metadata

library_name: transformers
license: apache-2.0
base_model: wu-kiot/whisper-small-am-fleurs
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: whisper-small-fc-am
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: am
          split: None
          args: am
        metrics:
          - name: Wer
            type: wer
            value: 62.73062730627307

whisper-small-fc-am

This model is a fine-tuned version of wu-kiot/whisper-small-am-fleurs on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.3756
Wer: 62.7306

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: 5e-05
train_batch_size: 16
eval_batch_size: 32
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: 150
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2866	1.0	44	0.2855	63.9958
0.1582	2.0	88	0.2958	64.1539
0.0885	3.0	132	0.3311	67.4222
0.0793	4.0	176	0.3700	66.4207
0.0375	5.0	220	0.3756	62.7306

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0