wav2vec2-large-xls-r-300m-ar
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.4819
- Wer: 0.4244
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 11.0435 | 0.67 | 400 | 4.3104 | 1.0 |
| 3.4451 | 1.34 | 800 | 3.1566 | 1.0 |
| 3.1399 | 2.01 | 1200 | 3.0532 | 0.9990 |
| 2.8538 | 2.68 | 1600 | 1.6994 | 0.9238 |
| 1.7195 | 3.35 | 2000 | 0.8867 | 0.6727 |
| 1.326 | 4.02 | 2400 | 0.6603 | 0.5834 |
| 1.1561 | 4.69 | 2800 | 0.5809 | 0.5479 |
| 1.0764 | 5.36 | 3200 | 0.5943 | 0.5495 |
| 1.0144 | 6.03 | 3600 | 0.5344 | 0.5251 |
| 0.965 | 6.7 | 4000 | 0.4844 | 0.4936 |
| 0.927 | 7.37 | 4400 | 0.5048 | 0.5019 |
| 0.8985 | 8.04 | 4800 | 0.5809 | 0.5267 |
| 0.8684 | 8.71 | 5200 | 0.4740 | 0.4753 |
| 0.8581 | 9.38 | 5600 | 0.4813 | 0.4834 |
| 0.8334 | 10.05 | 6000 | 0.4515 | 0.4545 |
| 0.8134 | 10.72 | 6400 | 0.4370 | 0.4543 |
| 0.8002 | 11.39 | 6800 | 0.4225 | 0.4384 |
| 0.7884 | 12.06 | 7200 | 0.4593 | 0.4565 |
| 0.7675 | 12.73 | 7600 | 0.4752 | 0.4680 |
| 0.7607 | 13.4 | 8000 | 0.4950 | 0.4771 |
| 0.7475 | 14.07 | 8400 | 0.4373 | 0.4391 |
| 0.7397 | 14.74 | 8800 | 0.4506 | 0.4541 |
| 0.7289 | 15.41 | 9200 | 0.4840 | 0.4691 |
| 0.722 | 16.08 | 9600 | 0.4701 | 0.4571 |
| 0.7067 | 16.75 | 10000 | 0.4561 | 0.4461 |
| 0.7033 | 17.42 | 10400 | 0.4384 | 0.4347 |
| 0.6915 | 18.09 | 10800 | 0.4424 | 0.4290 |
| 0.6854 | 18.76 | 11200 | 0.4635 | 0.4360 |
| 0.6813 | 19.43 | 11600 | 0.4280 | 0.4147 |
| 0.6776 | 20.1 | 12000 | 0.4610 | 0.4344 |
| 0.67 | 20.77 | 12400 | 0.4540 | 0.4367 |
| 0.6653 | 21.44 | 12800 | 0.4509 | 0.4234 |
| 0.6609 | 22.11 | 13200 | 0.4874 | 0.4444 |
| 0.6541 | 22.78 | 13600 | 0.4542 | 0.4230 |
| 0.6528 | 23.45 | 14000 | 0.4732 | 0.4373 |
| 0.6463 | 24.12 | 14400 | 0.4483 | 0.4188 |
| 0.6399 | 24.79 | 14800 | 0.4731 | 0.4341 |
| 0.6353 | 25.46 | 15200 | 0.5031 | 0.4412 |
| 0.6358 | 26.13 | 15600 | 0.4986 | 0.4397 |
| 0.6317 | 26.8 | 16000 | 0.5000 | 0.4360 |
| 0.6262 | 27.47 | 16400 | 0.4958 | 0.4318 |
| 0.6317 | 28.14 | 16800 | 0.4738 | 0.4234 |
| 0.6205 | 28.81 | 17200 | 0.4853 | 0.4262 |
| 0.6205 | 29.48 | 17600 | 0.4819 | 0.4244 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0