metadata
library_name: transformers
language:
- ru
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-russian
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Wav2vec2-large ru - slowlydoor
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: ru
split: None
args: 'config: ru, split: test'
metrics:
- name: Wer
type: wer
value: 22.3988525667842
Wav2vec2-large ru - slowlydoor
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-russian on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2124
- Wer: 22.3989
- Cer: 4.8036
- Ser: 75.4264
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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Ser |
|---|---|---|---|---|---|---|
| 0.3421 | 0.1516 | 500 | 0.2593 | 27.7416 | 6.2518 | 81.6311 |
| 0.2979 | 0.3032 | 1000 | 0.2741 | 27.9854 | 6.3745 | 82.2290 |
| 0.2787 | 0.4548 | 1500 | 0.2538 | 27.3041 | 6.0743 | 81.1998 |
| 0.325 | 0.6064 | 2000 | 0.2701 | 29.4006 | 6.5501 | 83.6503 |
| 0.3048 | 0.7580 | 2500 | 0.2435 | 27.0914 | 6.0148 | 80.8077 |
| 0.294 | 0.9096 | 3000 | 0.2495 | 26.9503 | 5.9946 | 80.9939 |
| 0.2648 | 1.0612 | 3500 | 0.2675 | 26.8356 | 6.0261 | 80.8175 |
| 0.2691 | 1.2129 | 4000 | 0.2372 | 26.1220 | 5.8259 | 80.2294 |
| 0.2245 | 1.3645 | 4500 | 0.2394 | 26.1603 | 5.8315 | 80.3470 |
| 0.2738 | 1.5161 | 5000 | 0.2388 | 26.0420 | 5.7826 | 79.9941 |
| 0.2767 | 1.6677 | 5500 | 0.2330 | 25.8089 | 5.7248 | 79.5138 |
| 0.2689 | 1.8193 | 6000 | 0.2284 | 25.7312 | 5.6832 | 79.6216 |
| 0.2571 | 1.9709 | 6500 | 0.2370 | 25.3403 | 5.6065 | 79.3080 |
| 0.2479 | 2.1225 | 7000 | 0.2372 | 25.2065 | 5.5776 | 78.9943 |
| 0.2021 | 2.2741 | 7500 | 0.2284 | 24.8718 | 5.4638 | 78.6610 |
| 0.1864 | 2.4257 | 8000 | 0.2280 | 24.8132 | 5.4340 | 78.8669 |
| 0.1953 | 2.5773 | 8500 | 0.2237 | 24.4941 | 5.3856 | 78.3670 |
| 0.195 | 2.7289 | 9000 | 0.2190 | 24.2658 | 5.2770 | 77.8279 |
| 0.1829 | 2.8805 | 9500 | 0.2194 | 24.2443 | 5.2697 | 77.8671 |
| 0.1457 | 3.0321 | 10000 | 0.2205 | 24.2587 | 5.2398 | 77.8279 |
| 0.1435 | 3.1837 | 10500 | 0.2223 | 23.7985 | 5.1608 | 77.1613 |
| 0.1435 | 3.3354 | 11000 | 0.2219 | 23.6551 | 5.1230 | 76.9065 |
| 0.1752 | 3.4870 | 11500 | 0.2186 | 23.4829 | 5.0767 | 76.5438 |
| 0.1793 | 3.6386 | 12000 | 0.2232 | 23.4339 | 5.0977 | 76.4556 |
| 0.1682 | 3.7902 | 12500 | 0.2133 | 23.1853 | 5.0090 | 76.0929 |
| 0.1607 | 3.9418 | 13000 | 0.2135 | 22.7610 | 4.9091 | 75.7597 |
| 0.1463 | 4.0934 | 13500 | 0.2138 | 22.8495 | 4.9314 | 76.1125 |
| 0.1654 | 4.2450 | 14000 | 0.2138 | 22.6379 | 4.8814 | 75.7008 |
| 0.1586 | 4.3966 | 14500 | 0.2173 | 22.6678 | 4.8705 | 75.5342 |
| 0.1438 | 4.5482 | 15000 | 0.2166 | 22.5411 | 4.8437 | 75.5342 |
| 0.1645 | 4.6998 | 15500 | 0.2146 | 22.4658 | 4.8308 | 75.3774 |
| 0.1254 | 4.8514 | 16000 | 0.2124 | 22.3989 | 4.8036 | 75.4264 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1