.0001          | 0.0309 |
+| 0.0026        | 68.6084 | 10600 | 0.0000          | 0.0354 |
+| 0.0031        | 69.9029 | 10800 | 0.0000          | 0.0352 |
+| 0.0035        | 71.1974 | 11000 | 0.0004          | 0.0263 |
+| 0.0038        | 72.4919 | 11200 | 0.0000          | 0.0225 |
+| 0.0027        | 73.7864 | 11400 | 0.0001          | 0.0227 |
+| 0.0037        | 75.0809 | 11600 | 0.0000          | 0.0366 |
+| 0.0024        | 76.3754 | 11800 | 0.0000          | 0.0370 |
+| 0.0028        | 77.6699 | 12000 | 0.0002          | 0.0245 |
+| 0.0025        | 78.9644 | 12200 | 0.0003          | 0.0137 |
+| 0.0022        | 80.2589 | 12400 | 0.0000          | 0.0320 |
+| 0.0021        | 81.5534 | 12600 | 0.0000          | 0.0348 |
+| 0.0023        | 82.8479 | 12800 | 0.0000          | 0.0255 |
+| 0.002         | 84.1424 | 13000 | 0.0000          | 0.0257 |
+| 0.0016        | 85.4369 | 13200 | 0.0000          | 0.0354 |
+| 0.0018        | 86.7314 | 13400 | 0.0000          | 0.0442 |
+| 0.0018        | 88.0259 | 13600 | 0.0000          | 0.0380 |
+| 0.0014        | 89.3204 | 13800 | 0.0000          | 0.0390 |
+| 0.0014        | 90.6149 | 14000 | 0.0000          | 0.0404 |
+| 0.0014        | 91.9094 | 14200 | 0.0000          | 0.0430 |
+| 0.0015        | 93.2039 | 14400 | 0.0000          | 0.0428 |
+| 0.0008        | 94.4984 | 14600 | 0.0000          | 0.0428 |
+| 0.0011        | 95.7929 | 14800 | 0.0000          | 0.0414 |
+| 0.001         | 97.0874 | 15000 | 0.0000          | 0.0396 |
+| 0.0008        | 98.3819 | 15200 | 0.0000          | 0.0416 |
+| 0.0009        | 99.6764 | 15400 | 0.0000          | 0.0420 |
+### Framework versions
+- Transformers 4.45.0.dev0
+- Pytorch 2.1.2
+- Datasets 2.20.0
+- Tokenizers 0.19.1