wav2vec2-xls-r-300m-gn-cv8-3
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: 0.9517
- Wer: 0.8542
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: 0.0001
- 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: 100
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 19.9125 | 5.54 | 100 | 5.4279 | 1.0 |
| 3.8031 | 11.11 | 200 | 3.3070 | 1.0 |
| 3.3783 | 16.65 | 300 | 3.2450 | 1.0 |
| 3.3472 | 22.22 | 400 | 3.2424 | 1.0 |
| 3.2714 | 27.76 | 500 | 3.1100 | 1.0 |
| 3.2367 | 33.32 | 600 | 3.1091 | 1.0 |
| 3.1968 | 38.86 | 700 | 3.1013 | 1.0 |
| 3.2004 | 44.43 | 800 | 3.1173 | 1.0 |
| 3.1656 | 49.97 | 900 | 3.0682 | 1.0 |
| 3.1563 | 55.54 | 1000 | 3.0457 | 1.0 |
| 3.1356 | 61.11 | 1100 | 3.0139 | 1.0 |
| 3.086 | 66.65 | 1200 | 2.8108 | 1.0 |
| 2.954 | 72.22 | 1300 | 2.3238 | 1.0 |
| 2.6125 | 77.76 | 1400 | 1.6461 | 1.0 |
| 2.3296 | 83.32 | 1500 | 1.2834 | 0.9744 |
| 2.1345 | 88.86 | 1600 | 1.1091 | 0.9693 |
| 2.0346 | 94.43 | 1700 | 1.0273 | 0.9233 |
| 1.9611 | 99.97 | 1800 | 0.9642 | 0.9182 |
| 1.9066 | 105.54 | 1900 | 0.9590 | 0.9105 |
| 1.8178 | 111.11 | 2000 | 0.9679 | 0.9028 |
| 1.7799 | 116.65 | 2100 | 0.9007 | 0.8619 |
| 1.7726 | 122.22 | 2200 | 0.9689 | 0.8951 |
| 1.7389 | 127.76 | 2300 | 0.8876 | 0.8593 |
| 1.7151 | 133.32 | 2400 | 0.8716 | 0.8542 |
| 1.6842 | 138.86 | 2500 | 0.9536 | 0.8772 |
| 1.6449 | 144.43 | 2600 | 0.9296 | 0.8542 |
| 1.5978 | 149.97 | 2700 | 0.8895 | 0.8440 |
| 1.6515 | 155.54 | 2800 | 0.9162 | 0.8568 |
| 1.6586 | 161.11 | 2900 | 0.9039 | 0.8568 |
| 1.5966 | 166.65 | 3000 | 0.8627 | 0.8542 |
| 1.5695 | 172.22 | 3100 | 0.9549 | 0.8824 |
| 1.5699 | 177.76 | 3200 | 0.9332 | 0.8517 |
| 1.5297 | 183.32 | 3300 | 0.9163 | 0.8338 |
| 1.5367 | 188.86 | 3400 | 0.8822 | 0.8312 |
| 1.5586 | 194.43 | 3500 | 0.9217 | 0.8363 |
| 1.5429 | 199.97 | 3600 | 0.9564 | 0.8568 |
| 1.5273 | 205.54 | 3700 | 0.9508 | 0.8542 |
| 1.5043 | 211.11 | 3800 | 0.9374 | 0.8542 |
| 1.4724 | 216.65 | 3900 | 0.9622 | 0.8619 |
| 1.4794 | 222.22 | 4000 | 0.9550 | 0.8363 |
| 1.4843 | 227.76 | 4100 | 0.9577 | 0.8465 |
| 1.4781 | 233.32 | 4200 | 0.9543 | 0.8440 |
| 1.4507 | 238.86 | 4300 | 0.9553 | 0.8491 |
| 1.4997 | 244.43 | 4400 | 0.9728 | 0.8491 |
| 1.4371 | 249.97 | 4500 | 0.9543 | 0.8670 |
| 1.4825 | 255.54 | 4600 | 0.9636 | 0.8619 |
| 1.4187 | 261.11 | 4700 | 0.9609 | 0.8440 |
| 1.4363 | 266.65 | 4800 | 0.9567 | 0.8593 |
| 1.4463 | 272.22 | 4900 | 0.9581 | 0.8542 |
| 1.4117 | 277.76 | 5000 | 0.9517 | 0.8542 |
Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0