| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: urdumodel |
| | results: [] |
| | metrics: |
| | - wer |
| | - cer |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # urdumodel |
| |
|
| | This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4939 |
| | - Wer: 0.3698 |
| | - Cer: 0.1465 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used. |
| |
|
| | ## Training procedure |
| | All the code is available here |
| | https://github.com/talhaanwarch/Urdu-ASR |
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0003 |
| | - train_batch_size: 24 |
| | - eval_batch_size: 24 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 96 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 20 |
| | - mixed_precision_training: Native AMP |
| | |
| | # Model score on test |
| | When I train I got different WER and CER score on test set, but when I tested locally |
| | I got WER of 0.27 without language model and 0.22 with language model. |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.21.1 |
| | - Pytorch 1.12.0 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| | |
