README.md

metadata

library_name: transformers
language:
  - eu
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Tiny Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_17_0 eu
          type: mozilla-foundation/common_voice_17_0
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 20.124409102568798

Whisper Tiny Basque

This model is a fine-tuned version of openai/whisper-tiny on the mozilla-foundation/common_voice_17_0 eu dataset. It achieves the following results on the evaluation set:

• Loss: 0.4350
• Wer: 20.1244

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: 3.75e-05
• train_batch_size: 256
• eval_batch_size: 128
• seed: 42
• optimizer: Use OptimizerNames.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: 500
• training_steps: 40000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0348	9.3458	1000	0.3382	22.7152
0.0021	18.6916	2000	0.4092	21.7844
0.0009	28.0374	3000	0.4509	21.9026
0.0023	37.3832	4000	0.4062	20.7181
0.0003	46.7290	5000	0.4350	20.1244
0.0002	56.0748	6000	0.4546	20.1702
0.0001	65.4206	7000	0.4745	20.2179
0.0001	74.7664	8000	0.4941	20.1995
0.0	84.1121	9000	0.5142	20.3342
0.0	93.4579	10000	0.5353	20.4386
0.0	102.8037	11000	0.5567	20.5495
0.0	112.1495	12000	0.5788	20.6100
0.0	121.4953	13000	0.6023	20.6988
0.0	130.8411	14000	0.6253	20.7767
0.0	140.1869	15000	0.6486	20.8683

Framework versions

• Transformers 4.52.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1