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GwadaDLT/
wav2vec2-gcf

Name: wav2vec2-gcf
Availability: InStock

automatic-speech-recognition

generated_from_trainer

LLL-CREAM/wav2vec2-HAT-0.2K-ALH-base

finetune

cc-by-nc-sa-4.0

endpoints_compatible

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wav2vec2-gcf

This model is a fine-tuned version of LLL-CREAM/wav2vec2-HAT-0.2K-ALH-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.6800
Wer: 100.0

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: 0.0001
train_batch_size: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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: 100
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.6305	0.9390	400	2.7244	100.0
5.4888	1.8779	800	2.7181	100.0
5.5111	2.8169	1200	2.8026	100.0
5.5101	3.7559	1600	2.7278	100.0
5.3605	4.6948	2000	2.6800	100.0

Framework versions

Transformers 5.5.0
Pytorch 2.4.1+cu124
Datasets 3.6.0
Tokenizers 0.22.2

GwadaDLT/wav2vec2-gcf

wav2vec2-gcf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

GwadaDLT/
wav2vec2-gcf