README.md

metadata

library_name: transformers
language:
  - te
base_model:
  - openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Small Te - Prashanth Kattoju
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17
          type: mozilla-foundation/common_voice_17_0
          config: te
          split: test
          args: te
        metrics:
          - name: Wer
            type: wer
            value: 38.46153846153847

Whisper Small Te - Prashanth Kattoju

This model is a fine-tuned version of openai/whisper-small-v2 on the Common Voice 17 dataset. It achieves the following results on the evaluation set:

• Loss: 0.8926
• Wer Ortho: 85.1648
• Wer: 38.4615

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_steps: 50
• training_steps: 1000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.0	142.8571	1000	0.8926	85.1648	38.4615

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.1
• Tokenizers 0.19.1