metadata

language:
  - tt
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: Whisper Small TT
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16.1
          type: mozilla-foundation/common_voice_16_1
          config: tt
          split: None
          args: 'config: tt, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 34.84448939782538

Whisper Medium fine-tuned for Tatar language

This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set:

Loss: 0.2809
Wer: 34.8445

Training and evaluation data

Training data was taken from Common Voice 16.1 dataset

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 250
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1399	1.2293	1000	0.3081	38.2040
0.0639	2.4585	2000	0.2809	34.8445

Framework versions

Transformers 4.41.0
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1