README.md

metadata

library_name: transformers
language:
  - hi
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - ifc0nfig/whisper_fine_tune_v3
metrics:
  - wer
model-index:
  - name: Whisper Small Hi - Vyapar V2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Vyapar Calling Data 1200 hours
          type: ifc0nfig/whisper_fine_tune_v3
          args: 'split: test'
        metrics:
          - name: Wer
            type: wer
            value: 48.40833787694502

Whisper Small Hi - Vyapar V2

This model is a fine-tuned version of openai/whisper-small on the Vyapar Calling Data 1200 hours dataset. It achieves the following results on the evaluation set:

• Loss: 0.9627
• Wer: 48.4083

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.8904	0.9251	1000	1.0187	56.1507
1.7278	1.8501	2000	0.9433	53.3196
1.1629	2.7752	3000	0.9364	48.6768
0.8499	3.7003	4000	0.9627	48.4083

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu118
• Datasets 3.2.0
• Tokenizers 0.21.0