metadata

language:
  - ur
license: apache-2.0
tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
  - cer
model-index:
  - name: wav2vec2-60-urdu
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_7_0
          name: Common Voice ur
          args: ur
        metrics:
          - type: wer
            value: 59.1
            name: 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
          - type: cer
            value: 33.1
            name: Test CER
            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

wav2vec2-large-xlsr-53-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: 0.5913
Cer: 0.3310

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: 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
12.6045	8.33	100	8.4997	0.6978	0.3923
1.3367	16.67	200	5.0015	0.6515	0.3556
0.5344	25.0	300	9.3687	0.6393	0.3625
0.2922	33.33	400	9.2381	0.6236	0.3432
0.1867	41.67	500	6.2150	0.6035	0.3448
0.1166	50.0	600	6.4496	0.5913	0.3310

Framework versions

Transformers 4.15.0
Pytorch 1.10.0+cu111
Datasets 1.17.0
Tokenizers 0.10.3