Model Overview
Model Features
Model Capabilities
Use Cases
🚀 wavlm-vindata-demo-dist
This model is a fine - tuned version of microsoft/wavlm-base on the PHONGDTD/VINDATAVLSP - NA dataset. It offers a practical solution for automatic speech recognition tasks, leveraging the pre - trained capabilities of the base model and adapting them to the specific dataset. The model achieves the following results on the evaluation set:
- Loss: 3.4439
- Wer: 1.0
🚀 Quick Start
This model can be used directly in speech recognition tasks. You can load the model through the Hugging Face Transformers library and start inference.
📚 Documentation
Model description
This model is a fine - tuned version of microsoft/wavlm-base on the PHONGDTD/VINDATAVLSP - NA dataset.
Intended uses & limitations
The model is mainly used for automatic speech recognition tasks. However, more information about its limitations needs to be further explored.
Training and evaluation data
The model is trained on the PHONGDTD/VINDATAVLSP - NA dataset. But more detailed information about the data needs to be provided.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi - GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
4.0704 | 0.01 | 100 | 3.8768 | 1.0 |
3.6236 | 0.01 | 200 | 3.4611 | 1.0 |
6.597 | 0.02 | 300 | 3.4557 | 1.0 |
3.4744 | 0.03 | 400 | 3.4567 | 1.0 |
5.3992 | 0.04 | 500 | 3.4631 | 1.0 |
4.5348 | 0.04 | 600 | 3.4651 | 1.0 |
3.2457 | 0.05 | 700 | 3.4917 | 1.0 |
3.9245 | 0.06 | 800 | 3.4680 | 1.0 |
3.2904 | 0.07 | 900 | 3.4518 | 1.0 |
3.4768 | 0.07 | 1000 | 3.4506 | 1.0 |
3.2418 | 0.08 | 1100 | 3.4474 | 1.0 |
3.3111 | 0.09 | 1200 | 3.4684 | 1.0 |
3.986 | 0.09 | 1300 | 3.4465 | 1.0 |
4.3206 | 0.1 | 1400 | 3.4723 | 1.0 |
4.682 | 0.11 | 1500 | 3.4732 | 1.0 |
4.858 | 0.12 | 1600 | 3.4416 | 1.0 |
3.2949 | 0.12 | 1700 | 3.4481 | 1.0 |
3.4435 | 0.13 | 1800 | 3.4570 | 1.0 |
5.0695 | 0.14 | 1900 | 3.4448 | 1.0 |
3.4962 | 0.14 | 2000 | 3.4416 | 1.0 |
3.4891 | 0.15 | 2100 | 3.4455 | 1.0 |
4.1281 | 0.16 | 2200 | 3.4447 | 1.0 |
3.5956 | 0.17 | 2300 | 3.4512 | 1.0 |
3.6312 | 0.17 | 2400 | 3.4484 | 1.0 |
4.5383 | 0.18 | 2500 | 3.4435 | 1.0 |
6.1329 | 0.19 | 2600 | 3.4530 | 1.0 |
3.709 | 0.2 | 2700 | 3.4466 | 1.0 |
3.289 | 0.2 | 2800 | 3.4463 | 1.0 |
4.3301 | 0.21 | 2900 | 3.4418 | 1.0 |
4.6656 | 0.22 | 3000 | 3.4447 | 1.0 |
3.4288 | 0.22 | 3100 | 3.4715 | 1.0 |
3.5506 | 0.23 | 3200 | 3.4437 | 1.0 |
3.7497 | 0.24 | 3300 | 3.4910 | 1.0 |
3.5198 | 0.25 | 3400 | 3.4574 | 1.0 |
3.4183 | 0.25 | 3500 | 3.4607 | 1.0 |
4.5573 | 0.26 | 3600 | 3.4421 | 1.0 |
3.5737 | 0.27 | 3700 | 3.4481 | 1.0 |
4.9008 | 0.28 | 3800 | 3.4411 | 1.0 |
4.8725 | 0.28 | 3900 | 3.4422 | 1.0 |
3.5799 | 0.29 | 4000 | 3.4659 | 1.0 |
3.3257 | 0.3 | 4100 | 3.4519 | 1.0 |
3.6887 | 0.3 | 4200 | 3.4827 | 1.0 |
3.3037 | 0.31 | 4300 | 3.4632 | 1.0 |
5.5543 | 0.32 | 4400 | 3.4480 | 1.0 |
3.2898 | 0.33 | 4500 | 3.4404 | 1.0 |
3.2794 | 0.33 | 4600 | 3.4633 | 1.0 |
3.7896 | 0.34 | 4700 | 3.4439 | 1.0 |
3.6662 | 0.35 | 4800 | 3.4587 | 1.0 |
3.588 | 0.35 | 4900 | 3.4520 | 1.0 |
4.0535 | 0.36 | 5000 | 3.4450 | 1.0 |
3.4335 | 0.37 | 5100 | 3.4577 | 1.0 |
3.6317 | 0.38 | 5200 | 3.4443 | 1.0 |
5.2564 | 0.38 | 5300 | 3.4505 | 1.0 |
3.8781 | 0.39 | 5400 | 3.4418 | 1.0 |
4.6269 | 0.4 | 5500 | 3.4425 | 1.0 |
3.6095 | 0.41 | 5600 | 3.4581 | 1.0 |
4.6164 | 0.41 | 5700 | 3.4404 | 1.0 |
3.117 | 0.42 | 5800 | 3.4596 | 1.0 |
4.3939 | 0.43 | 5900 | 3.4401 | 1.0 |
3.5856 | 0.43 | 6000 | 3.4413 | 1.0 |
3.5187 | 0.44 | 6100 | 3.4452 | 1.0 |
4.7991 | 0.45 | 6200 | 3.4481 | 1.0 |
3.3905 | 0.46 | 6300 | 3.4420 | 1.0 |
3.5086 | 0.46 | 6400 | 3.4494 | 1.0 |
4.8217 | 0.47 | 6500 | 3.4477 | 1.0 |
3.3193 | 0.48 | 6600 | 3.4382 | 1.0 |
5.3482 | 0.49 | 6700 | 3.4580 | 1.0 |
3.3947 | 0.49 | 6800 | 3.4767 | 1.0 |
6.3352 | 0.5 | 6900 | 3.4476 | 1.0 |
3.4448 | 0.51 | 7000 | 3.4557 | 1.0 |
3.5358 | 0.51 | 7100 | 3.4438 | 1.0 |
3.3499 | 0.52 | 7200 | 3.4445 | 1.0 |
3.6932 | 0.53 | 7300 | 3.4463 | 1.0 |
6.9058 | 0.54 | 7400 | 3.4482 | 1.0 |
4.5514 | 0.54 | 7500 | 3.4422 | 1.0 |
3.517 | 0.55 | 7600 | 3.4505 | 1.0 |
7.4479 | 0.56 | 7700 | 3.4461 | 1.0 |
3.3761 | 0.56 | 7800 | 3.4511 | 1.0 |
4.5925 | 0.57 | 7900 | 3.4389 | 1.0 |
5.2682 | 0.58 | 8000 | 3.4563 | 1.0 |
5.6748 | 0.59 | 8100 | 3.4601 | 1.0 |
4.4335 | 0.59 | 8200 | 3.4439 | 1.0 |
5.1686 | 0.6 | 8300 | 3.4444 | 1.0 |
3.5245 | 0.61 | 8400 | 3.4629 | 1.0 |
4.9426 | 0.62 | 8500 | 3.4389 | 1.0 |
4.4654 | 0.62 | 8600 | 3.4427 | 1.0 |
3.5626 | 0.63 | 8700 | 3.4521 | 1.0 |
4.7086 | 0.64 | 8800 | 3.4489 | 1.0 |
3.238 | 0.64 | 8900 | 3.4478 | 1.0 |
4.2738 | 0.65 | 9000 | 3.4510 | 1.0 |
3.4468 | 0.66 | 9100 | 3.4411 | 1.0 |
3.2292 | 0.67 | 9200 | 3.4416 | 1.0 |
3.4972 | 0.67 | 9300 | 3.4643 | 1.0 |
7.3434 | 0.68 | 9400 | 3.4587 | 1.0 |
3.708 | 0.69 | 9500 | 3.4799 | 1.0 |
4.6466 | 0.69 | 9600 | 3.4490 | 1.0 |
3.3347 | 0.7 | 9700 | 3.4532 | 1.0 |
5.1486 | 0.71 | 9800 | 3.4427 | 1.0 |
3.6456 | 0.72 | 9900 | 3.4492 | 1.0 |
5.3904 | 0.72 | 10000 | 3.4497 | 1.0 |
4.8832 | 0.73 | 10100 | 3.4476 | 1.0 |
3.4482 | 0.74 | 10200 | 3.4539 | 1.0 |
3.617 | 0.75 | 10300 | 3.4547 | 1.0 |
5.4691 | 0.75 | 10400 | 3.4663 | 1.0 |
4.2759 | 0.76 | 10500 | 3.4401 | 1.0 |
8.2106 | 0.77 | 10600 | 3.4404 | 1.0 |
3.4894 | 0.77 | 10700 | 3.4426 | 1.0 |
3.6875 | 0.78 | 10800 | 3.4439 | 1.0 |
3.3277 | 0.79 | 10900 | 3.4446 | 1.0 |
4.5175 | 0.8 | 11000 | 3.4456 | 1.0 |
5.2161 | 0.8 | 11100 | 3.4388 | 1.0 |
3.5234 | 0.81 | 11200 | 3.4418 | 1.0 |
4.2212 | 0.82 | 11300 | 3.4392 | 1.0 |
3.6923 | 0.83 | 11400 | 3.4494 | 1.0 |
3.4863 | 0.83 | 11500 | 3.4572 | 1.0 |
6.3201 | 0.84 | 11600 | 3.4377 | 1.0 |
3.7543 | 0.85 | 11700 | 3.4533 | 1.0 |
3.3959 | 0.85 | 11800 | 3.4600 | 1.0 |
3.5691 | 0.86 | 11900 | 3.4673 | 1.0 |
3.49 | 0.87 | 12000 | 3.4407 | 1.0 |
7.1165 | 0.88 | 12100 | 3.4427 | 1.0 |
6.731 | 0.88 | 12200 | 3.4394 | 1.0 |
4.4682 | 0.89 | 12300 | 3.4407 | 1.0 |
3.3696 | 0.9 | 12400 | 3.4415 | 1.0 |
4.0241 | 0.9 | 12500 | 3.4454 | 1.0 |
3.521 | 0.91 | 12600 | 3.4379 | 1.0 |
5.5273 | 0.92 | 12700 | 3.4423 | 1.0 |
3.4781 | 0.93 | 12800 | 3.4635 | 1.0 |
3.4542 | 0.93 | 12900 | 3.4411 | 1.0 |
3.2363 | 0.94 | 13000 | 3.4396 | 1.0 |
5.3009 | 0.95 | 13100 | 3.4458 | 1.0 |
3.498 | 0.96 | 13200 | 3.4398 | 1.0 |
6.3325 | 0.96 | 13300 | 3.4514 | 1.0 |
3.5368 | 0.97 | 13400 | 3.4437 | 1.0 |
5.1164 | 0.98 | 13500 | 3.4623 | 1.0 |
3.6144 | 0.98 | 13600 | 3.4512 | 1.0 |
6.6018 | 0.99 | 13700 | 3.4493 | 1.0 |
3.7539 | 1.0 | 13800 | 3.4597 | 1.0 |
3.2903 | 1.01 | 13900 | 3.4813 | 1.0 |
3.3243 | 1.01 | 14000 | 3.4510 | 1.0 |
3.3485 | 1.02 | 14100 | 3.4389 | 1.0 |
3.6197 | 1.03 | 14200 | 3.4519 | 1.0 |
3.322 | 1.04 | 14300 | 3.4399 | 1.0 |
3.2897 | 1.04 | 14400 | 3.4378 | 1.0 |
3.3969 | 1.05 | 14500 | 3.4476 | 1.0 |
3.3289 | 1.06 | 14600 | 3.4646 | 1.0 |
3.3556 | 1.06 | 14700 | 3.4520 | 1.0 |
3.2527 | 1.07 | 14800 | 3.4575 | 1.0 |
3.4003 | 1.08 | 14900 | 3.4443 | 1.0 |
3.3171 | 1.09 | 15000 | 3.4434 | 1.0 |
3.4034 | 1.09 | 15100 | 3.4448 | 1.0 |
3.4363 | 1.1 | 15200 | 3.4560 | 1.0 |
3.3969 | 1.11 | 15300 | 3.4405 | 1.0 |
3.4134 | 1.11 | 15400 | 3.4408 | 1.0 |
3.5059 | 1.12 | 15500 | 3.4395 | 1.0 |
3.3963 | 1.13 | 15600 | 3.4488 | 1.0 |
3.2937 | 1.14 | 15700 | 3.4482 | 1.0 |
3.5635 | 1.14 | 15800 | 3.4621 | 1.0 |
3.4463 | 1.15 | 15900 | 3.4433 | 1.0 |
3.2588 | 1.16 | 16000 | 3.4434 | 1.0 |
3.3617 | 1.17 | 16100 | 3.4542 | 1.0 |
3.3721 | 1.17 | 16200 | 3.4388 | 1.0 |
3.3867 | 1.18 | 16300 | 3.4577 | 1.0 |
3.34 | 1.19 | 16400 | 3.4510 | 1.0 |
3.3676 | 1.19 | 16500 | 3.4434 | 1.0 |
3.5519 | 1.2 | 16600 | 3.4410 | 1.0 |
3.3129 | 1.21 | 16700 | 3.4507 | 1.0 |
3.3368 | 1.22 | 16800 | 3.4718 | 1.0 |
3.3107 | 1.22 | 16900 | 3.4439 | 1.0 |
3.2987 | 1.23 | 17000 | 3.4471 | 1.0 |
3.3102 | 1.24 | 17100 | 3.4435 | 1.0 |
3.2089 | 1.25 | 17200 | 3.4432 | 1.0 |
3.415 | 1.25 | 17300 | 3.4472 | 1.0 |
3.2884 | 1.26 | 17400 | 3.4388 | 1.0 |
3.3837 | 1.27 | 17500 | 3.4444 | 1.0 |
3.3181 | 1.27 | 17600 | 3.4438 | 1.0 |
3.3071 | 1.28 | 17700 | 3.4406 | 1.0 |
3.389 | 1.29 | 17800 | 3.4573 | 1.0 |
3.3246 | 1.3 | 17900 | 3.4580 | 1.0 |
3.3122 | 1.3 | 18000 | 3.4455 | 1.0 |
3.282 | 1.31 | 18100 | 3.4606 | 1.0 |
3.2671 | 1.32 | 18200 | 3.4378 | 1.0 |
3.3441 | 1.32 | 18300 | 3.4432 | 1.0 |
3.3115 | 1.33 | 18400 | 3.4458 | 1.0 |
3.3542 | 1.34 | 18500 | 3.4617 | 1.0 |
3.3924 | 1.35 | 18600 | 3.4549 | 1.0 |
3.4895 | 1.35 | 18700 | 3.4557 | 1.0 |
3.4071 | 1.36 | 18800 | 3.4462 | 1.0 |
3.3373 | 1.37 | 18900 | 3.4606 | 1.0 |
3.3497 | 1.38 | 19000 | 3.4458 | 1.0 |
3.3088 | 1.38 | 19100 | 3.4712 | 1.0 |
3.333 | 1.39 | 19200 | 3.4483 | 1.0 |
3.3773 | 1.4 | 19300 | 3.4455 | 1.0 |
3.357 | 1.4 | 19400 | 3.4379 | 1.0 |
3.3506 | 1.41 | 19500 | 3.4477 | 1.0 |
3.2944 | 1.42 | 19600 | 3.4478 | 1.0 |
3.241 | 1.43 | 19700 | 3.4 |

