README.md

metadata

language:
  - bn
license: apache-2.0
base_model: openai/whisper-small
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - bengaliAI-kaggle
metrics:
  - wer
model-index:
  - name: whisper-small fintuned-0-10000-50%
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: bengaliAI-kaggle
          type: bengaliAI-kaggle
          args: 'config: bn, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 90.44179607559889

whisper-small fintuned-0-10000-50%

This model is a fine-tuned version of openai/whisper-small on the bengaliAI-kaggle dataset. It achieves the following results on the evaluation set:

• Loss: 0.6193
• Wer: 90.4418

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: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• training_steps: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.8424	0.4	100	0.7538	103.2021
0.6195	0.8	200	0.6193	90.4418

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.0
• Datasets 2.14.4
• Tokenizers 0.13.3