metadata

language:
  - en
license: apache-2.0
base_model: openai/whisper-large
tags:
  - generated_from_trainer
datasets:
  - common_voice_1_0
metrics:
  - wer
model-index:
  - name: whisper-large-english-TG
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice
          type: common_voice_1_0
          config: en
          split: None
          args: 'config: en, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 18.00053310232233

whisper-large-english-TG

This model is a fine-tuned version of openai/whisper-large on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.4494
Wer: 18.0005

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
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0452	2.6350	1000	0.3455	19.6915
0.0034	5.2701	2000	0.3999	17.8823
0.0005	7.9051	3000	0.4770	18.1438
0.0001	10.5402	4000	0.4494	18.0005

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.19.1