README.md

metadata

language:
  - te
base_model: openai/whisper-small-te
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper small Te sample - Prajwal Nagaraj
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: te
          split: None
          args: 'config: te, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 87.36263736263736

Whisper small Te sample - Prajwal Nagaraj

This model is a fine-tuned version of openai/whisper-small-te on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.7139
• Wer: 87.3626

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0001	71.4286	500	0.7139	87.3626

Framework versions

• Transformers 4.41.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1