metadata

license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
  - hf-asr-leaderboard
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Medium Tajik
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: tg_tj
          split: test
          args: tg_tj
        metrics:
          - name: Wer
            type: wer
            value: 23.153018764230197

Whisper Medium Tajik

This model is a fine-tuned version of openai/whisper-medium on the google/fleurs tg_tj dataset. It achieves the following results on the evaluation set:

Loss: 0.9217
Wer: 23.1530

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: 32
eval_batch_size: 16
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
0.0016	66.0	1000	0.6929	24.2993
0.0001	133.0	2000	0.8054	23.3022
0.0001	199.0	3000	0.8652	23.2237
0.0	266.0	4000	0.9019	23.2394
0.0	333.0	5000	0.9217	23.1530

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2