wav2vec2-base – AI Model by halcyonzhou | AlphaNeural AI

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halcyonzhou/
wav2vec2-base

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wav2vec2-base

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4500
Wer: 0.2132

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: 3e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use adafactor and the args are: No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 6000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1825	4.4267	500	0.2557	0.2045
0.1185	8.8533	1000	0.3017	0.2121
0.0937	13.2756	1500	0.3172	0.2039
0.0777	17.7022	2000	0.3681	0.2179
0.059	22.1244	2500	0.4151	0.2232
0.0673	26.5511	3000	0.4483	0.2138
0.0479	30.9778	3500	0.4478	0.2168
0.0463	35.4	4000	0.4102	0.2138
0.0436	39.8267	4500	0.4533	0.2109
0.0355	44.2489	5000	0.4166	0.2150
0.0242	48.6756	5500	0.4591	0.2156
0.0227	53.0978	6000	0.4500	0.2132

Framework versions

Transformers 4.55.1
Pytorch 2.8.0+cu129
Datasets 3.6.0
Tokenizers 0.21.4