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---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-urdu
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_7_0 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Common Voice ur # Required. Example: Common Voice zh-CN
args: ur # Optional. Example: zh-CN
metrics:
- type: wer # Required. Example: wer
value: 52.40 # Required. Example: 20.90
name: Test WER With LM # Optional. Example: Test WER
args:
- 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 # Optional. Example for BLEU: max_order
- type: cer With LM # Required. Example: wer
value: 26.46 # Required. Example: 20.90
name: Test CER # Optional. Example: Test WER
args:
- 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
- type: wer With LM CV8 # Required. Example: wer
value: 45.63 # Required. Example: 20.90
name: Test CER # Optional. Example: Test WER
- type: cer With LM CV8 # Required. Example: wer
value: 20.45 # Required. Example: 20.90
name: Test CER # Optional. Example: Test WER
---
<!-- 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-xls-r-300m-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: 0.5747
- 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
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