metadata

language:
  - ru
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_0
metrics:
  - wer
model-index:
  - name: Whisper Base Ru
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16.0
          type: mozilla-foundation/common_voice_16_0
          config: ru
          split: None
          args: 'config: ru, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 131.35769718547476

Whisper Base Ru

This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.2080
Wer: 131.3577

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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2013	0.61	1000	0.2301	130.4397
0.0753	1.21	2000	0.2159	131.7603
0.0902	1.82	3000	0.2046	129.7846
0.0394	2.43	4000	0.2080	131.3577

Framework versions

Transformers 4.37.2
Pytorch 2.2.2
Datasets 2.18.0
Tokenizers 0.15.1