metadata

language:
  - multilingual
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: model trenovan na de setu, nastaveni jazyka de
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: xbilek25/train_set_1st_1000_de_en_de
          type: mozilla-foundation/common_voice_11_0
          args: 'config: ende, split: train'
        metrics:
          - name: Wer
            type: wer
            value: 48.131985098456624

model trenovan na de setu, nastaveni jazyka de

This model is a fine-tuned version of openai/whisper-medium on the xbilek25/train_set_1st_1000_de_en_de dataset. It achieves the following results on the evaluation set:

Loss: 0.1983
Wer: 48.1320

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: 1
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0468	1.25	1000	0.1860	27.8659
0.0034	3.25	2000	0.1983	48.1320

Framework versions

Transformers 4.37.2
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.15.2