|
--- |
|
language: ko |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- generated_from_trainer |
|
- hf-asr-leaderboard |
|
- robust-speech-event |
|
datasets: |
|
- kresnik/zeroth_korean |
|
model-index: |
|
- name: Wav2Vec2 XLS-R 1B Korean |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Robust Speech Event - Dev Data |
|
type: speech-recognition-community-v2/dev_data |
|
args: ko |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 82.07 |
|
- name: Test CER |
|
type: cer |
|
value: 42.12 |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Robust Speech Event - Test Data |
|
type: speech-recognition-community-v2/eval_data |
|
args: ko |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 82.09 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0639 |
|
- Wer: 0.0449 |
|
|
|
## 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: 7.5e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- 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: 2000 |
|
- num_epochs: 50.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 4.603 | 0.72 | 500 | 4.6572 | 0.9985 | |
|
| 2.6314 | 1.44 | 1000 | 2.0424 | 0.9256 | |
|
| 2.2708 | 2.16 | 1500 | 0.9889 | 0.6989 | |
|
| 2.1769 | 2.88 | 2000 | 0.8366 | 0.6312 | |
|
| 2.1142 | 3.6 | 2500 | 0.7555 | 0.5998 | |
|
| 2.0084 | 4.32 | 3000 | 0.7144 | 0.6003 | |
|
| 1.9272 | 5.04 | 3500 | 0.6311 | 0.5461 | |
|
| 1.8687 | 5.75 | 4000 | 0.6252 | 0.5430 | |
|
| 1.8186 | 6.47 | 4500 | 0.5491 | 0.4988 | |
|
| 1.7364 | 7.19 | 5000 | 0.5463 | 0.4959 | |
|
| 1.6809 | 7.91 | 5500 | 0.4724 | 0.4484 | |
|
| 1.641 | 8.63 | 6000 | 0.4679 | 0.4461 | |
|
| 1.572 | 9.35 | 6500 | 0.4387 | 0.4236 | |
|
| 1.5256 | 10.07 | 7000 | 0.3970 | 0.4003 | |
|
| 1.5044 | 10.79 | 7500 | 0.3690 | 0.3893 | |
|
| 1.4563 | 11.51 | 8000 | 0.3752 | 0.3875 | |
|
| 1.394 | 12.23 | 8500 | 0.3386 | 0.3567 | |
|
| 1.3641 | 12.95 | 9000 | 0.3290 | 0.3467 | |
|
| 1.2878 | 13.67 | 9500 | 0.2893 | 0.3135 | |
|
| 1.2602 | 14.39 | 10000 | 0.2723 | 0.3029 | |
|
| 1.2302 | 15.11 | 10500 | 0.2603 | 0.2989 | |
|
| 1.1865 | 15.83 | 11000 | 0.2440 | 0.2794 | |
|
| 1.1491 | 16.55 | 11500 | 0.2500 | 0.2788 | |
|
| 1.093 | 17.27 | 12000 | 0.2279 | 0.2629 | |
|
| 1.0367 | 17.98 | 12500 | 0.2076 | 0.2443 | |
|
| 0.9954 | 18.7 | 13000 | 0.1844 | 0.2259 | |
|
| 0.99 | 19.42 | 13500 | 0.1794 | 0.2179 | |
|
| 0.9385 | 20.14 | 14000 | 0.1765 | 0.2122 | |
|
| 0.8952 | 20.86 | 14500 | 0.1706 | 0.1974 | |
|
| 0.8841 | 21.58 | 15000 | 0.1791 | 0.1969 | |
|
| 0.847 | 22.3 | 15500 | 0.1780 | 0.2060 | |
|
| 0.8669 | 23.02 | 16000 | 0.1608 | 0.1862 | |
|
| 0.8066 | 23.74 | 16500 | 0.1447 | 0.1626 | |
|
| 0.7908 | 24.46 | 17000 | 0.1457 | 0.1655 | |
|
| 0.7459 | 25.18 | 17500 | 0.1350 | 0.1445 | |
|
| 0.7218 | 25.9 | 18000 | 0.1276 | 0.1421 | |
|
| 0.703 | 26.62 | 18500 | 0.1177 | 0.1302 | |
|
| 0.685 | 27.34 | 19000 | 0.1147 | 0.1305 | |
|
| 0.6811 | 28.06 | 19500 | 0.1128 | 0.1244 | |
|
| 0.6444 | 28.78 | 20000 | 0.1120 | 0.1213 | |
|
| 0.6323 | 29.5 | 20500 | 0.1137 | 0.1166 | |
|
| 0.5998 | 30.22 | 21000 | 0.1051 | 0.1107 | |
|
| 0.5706 | 30.93 | 21500 | 0.1035 | 0.1037 | |
|
| 0.5555 | 31.65 | 22000 | 0.1031 | 0.0927 | |
|
| 0.5389 | 32.37 | 22500 | 0.0997 | 0.0900 | |
|
| 0.5201 | 33.09 | 23000 | 0.0920 | 0.0912 | |
|
| 0.5146 | 33.81 | 23500 | 0.0929 | 0.0947 | |
|
| 0.515 | 34.53 | 24000 | 0.1000 | 0.0953 | |
|
| 0.4743 | 35.25 | 24500 | 0.0922 | 0.0892 | |
|
| 0.4707 | 35.97 | 25000 | 0.0852 | 0.0808 | |
|
| 0.4456 | 36.69 | 25500 | 0.0855 | 0.0779 | |
|
| 0.443 | 37.41 | 26000 | 0.0843 | 0.0738 | |
|
| 0.4388 | 38.13 | 26500 | 0.0816 | 0.0699 | |
|
| 0.4162 | 38.85 | 27000 | 0.0752 | 0.0645 | |
|
| 0.3979 | 39.57 | 27500 | 0.0761 | 0.0621 | |
|
| 0.3889 | 40.29 | 28000 | 0.0771 | 0.0625 | |
|
| 0.3923 | 41.01 | 28500 | 0.0755 | 0.0598 | |
|
| 0.3693 | 41.73 | 29000 | 0.0730 | 0.0578 | |
|
| 0.3642 | 42.45 | 29500 | 0.0739 | 0.0598 | |
|
| 0.3532 | 43.17 | 30000 | 0.0712 | 0.0553 | |
|
| 0.3513 | 43.88 | 30500 | 0.0762 | 0.0516 | |
|
| 0.3349 | 44.6 | 31000 | 0.0731 | 0.0504 | |
|
| 0.3305 | 45.32 | 31500 | 0.0725 | 0.0507 | |
|
| 0.3285 | 46.04 | 32000 | 0.0709 | 0.0489 | |
|
| 0.3179 | 46.76 | 32500 | 0.0667 | 0.0467 | |
|
| 0.3158 | 47.48 | 33000 | 0.0653 | 0.0494 | |
|
| 0.3033 | 48.2 | 33500 | 0.0638 | 0.0456 | |
|
| 0.3023 | 48.92 | 34000 | 0.0644 | 0.0464 | |
|
| 0.2975 | 49.64 | 34500 | 0.0643 | 0.0455 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0.dev0 |
|
- Pytorch 1.10.2+cu102 |
|
- Datasets 1.18.3.dev0 |
|
- Tokenizers 0.11.0 |
|
|
