metadata

language:
  - hi
license: apache-2.0
base_model: Aakali/whisper-medium-hi
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper medium-translate Hi - Aa
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          args: 'config: hi, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 48.11612382957753

Whisper medium-translate Hi - Aa

This model is a fine-tuned version of Aakali/whisper-medium-hi on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.9904
Wer: 48.1161

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: 8
seed: 42
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
0.1405	2.4450	1000	0.7580	51.5075
0.0245	4.8900	2000	0.8571	51.4000
0.0026	7.3350	3000	0.9280	48.3132
0.0011	9.7800	4000	0.9673	47.6457
0.0006	12.2249	5000	0.9904	48.1161

Framework versions

Transformers 4.41.0
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1