Ukrainian STT model (with Language Model)
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UK dataset.
It achieves the following results on the evaluation set without the language model:
- Loss: 0.1875
- Wer: 0.2033
- Cer: 0.0384
Model description
On 100 test example the model shows the following results:
Without LM:
- WER: 0.1862
- CER: 0.0277
With LM:
- WER: 0.1218
- CER: 0.0190
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.2815 | 7.93 | 500 | 0.3536 | 0.4753 | 0.1009 |
| 1.0869 | 15.86 | 1000 | 0.2317 | 0.3111 | 0.0614 |
| 0.9984 | 23.8 | 1500 | 0.2022 | 0.2676 | 0.0521 |
| 0.975 | 31.74 | 2000 | 0.1948 | 0.2469 | 0.0487 |
| 0.9306 | 39.67 | 2500 | 0.1916 | 0.2377 | 0.0464 |
| 0.8868 | 47.61 | 3000 | 0.1903 | 0.2257 | 0.0439 |
| 0.8424 | 55.55 | 3500 | 0.1786 | 0.2206 | 0.0423 |
| 0.8126 | 63.49 | 4000 | 0.1849 | 0.2160 | 0.0416 |
| 0.7901 | 71.42 | 4500 | 0.1869 | 0.2138 | 0.0413 |
| 0.7671 | 79.36 | 5000 | 0.1855 | 0.2075 | 0.0394 |
| 0.7467 | 87.3 | 5500 | 0.1884 | 0.2049 | 0.0389 |
| 0.731 | 95.24 | 6000 | 0.1877 | 0.2060 | 0.0387 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_7_0with splittest
python eval.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --dataset mozilla-foundation/common_voice_7_0 --config uk --split test
Eval results on Common Voice 7 "test" (WER):
| Without LM | With LM (run ./eval.py) |
|---|---|
| 21.52 | 14.62 |
