<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
b24-wav2vec2-large-xls-r-romansh-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3401
- Wer: 0.2625
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 9.4471 | 0.76 | 100 | 3.3151 | 1.0 |
| 3.0392 | 1.52 | 200 | 3.0118 | 1.0 |
| 2.9633 | 2.29 | 300 | 3.0023 | 1.0 |
| 2.9643 | 3.05 | 400 | 2.9365 | 1.0 |
| 2.9381 | 3.81 | 500 | 2.9319 | 1.0 |
| 2.9411 | 4.58 | 600 | 2.9264 | 1.0 |
| 2.9407 | 5.34 | 700 | 2.9141 | 1.0 |
| 2.9027 | 6.11 | 800 | 2.8848 | 1.0 |
| 2.8833 | 6.87 | 900 | 2.8796 | 0.9988 |
| 2.8805 | 7.63 | 1000 | 2.8679 | 0.9956 |
| 2.7051 | 8.4 | 1100 | 1.8944 | 1.0 |
| 1.343 | 9.16 | 1200 | 0.7785 | 0.6970 |
| 0.8156 | 9.92 | 1300 | 0.5659 | 0.5824 |
| 0.591 | 10.68 | 1400 | 0.4982 | 0.5163 |
| 0.488 | 11.45 | 1500 | 0.4421 | 0.4299 |
| 0.4056 | 12.21 | 1600 | 0.3927 | 0.3959 |
| 0.3488 | 12.97 | 1700 | 0.4095 | 0.3910 |
| 0.2977 | 13.74 | 1800 | 0.3833 | 0.3687 |
| 0.273 | 14.5 | 1900 | 0.3690 | 0.3388 |
| 0.2601 | 15.27 | 2000 | 0.3505 | 0.3121 |
| 0.2258 | 16.03 | 2100 | 0.3577 | 0.3121 |
| 0.2122 | 16.79 | 2200 | 0.3467 | 0.3018 |
| 0.2095 | 17.56 | 2300 | 0.3361 | 0.2951 |
| 0.1719 | 18.32 | 2400 | 0.3572 | 0.2948 |
| 0.1722 | 19.08 | 2500 | 0.3380 | 0.2857 |
| 0.1634 | 19.84 | 2600 | 0.3516 | 0.2883 |
| 0.1592 | 20.61 | 2700 | 0.3374 | 0.2846 |
| 0.153 | 21.37 | 2800 | 0.3395 | 0.2783 |
| 0.1479 | 22.14 | 2900 | 0.3336 | 0.2729 |
| 0.1443 | 22.9 | 3000 | 0.3234 | 0.2669 |
| 0.1339 | 23.66 | 3100 | 0.3345 | 0.2664 |
| 0.1149 | 24.43 | 3200 | 0.3369 | 0.2664 |
| 0.1205 | 25.19 | 3300 | 0.3470 | 0.2660 |
| 0.1251 | 25.95 | 3400 | 0.3319 | 0.2629 |
| 0.1201 | 26.71 | 3500 | 0.3381 | 0.2667 |
| 0.1107 | 27.48 | 3600 | 0.3538 | 0.2655 |
| 0.1117 | 28.24 | 3700 | 0.3423 | 0.2625 |
| 0.1104 | 29.01 | 3800 | 0.3398 | 0.2608 |
| 0.104 | 29.77 | 3900 | 0.3401 | 0.2625 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
