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wav2vec2-large-xlsr-common1000asli-demo-colab-dd
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 1.0671
- Wer: 0.5268
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: 0.0003
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
10.4469 | 10.53 | 400 | 2.9884 | 0.9788 |
1.6358 | 21.05 | 800 | 0.5968 | 0.6578 |
0.3404 | 31.58 | 1200 | 0.5348 | 0.5757 |
0.2218 | 42.11 | 1600 | 0.5945 | 0.5659 |
0.1676 | 52.63 | 2000 | 0.6318 | 0.5753 |
0.1442 | 63.16 | 2400 | 0.6395 | 0.5559 |
0.1237 | 73.68 | 2800 | 0.6981 | 0.5522 |
0.1132 | 84.21 | 3200 | 0.7174 | 0.5428 |
0.1056 | 94.74 | 3600 | 0.7120 | 0.5531 |
0.0942 | 105.26 | 4000 | 0.7865 | 0.5529 |
0.0967 | 115.79 | 4400 | 0.7796 | 0.5546 |
0.0854 | 126.32 | 4800 | 0.7392 | 0.5507 |
0.0816 | 136.84 | 5200 | 0.8173 | 0.5575 |
0.0748 | 147.37 | 5600 | 0.8164 | 0.5550 |
0.0691 | 157.89 | 6000 | 0.8061 | 0.5444 |
0.0654 | 168.42 | 6400 | 0.8098 | 0.5524 |
0.0627 | 178.95 | 6800 | 0.8527 | 0.5655 |
0.0653 | 189.47 | 7200 | 0.8210 | 0.5412 |
0.0639 | 200.0 | 7600 | 0.8619 | 0.5602 |
0.0614 | 210.53 | 8000 | 0.8453 | 0.5606 |
0.0647 | 221.05 | 8400 | 0.8248 | 0.5564 |
0.0611 | 231.58 | 8800 | 0.8323 | 0.5637 |
0.0606 | 242.11 | 9200 | 0.8754 | 0.5654 |
0.0587 | 252.63 | 9600 | 0.8684 | 0.5528 |
0.0524 | 263.16 | 10000 | 0.8798 | 0.5556 |
0.0499 | 273.68 | 10400 | 0.8593 | 0.5553 |
0.0466 | 284.21 | 10800 | 0.9079 | 0.5520 |
0.0505 | 294.74 | 11200 | 0.8680 | 0.5607 |
0.0517 | 305.26 | 11600 | 0.8709 | 0.5557 |
0.0522 | 315.79 | 12000 | 0.8687 | 0.5570 |
0.0453 | 326.32 | 12400 | 0.8585 | 0.5614 |
0.047 | 336.84 | 12800 | 0.9249 | 0.5581 |
0.0431 | 347.37 | 13200 | 0.8934 | 0.5543 |
0.0454 | 357.89 | 13600 | 0.8837 | 0.5583 |
0.0472 | 368.42 | 14000 | 0.9070 | 0.5565 |
0.0431 | 378.95 | 14400 | 0.9202 | 0.5526 |
0.0404 | 389.47 | 14800 | 0.9234 | 0.5543 |
0.0386 | 400.0 | 15200 | 0.9056 | 0.5549 |
0.0372 | 410.53 | 15600 | 0.9901 | 0.5493 |
0.0376 | 421.05 | 16000 | 0.9109 | 0.5460 |
0.0365 | 431.58 | 16400 | 0.9313 | 0.5487 |
0.0347 | 442.11 | 16800 | 0.9027 | 0.5496 |
0.0361 | 452.63 | 17200 | 0.9614 | 0.5457 |
0.0323 | 463.16 | 17600 | 0.9782 | 0.5558 |
0.0325 | 473.68 | 18000 | 0.9549 | 0.5481 |
0.032 | 484.21 | 18400 | 0.9781 | 0.5431 |
0.0289 | 494.74 | 18800 | 0.9840 | 0.5463 |
0.0292 | 505.26 | 19200 | 0.9397 | 0.5357 |
0.0276 | 515.79 | 19600 | 0.9228 | 0.5467 |
0.0283 | 526.32 | 20000 | 0.9683 | 0.5394 |
0.0281 | 536.84 | 20400 | 0.9783 | 0.5479 |
0.026 | 547.37 | 20800 | 0.9663 | 0.5472 |
0.0288 | 557.89 | 21200 | 0.9424 | 0.5426 |
0.0275 | 568.42 | 21600 | 0.9788 | 0.5435 |
0.0264 | 578.95 | 22000 | 0.9703 | 0.5473 |
0.0259 | 589.47 | 22400 | 0.9994 | 0.5446 |
0.0243 | 600.0 | 22800 | 0.9637 | 0.5590 |
0.0251 | 610.53 | 23200 | 0.9577 | 0.5457 |
0.0222 | 621.05 | 23600 | 0.9780 | 0.5419 |
0.0227 | 631.58 | 24000 | 0.9582 | 0.5417 |
0.0222 | 642.11 | 24400 | 0.9847 | 0.5432 |
0.0214 | 652.63 | 24800 | 1.0171 | 0.5449 |
0.022 | 663.16 | 25200 | 0.9819 | 0.5430 |
0.0202 | 673.68 | 25600 | 0.9737 | 0.5413 |
0.0187 | 684.21 | 26000 | 0.9977 | 0.5440 |
0.0213 | 694.74 | 26400 | 0.9919 | 0.5464 |
0.0197 | 705.26 | 26800 | 0.9769 | 0.5357 |
0.0183 | 715.79 | 27200 | 0.9964 | 0.5377 |
0.0187 | 726.32 | 27600 | 0.9973 | 0.5341 |
0.0191 | 736.84 | 28000 | 0.9970 | 0.5399 |
0.0183 | 747.37 | 28400 | 1.0179 | 0.5371 |
0.0176 | 757.89 | 28800 | 1.0020 | 0.5440 |
0.018 | 768.42 | 29200 | 0.9992 | 0.5394 |
0.0157 | 778.95 | 29600 | 1.0502 | 0.5397 |
0.0165 | 789.47 | 30000 | 1.0463 | 0.5397 |
0.0147 | 800.0 | 30400 | 1.0363 | 0.5430 |
0.0153 | 810.53 | 30800 | 0.9890 | 0.5407 |
0.0145 | 821.05 | 31200 | 1.0139 | 0.5369 |
0.0143 | 831.58 | 31600 | 1.0260 | 0.5346 |
0.0141 | 842.11 | 32000 | 1.0277 | 0.5361 |
0.0139 | 852.63 | 32400 | 1.0639 | 0.5335 |
0.0132 | 863.16 | 32800 | 1.0661 | 0.5314 |
0.013 | 873.68 | 33200 | 1.0537 | 0.5335 |
0.0126 | 884.21 | 33600 | 1.0433 | 0.5347 |
0.0121 | 894.74 | 34000 | 1.0275 | 0.5326 |
0.0128 | 905.26 | 34400 | 1.0405 | 0.5327 |
0.0112 | 915.79 | 34800 | 1.0626 | 0.5296 |
0.0115 | 926.32 | 35200 | 1.0583 | 0.5284 |
0.0109 | 936.84 | 35600 | 1.0494 | 0.5287 |
0.0113 | 947.37 | 36000 | 1.0655 | 0.5294 |
0.0104 | 957.89 | 36400 | 1.0723 | 0.5269 |
0.0108 | 968.42 | 36800 | 1.0680 | 0.5267 |
0.0104 | 978.95 | 37200 | 1.0707 | 0.5261 |
0.0108 | 989.47 | 37600 | 1.0649 | 0.5268 |
0.0103 | 1000.0 | 38000 | 1.0671 | 0.5268 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3