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---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- common_voice
metrics:
- wer
- cer
model-index:
- name: wav2vec2-large-xlsr-53-urdu
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Urdu Speech Recognition # Optional. Example: Speech Recognition
dataset:
type: common_voice # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Urdu # Required. Example: Common Voice zh-CN
args: ur # Optional. Example: zh-CN
metrics:
- type: wer # Required. Example: wer
value: 66.2 # Required. Example: 20.90
name: Test WER # Optional. Example: Test WER
args:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 50
- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
- type: cer # Required. Example: wer
value: 31.7 # Required. Example: 20.90
name: Test CER # Optional. Example: Test WER
args:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 50
- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
---
<!-- 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. -->
# wav2vec2-large-xlsr-53-urdu
This model is a fine-tuned version of [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5727
- Wer: 0.6620
- Cer: 0.3166
More information needed
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 Persian checkpoint and finetune the XLSR model.
## Training procedure
Trained on m3hrdadfi/wav2vec2-large-xlsr-persian-v3 due to lesser number of samples. Persian and Urdu are quite similar.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.9707 | 8.33 | 100 | 1.2689 | 0.8463 | 0.4373 |
| 0.746 | 16.67 | 200 | 1.2370 | 0.7214 | 0.3486 |
| 0.3719 | 25.0 | 300 | 1.3885 | 0.6908 | 0.3381 |
| 0.2411 | 33.33 | 400 | 1.4780 | 0.6690 | 0.3186 |
| 0.1841 | 41.67 | 500 | 1.5557 | 0.6629 | 0.3241 |
| 0.165 | 50.0 | 600 | 1.5727 | 0.6620 | 0.3166 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
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