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GwadaDLT/
wav2vec2-gcf-r8

Name: wav2vec2-gcf-r8
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-r8

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: 3.9370
Wer: 96.07

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: 0.0003
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: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
7.4949	2.3474	1000	3.9160	96.07
7.4919	4.6948	2000	3.9299	96.07
7.3981	7.0423	3000	3.9875	96.07
7.4559	9.3897	4000	3.9690	96.07
7.4886	11.7371	5000	4.0359	96.07
7.4874	14.0845	6000	3.9979	96.07
7.4587	16.4319	7000	3.9472	96.07
7.4759	18.7793	8000	3.9370	96.07

Framework versions

Transformers 5.5.0
Pytorch 2.4.1+cu124
Datasets 3.6.0
Tokenizers 0.22.2

GwadaDLT/wav2vec2-gcf-r8

wav2vec2-gcf-r8

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

GwadaDLT/
wav2vec2-gcf-r8