indicwav2vec-hindi-mucs-62256-hinglish_mixed_scripts-45_100-1e-4-epochs-100-FT
This model is a fine-tuned version of ai4bharat/indicwav2vec-hindi on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8878
- Global Wer: 21.5720
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Global Wer |
|---|---|---|---|---|
| No log | 1.0 | 302 | 3.5012 | 100.0 |
| 23.0206 | 2.0 | 604 | 1.1709 | 49.3875 |
| 23.0206 | 3.0 | 906 | 0.8571 | 36.5090 |
| 2.1736 | 4.0 | 1208 | 0.7523 | 30.8778 |
| 1.4177 | 5.0 | 1510 | 0.7476 | 30.0953 |
| 1.4177 | 6.0 | 1812 | 0.7401 | 27.1691 |
| 1.0921 | 7.0 | 2114 | 0.7150 | 26.4376 |
| 1.0921 | 8.0 | 2416 | 0.6728 | 27.4413 |
| 0.9379 | 9.0 | 2718 | 0.7249 | 24.4471 |
| 0.7912 | 10.0 | 3020 | 0.7256 | 24.1409 |
| 0.7912 | 11.0 | 3322 | 0.7004 | 24.2600 |
| 0.6755 | 12.0 | 3624 | 0.7326 | 23.5965 |
| 0.6755 | 13.0 | 3926 | 0.7307 | 23.5454 |
| 0.6161 | 14.0 | 4228 | 0.7548 | 23.4433 |
| 0.5508 | 15.0 | 4530 | 0.7898 | 22.4736 |
| 0.5508 | 16.0 | 4832 | 0.7920 | 22.0313 |
| 0.4870 | 17.0 | 5134 | 0.7982 | 22.8309 |
| 0.4870 | 18.0 | 5436 | 0.7756 | 22.2865 |
| 0.4590 | 19.0 | 5738 | 0.8266 | 21.4359 |
| 0.4186 | 20.0 | 6040 | 0.8378 | 20.5682 |
| 0.4186 | 21.0 | 6342 | 0.8256 | 21.6911 |
| 0.3727 | 22.0 | 6644 | 0.8933 | 21.1637 |
| 0.3727 | 23.0 | 6946 | 0.8878 | 21.5720 |
Framework versions
- Transformers 5.13.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.22.2