whisper-large-v3-salt-plus-xog-myx-kin-swa-continued

This model is a fine-tuned version of jq/whisper-large-v3-salt-plus-xog-myx-kin-swa on the generator dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.4453
• eval_model_preparation_time: 0.0155
• eval_WER_eng: 0.018
• eval_WER_lug: 0.142
• eval_WER_ach: 0.195
• eval_WER_lgg: 0.189
• eval_WER_teo: 0.202
• eval_WER_nyn: 0.234
• eval_WER_myx: 0.461
• eval_WER_xog: 0.453
• eval_WER_swa: 0.069
• eval_WER_kin: 0.111
• eval_WER_mean: 0.207
• eval_CER_eng: 0.009
• eval_CER_lug: 0.029
• eval_CER_ach: 0.045
• eval_CER_lgg: 0.045
• eval_CER_teo: 0.051
• eval_CER_nyn: 0.043
• eval_CER_myx: 0.092
• eval_CER_xog: 0.081
• eval_CER_swa: 0.015
• eval_CER_kin: 0.031
• eval_CER_mean: 0.044
• eval_runtime: 738.1416
• eval_samples_per_second: 1.165
• eval_steps_per_second: 0.073
• step: 0

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: 16
• seed: 42
• 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: 3000
• training_steps: 30000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.48.2
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0