|
--- |
|
license: apache-2.0 |
|
base_model: facebook/wav2vec2-xls-r-300m |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- common_voice_16_1 |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: wav2vec-turkish |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: common_voice_16_1 |
|
type: common_voice_16_1 |
|
config: tr |
|
split: test |
|
args: tr |
|
metrics: |
|
- name: Wer |
|
type: wer |
|
value: 0.27954553626002226 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# wav2vec-turkish |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_16_1 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3312 |
|
- Wer: 0.2795 |
|
|
|
## 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: 8 |
|
- 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 5.433 | 0.29 | 400 | 1.1787 | 0.9025 | |
|
| 0.7193 | 0.58 | 800 | 0.6239 | 0.6505 | |
|
| 0.5243 | 0.88 | 1200 | 0.5098 | 0.5901 | |
|
| 0.4514 | 1.17 | 1600 | 0.4618 | 0.5131 | |
|
| 0.419 | 1.46 | 2000 | 0.4341 | 0.4990 | |
|
| 0.3975 | 1.75 | 2400 | 0.4016 | 0.4809 | |
|
| 0.3756 | 2.05 | 2800 | 0.3926 | 0.4684 | |
|
| 0.3421 | 2.34 | 3200 | 0.3841 | 0.4639 | |
|
| 0.3418 | 2.63 | 3600 | 0.3889 | 0.4551 | |
|
| 0.3409 | 2.92 | 4000 | 0.3615 | 0.4295 | |
|
| 0.3039 | 3.21 | 4400 | 0.3939 | 0.4562 | |
|
| 0.2934 | 3.51 | 4800 | 0.3866 | 0.4531 | |
|
| 0.2971 | 3.8 | 5200 | 0.3891 | 0.4497 | |
|
| 0.2953 | 4.09 | 5600 | 0.3694 | 0.4405 | |
|
| 0.2836 | 4.38 | 6000 | 0.3583 | 0.4252 | |
|
| 0.2721 | 4.67 | 6400 | 0.3562 | 0.4164 | |
|
| 0.2685 | 4.97 | 6800 | 0.3574 | 0.4215 | |
|
| 0.251 | 5.26 | 7200 | 0.3660 | 0.4239 | |
|
| 0.2537 | 5.55 | 7600 | 0.3723 | 0.4308 | |
|
| 0.2629 | 5.84 | 8000 | 0.3758 | 0.4359 | |
|
| 0.2469 | 6.14 | 8400 | 0.3799 | 0.4295 | |
|
| 0.2342 | 6.43 | 8800 | 0.3453 | 0.3947 | |
|
| 0.2306 | 6.72 | 9200 | 0.3361 | 0.3977 | |
|
| 0.2284 | 7.01 | 9600 | 0.3592 | 0.3970 | |
|
| 0.213 | 7.3 | 10000 | 0.3451 | 0.3904 | |
|
| 0.2188 | 7.6 | 10400 | 0.3426 | 0.3828 | |
|
| 0.2239 | 7.89 | 10800 | 0.3392 | 0.3878 | |
|
| 0.205 | 8.18 | 11200 | 0.3729 | 0.4021 | |
|
| 0.2049 | 8.47 | 11600 | 0.3511 | 0.3981 | |
|
| 0.2082 | 8.77 | 12000 | 0.3719 | 0.4143 | |
|
| 0.2047 | 9.06 | 12400 | 0.3569 | 0.3984 | |
|
| 0.1895 | 9.35 | 12800 | 0.3416 | 0.3798 | |
|
| 0.1935 | 9.64 | 13200 | 0.3378 | 0.3793 | |
|
| 0.1963 | 9.93 | 13600 | 0.3301 | 0.3883 | |
|
| 0.1889 | 10.23 | 14000 | 0.3577 | 0.3881 | |
|
| 0.182 | 10.52 | 14400 | 0.3281 | 0.3776 | |
|
| 0.1794 | 10.81 | 14800 | 0.3368 | 0.3780 | |
|
| 0.1736 | 11.1 | 15200 | 0.3275 | 0.3664 | |
|
| 0.1659 | 11.4 | 15600 | 0.3504 | 0.3753 | |
|
| 0.1651 | 11.69 | 16000 | 0.3343 | 0.3733 | |
|
| 0.1735 | 11.98 | 16400 | 0.3510 | 0.3750 | |
|
| 0.1569 | 12.27 | 16800 | 0.3243 | 0.3558 | |
|
| 0.1535 | 12.56 | 17200 | 0.3239 | 0.3603 | |
|
| 0.1588 | 12.86 | 17600 | 0.3372 | 0.3655 | |
|
| 0.1524 | 13.15 | 18000 | 0.3453 | 0.3709 | |
|
| 0.1453 | 13.44 | 18400 | 0.3301 | 0.3590 | |
|
| 0.1483 | 13.73 | 18800 | 0.3443 | 0.3597 | |
|
| 0.1432 | 14.02 | 19200 | 0.3401 | 0.3584 | |
|
| 0.1374 | 14.32 | 19600 | 0.3357 | 0.3618 | |
|
| 0.1399 | 14.61 | 20000 | 0.3386 | 0.3621 | |
|
| 0.142 | 14.9 | 20400 | 0.3136 | 0.3547 | |
|
| 0.1307 | 15.19 | 20800 | 0.3328 | 0.3501 | |
|
| 0.1299 | 15.49 | 21200 | 0.3346 | 0.3458 | |
|
| 0.1301 | 15.78 | 21600 | 0.3188 | 0.3473 | |
|
| 0.1285 | 16.07 | 22000 | 0.3323 | 0.3522 | |
|
| 0.1197 | 16.36 | 22400 | 0.3333 | 0.3392 | |
|
| 0.1225 | 16.65 | 22800 | 0.3545 | 0.3590 | |
|
| 0.1263 | 16.95 | 23200 | 0.3360 | 0.3410 | |
|
| 0.1134 | 17.24 | 23600 | 0.3204 | 0.3332 | |
|
| 0.114 | 17.53 | 24000 | 0.3264 | 0.3349 | |
|
| 0.1165 | 17.82 | 24400 | 0.3160 | 0.3323 | |
|
| 0.1134 | 18.12 | 24800 | 0.3479 | 0.3377 | |
|
| 0.1066 | 18.41 | 25200 | 0.3306 | 0.3378 | |
|
| 0.1027 | 18.7 | 25600 | 0.3286 | 0.3286 | |
|
| 0.1083 | 18.99 | 26000 | 0.3285 | 0.3227 | |
|
| 0.0937 | 19.28 | 26400 | 0.3240 | 0.3259 | |
|
| 0.1007 | 19.58 | 26800 | 0.3286 | 0.3283 | |
|
| 0.0996 | 19.87 | 27200 | 0.3278 | 0.3277 | |
|
| 0.0972 | 20.16 | 27600 | 0.3171 | 0.3212 | |
|
| 0.0927 | 20.45 | 28000 | 0.3426 | 0.3283 | |
|
| 0.0932 | 20.75 | 28400 | 0.3418 | 0.3215 | |
|
| 0.0932 | 21.04 | 28800 | 0.3246 | 0.3192 | |
|
| 0.086 | 21.33 | 29200 | 0.3385 | 0.3201 | |
|
| 0.0868 | 21.62 | 29600 | 0.3441 | 0.3164 | |
|
| 0.0875 | 21.91 | 30000 | 0.3246 | 0.3161 | |
|
| 0.0815 | 22.21 | 30400 | 0.3303 | 0.3105 | |
|
| 0.0832 | 22.5 | 30800 | 0.3288 | 0.3062 | |
|
| 0.0781 | 22.79 | 31200 | 0.3411 | 0.3098 | |
|
| 0.077 | 23.08 | 31600 | 0.3343 | 0.3146 | |
|
| 0.0755 | 23.37 | 32000 | 0.3211 | 0.3093 | |
|
| 0.0742 | 23.67 | 32400 | 0.3268 | 0.3044 | |
|
| 0.0721 | 23.96 | 32800 | 0.3222 | 0.3045 | |
|
| 0.0699 | 24.25 | 33200 | 0.3266 | 0.2993 | |
|
| 0.0663 | 24.54 | 33600 | 0.3410 | 0.3008 | |
|
| 0.0719 | 24.84 | 34000 | 0.3221 | 0.3014 | |
|
| 0.0682 | 25.13 | 34400 | 0.3290 | 0.2976 | |
|
| 0.0674 | 25.42 | 34800 | 0.3356 | 0.2967 | |
|
| 0.0661 | 25.71 | 35200 | 0.3181 | 0.2964 | |
|
| 0.0681 | 26.0 | 35600 | 0.3318 | 0.2964 | |
|
| 0.0619 | 26.3 | 36000 | 0.3220 | 0.2945 | |
|
| 0.0617 | 26.59 | 36400 | 0.3270 | 0.2913 | |
|
| 0.0592 | 26.88 | 36800 | 0.3391 | 0.2909 | |
|
| 0.0569 | 27.17 | 37200 | 0.3394 | 0.2900 | |
|
| 0.0557 | 27.47 | 37600 | 0.3359 | 0.2877 | |
|
| 0.0555 | 27.76 | 38000 | 0.3306 | 0.2847 | |
|
| 0.055 | 28.05 | 38400 | 0.3344 | 0.2827 | |
|
| 0.0516 | 28.34 | 38800 | 0.3389 | 0.2845 | |
|
| 0.0544 | 28.63 | 39200 | 0.3360 | 0.2840 | |
|
| 0.0542 | 28.93 | 39600 | 0.3366 | 0.2828 | |
|
| 0.0524 | 29.22 | 40000 | 0.3343 | 0.2819 | |
|
| 0.0527 | 29.51 | 40400 | 0.3319 | 0.2803 | |
|
| 0.0503 | 29.8 | 40800 | 0.3312 | 0.2795 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.1 |
|
- Pytorch 2.2.0+cu121 |
|
- Datasets 2.17.0 |
|
- Tokenizers 0.15.2 |
|
|
