|
|
--- |
|
|
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 |
|
|
|
