w2v2-base-pretrained_lr5e-5_at1_da1-p4 – AI Model by MelanieKoe | AlphaNeural AI

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MelanieKoe/
w2v2-base-pretrained_lr5e-5_at1_da1-p4

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automatic-speech-recognition

generated_from_trainer

facebook/wav2vec2-base

endpoints_compatible

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w2v2-base-pretrained_lr5e-5_at1_da1-p4

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6341
Wer: 0.1039

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: 5e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
18.6256	5.21	250	4.2622	1.0
3.3901	10.42	500	3.2209	1.0
3.0963	15.62	750	3.1175	1.0
2.0992	20.83	1000	0.5962	0.4402
0.2069	26.04	1250	0.4456	0.1310
0.0849	31.25	1500	0.4902	0.1200
0.0596	36.46	1750	0.5079	0.1176
0.0437	41.67	2000	0.5362	0.1136
0.0355	46.88	2250	0.5433	0.1156
0.0281	52.08	2500	0.5994	0.1136
0.0238	57.29	2750	0.6018	0.1112
0.02	62.5	3000	0.5970	0.1120
0.0181	67.71	3250	0.6282	0.1083
0.0167	72.92	3500	0.6120	0.1075
0.0145	78.12	3750	0.6404	0.1047
0.014	83.33	4000	0.6341	0.1039

Framework versions

Transformers 4.35.0
Pytorch 2.0.0
Datasets 2.14.6
Tokenizers 0.14.1