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whisper-small-telugu-large-data

This model is a fine-tuned version of openai/whisper-small on the google/fleurs and openslr dataset in telugu. It achieves the following results on the evaluation set (google/fleurs, test set):

  • Loss: 0.3310
  • Wer: 38.8460

openai/whisper-small has the following zero shot performance on google/fleurs test set:

  • Wer: 117.91

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.128 2.27 500 0.2015 45.1692
0.0462 4.55 1000 0.1877 41.1050
0.0184 6.82 1500 0.2241 40.5153
0.0045 9.09 2000 0.2590 39.7260
0.0019 11.36 2500 0.2824 39.0819
0.0006 13.64 3000 0.3002 38.9096
0.0002 15.91 3500 0.3141 38.5920
0.0001 18.18 4000 0.3232 38.7463
0.0001 20.45 4500 0.3289 38.8370
0.0001 22.73 5000 0.3310 38.8460

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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Datasets used to train steja/whisper-small-telugu-large-data

Evaluation results