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tokens
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: 0.9811
- Wer: 0.4608
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.5212 | 0.59 | 400 | 3.3776 | 1.0 |
| 2.4798 | 1.18 | 800 | 1.0697 | 0.7740 |
| 1.0057 | 1.77 | 1200 | 0.7077 | 0.6487 |
| 0.7731 | 2.36 | 1600 | 0.6113 | 0.5883 |
| 0.6917 | 2.94 | 2000 | 0.5618 | 0.5573 |
| 0.5844 | 3.53 | 2400 | 0.5610 | 0.5532 |
| 0.5606 | 4.12 | 2800 | 0.5584 | 0.5484 |
| 0.4973 | 4.71 | 3200 | 0.5466 | 0.5333 |
| 0.4721 | 5.3 | 3600 | 0.5495 | 0.5178 |
| 0.4439 | 5.89 | 4000 | 0.5667 | 0.5237 |
| 0.3965 | 6.48 | 4400 | 0.5865 | 0.5322 |
| 0.3876 | 7.07 | 4800 | 0.6099 | 0.5135 |
| 0.3407 | 7.66 | 5200 | 0.5891 | 0.5228 |
| 0.33 | 8.25 | 5600 | 0.6135 | 0.5072 |
| 0.3032 | 8.84 | 6000 | 0.6004 | 0.5028 |
| 0.2706 | 9.43 | 6400 | 0.6321 | 0.4991 |
| 0.2709 | 10.01 | 6800 | 0.6541 | 0.5051 |
| 0.2373 | 10.6 | 7200 | 0.6613 | 0.5119 |
| 0.2284 | 11.19 | 7600 | 0.6798 | 0.5086 |
| 0.212 | 11.78 | 8000 | 0.6509 | 0.4910 |
| 0.1983 | 12.37 | 8400 | 0.7018 | 0.5043 |
| 0.1947 | 12.96 | 8800 | 0.6826 | 0.4965 |
| 0.1717 | 13.55 | 9200 | 0.7056 | 0.4828 |
| 0.1741 | 14.14 | 9600 | 0.7544 | 0.5060 |
| 0.1626 | 14.73 | 10000 | 0.7331 | 0.4915 |
| 0.1529 | 15.32 | 10400 | 0.7518 | 0.4772 |
| 0.1504 | 15.91 | 10800 | 0.7362 | 0.4732 |
| 0.1401 | 16.49 | 11200 | 0.7179 | 0.4769 |
| 0.1335 | 17.08 | 11600 | 0.7716 | 0.4826 |
| 0.1185 | 17.67 | 12000 | 0.7465 | 0.4798 |
| 0.1182 | 18.26 | 12400 | 0.8105 | 0.4733 |
| 0.1135 | 18.85 | 12800 | 0.7693 | 0.4743 |
| 0.1098 | 19.44 | 13200 | 0.8362 | 0.4888 |
| 0.1023 | 20.03 | 13600 | 0.8427 | 0.4768 |
| 0.1003 | 20.62 | 14000 | 0.8079 | 0.4741 |
| 0.0936 | 21.21 | 14400 | 0.8551 | 0.4651 |
| 0.0875 | 21.8 | 14800 | 0.8462 | 0.4712 |
| 0.0843 | 22.39 | 15200 | 0.9177 | 0.4782 |
| 0.0846 | 22.97 | 15600 | 0.8618 | 0.4735 |
| 0.08 | 23.56 | 16000 | 0.9017 | 0.4687 |
| 0.0789 | 24.15 | 16400 | 0.9034 | 0.4659 |
| 0.0717 | 24.74 | 16800 | 0.9690 | 0.4734 |
| 0.0714 | 25.33 | 17200 | 0.9395 | 0.4677 |
| 0.0699 | 25.92 | 17600 | 0.9222 | 0.4608 |
| 0.0658 | 26.51 | 18000 | 0.9222 | 0.4621 |
| 0.0612 | 27.1 | 18400 | 0.9691 | 0.4586 |
| 0.0583 | 27.69 | 18800 | 0.9647 | 0.4581 |
| 0.0596 | 28.28 | 19200 | 0.9820 | 0.4614 |
| 0.056 | 28.87 | 19600 | 0.9795 | 0.4596 |
| 0.055 | 29.45 | 20000 | 0.9811 | 0.4608 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
