metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: whisper-large-v2-kangri
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: bridgeconn/snow-mountain
          name: snow-moutain-Kangri
          config: Kangri
          split: train_500
        metrics:
          - type: wer
            value: 17.4
            name: WER
            lower_is_better: true

whisper-large-v2-kangri

This model is a fine-tuned version of vasista22/whisper-hindi-large-v2 on the bridgeconn/snow-mountain for the low resource Indian language- Kangri. It achieves the following results on the evaluation set:

Loss: 0.2967
Wer: 0.1740

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
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 16
total_eval_batch_size: 16
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
0.0001	40.0	1000	0.2442	0.1800
0.0	80.0	2000	0.2752	0.1764
0.0	120.0	3000	0.2870	0.1747
0.0	160.0	4000	0.2940	0.1745
0.0	200.0	5000	0.2967	0.1740

Framework versions

Transformers 4.28.0.dev0
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3