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---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- common_voice_v7
metrics:
- wer
- cer
model-index:
- name: wav2vec2-60-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: 59.8 # 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: 32.9 # 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 [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Wer: 0.5921
- Cer: 0.3288
## 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 Urdu-60 checkpoint and finetune the wav2vwc2 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: 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 | Wer | Cer |
|:-------------:|:-----:|:----:|:------:|:------:|
| 13.83 | 8.33 | 100 | 0.6611 | 0.3639 |
| 1.0144 | 16.67 | 200 | 0.6498 | 0.3731 |
| 0.5801 | 25.0 | 300 | 0.6454 | 0.3767 |
| 0.3344 | 33.33 | 400 | 0.6349 | 0.3548 |
| 0.1606 | 41.67 | 500 | 0.6105 | 0.3348 |
| 0.0974 | 50.0 | 600 | 0.5921 | 0.3288 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|