metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b19-wav2vec2-large-xls-r-romansh-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: rm-vallader
split: test
args: rm-vallader
metrics:
- name: Wer
type: wer
value: 0.29902189101071264
b19-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: 0.3604
- Wer: 0.2990
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: 50
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
11.864 | 0.38 | 50 | 4.3744 | 1.0 |
3.6659 | 0.76 | 100 | 3.0489 | 1.0 |
2.9989 | 1.14 | 150 | 2.9634 | 1.0 |
2.9763 | 1.52 | 200 | 3.0247 | 1.0 |
2.954 | 1.9 | 250 | 2.9476 | 1.0 |
2.9795 | 2.29 | 300 | 3.0039 | 1.0 |
2.9296 | 2.67 | 350 | 2.9468 | 1.0 |
2.9904 | 3.05 | 400 | 2.9507 | 1.0 |
2.9245 | 3.43 | 450 | 2.9430 | 1.0 |
2.9721 | 3.81 | 500 | 2.9444 | 1.0 |
2.949 | 4.2 | 550 | 2.9392 | 1.0 |
2.9766 | 4.58 | 600 | 2.9383 | 1.0 |
2.9396 | 4.96 | 650 | 2.9393 | 1.0 |
2.9899 | 5.34 | 700 | 2.9373 | 1.0 |
2.9325 | 5.72 | 750 | 2.9331 | 1.0 |
2.9492 | 6.11 | 800 | 2.9287 | 1.0 |
2.9493 | 6.49 | 850 | 2.9379 | 1.0 |
2.9095 | 6.87 | 900 | 2.9258 | 1.0 |
2.9572 | 7.25 | 950 | 2.9377 | 1.0 |
2.9053 | 7.63 | 1000 | 2.9177 | 1.0 |
3.0002 | 8.02 | 1050 | 2.9122 | 1.0 |
2.8892 | 8.4 | 1100 | 2.8918 | 1.0 |
2.8927 | 8.78 | 1150 | 2.8707 | 1.0 |
2.8945 | 9.16 | 1200 | 2.8234 | 0.9991 |
2.7721 | 9.54 | 1250 | 2.4910 | 1.0 |
2.283 | 9.92 | 1300 | 1.5046 | 0.9991 |
1.6257 | 10.3 | 1350 | 1.0799 | 0.9998 |
1.2815 | 10.68 | 1400 | 0.8832 | 0.8158 |
1.1252 | 11.07 | 1450 | 0.7531 | 0.7189 |
0.9593 | 11.45 | 1500 | 0.6509 | 0.6309 |
0.8695 | 11.83 | 1550 | 0.6119 | 0.5836 |
0.8199 | 12.21 | 1600 | 0.5643 | 0.5680 |
0.7481 | 12.59 | 1650 | 0.5298 | 0.5340 |
0.7029 | 12.97 | 1700 | 0.5342 | 0.5254 |
0.6472 | 13.36 | 1750 | 0.5047 | 0.4856 |
0.6388 | 13.74 | 1800 | 0.4892 | 0.4844 |
0.5849 | 14.12 | 1850 | 0.4585 | 0.4548 |
0.5642 | 14.5 | 1900 | 0.4496 | 0.4497 |
0.546 | 14.88 | 1950 | 0.4274 | 0.4218 |
0.5345 | 15.27 | 2000 | 0.4218 | 0.4176 |
0.4925 | 15.65 | 2050 | 0.3989 | 0.3996 |
0.4794 | 16.03 | 2100 | 0.3887 | 0.3936 |
0.4528 | 16.41 | 2150 | 0.4039 | 0.3922 |
0.4542 | 16.79 | 2200 | 0.4135 | 0.3882 |
0.4408 | 17.17 | 2250 | 0.4018 | 0.3826 |
0.4144 | 17.56 | 2300 | 0.4072 | 0.3701 |
0.4048 | 17.94 | 2350 | 0.4026 | 0.3759 |
0.3751 | 18.32 | 2400 | 0.3774 | 0.3570 |
0.393 | 18.7 | 2450 | 0.3799 | 0.3654 |
0.3763 | 19.08 | 2500 | 0.3820 | 0.3484 |
0.3657 | 19.46 | 2550 | 0.3839 | 0.3500 |
0.3483 | 19.84 | 2600 | 0.3967 | 0.3505 |
0.3601 | 20.23 | 2650 | 0.3854 | 0.3524 |
0.3432 | 20.61 | 2700 | 0.3770 | 0.3428 |
0.3382 | 20.99 | 2750 | 0.3797 | 0.3337 |
0.3277 | 21.37 | 2800 | 0.3922 | 0.3414 |
0.3192 | 21.75 | 2850 | 0.3838 | 0.3479 |
0.3087 | 22.14 | 2900 | 0.3766 | 0.3337 |
0.3165 | 22.52 | 2950 | 0.3712 | 0.3242 |
0.3197 | 22.9 | 3000 | 0.3640 | 0.3207 |
0.317 | 23.28 | 3050 | 0.3630 | 0.3232 |
0.3123 | 23.66 | 3100 | 0.3777 | 0.3302 |
0.2804 | 24.05 | 3150 | 0.3640 | 0.3156 |
0.2768 | 24.43 | 3200 | 0.3598 | 0.3034 |
0.3038 | 24.81 | 3250 | 0.3627 | 0.3088 |
0.2716 | 25.19 | 3300 | 0.3571 | 0.3093 |
0.2911 | 25.57 | 3350 | 0.3619 | 0.3102 |
0.2903 | 25.95 | 3400 | 0.3697 | 0.3167 |
0.291 | 26.33 | 3450 | 0.3579 | 0.3093 |
0.2721 | 26.71 | 3500 | 0.3726 | 0.3128 |
0.2589 | 27.1 | 3550 | 0.3603 | 0.3020 |
0.2745 | 27.48 | 3600 | 0.3704 | 0.3074 |
0.2757 | 27.86 | 3650 | 0.3605 | 0.3018 |
0.2677 | 28.24 | 3700 | 0.3572 | 0.3034 |
0.2613 | 28.62 | 3750 | 0.3614 | 0.3014 |
0.2758 | 29.01 | 3800 | 0.3637 | 0.2979 |
0.2652 | 29.39 | 3850 | 0.3615 | 0.3000 |
0.2736 | 29.77 | 3900 | 0.3604 | 0.2990 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3