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wav2vec2-large-xls-r-300m-Urdu
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 on the common_voice dataset. It achieves the following results on the evaluation set:
- Wer: 0.5747
- Cer: 0.3268
Model description
The training and valid dataset is 0.58 hours. It was hard to train any model on lower number of so I decided to take vakyansh-wav2vec2-urdu-urm-60 checkpoint and finetune the wav2vec2 model.
Training procedure
Trained on Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 due to lesser number of samples.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 4.3054 | 16.67 | 50 | 9.0055 | 0.8306 | 0.4869 |
| 2.0629 | 33.33 | 100 | 9.5849 | 0.6061 | 0.3414 |
| 0.8966 | 50.0 | 150 | 4.8686 | 0.6052 | 0.3426 |
| 0.4197 | 66.67 | 200 | 12.3261 | 0.5817 | 0.3370 |
| 0.294 | 83.33 | 250 | 11.9653 | 0.5712 | 0.3328 |
| 0.2329 | 100.0 | 300 | 7.6846 | 0.5747 | 0.3268 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
