metadata

license: apache-2.0
datasets:
  - mozilla-foundation/common_voice_7_0
language:
  - ur
metrics:
  - wer
  - cer
library_name: transformers
pipeline_tag: automatic-speech-recognition
model-index:
  - name: wav2vec2-urdu
    results:
      - task:
          type: automatic-speech-recognition
          name: Urdu Speech Recognition
        dataset:
          type: common_voice
          name: Common Voice (Urdu)
        metrics:
          - type: wer
            value: 0.5747
            name: WER
            config: load_metric("wer")
            verified: true
          - type: cer
            value: 0.3268
            name: CER
            config: load_metric("cer")
            verified: true

wav2vec2-large-xls-r-300m-Urdu

This model is a fine-tuned version of 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