metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-base
tags:
  - generated_from_trainer
datasets:
  - razhan/DOLMA-speech
metrics:
  - wer
model-index:
  - name: whisper-base-zza
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: razhan/DOLMA-speech zazaki
          type: razhan/DOLMA-speech
          args: zazaki
        metrics:
          - name: Wer
            type: wer
            value: 1.2574447646493756

whisper-base-zza

This model is a fine-tuned version of openai/whisper-base on the razhan/DOLMA-speech zazaki dataset. It achieves the following results on the evaluation set:

Loss: 4.0956
Wer: 1.2574
Cer: 1.0132

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: 192
eval_batch_size: 128
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 384
total_eval_batch_size: 256
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1
num_epochs: 5.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
No log	1.0	1	4.7831	1.2622	1.0146
No log	2.0	2	4.7831	1.2622	1.0146
No log	3.0	3	4.7831	1.2622	1.0146
No log	4.0	4	4.7831	1.2622	1.0146
No log	5.0	5	4.0956	1.2574	1.0132

Framework versions

Transformers 4.49.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0