Step Training Loss Validation Loss Wer 40 9.648200 4.719201 1.000000 80 3.953400 3.477898 1.000000 120 3.289700 3.099611 1.000000 160 3.038400 2.993551 1.000000 200 2.994500 2.979574 1.000000 240 2.959000 2.941970 1.000000 280 2.802100 2.520133 1.000000 320 1.862100 1.499739 0.746423 360 1.191800 1.336315 0.610261 400 0.951300 1.317062 0.598915 440 0.773900 1.312918 0.614702 480 0.624700 1.376327 0.557967

/usr/local/lib/python3.10/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:156: UserWarning: as_target_processor is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument text of the regular __call__ method (either in the same call as your audio inputs, or in a separate call. warnings.warn( /usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.10/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:156: UserWarning: as_target_processor is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument text of the regular __call__ method (either in the same call as your audio inputs, or in a separate call. warnings.warn( /usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn(

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30
mixed_precision_training: Native AMP

Framework versions

Transformers 4.39.3
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2

ogbi/wav2vec2-mms-300m-ikk-3

wav2vec2-mms-300m-ikk-3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Framework versions

ogbi/
wav2vec2-mms-300m-ikk-3