wav2vec2-base-cer – AI Model by abbenedek | AlphaNeural AI

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abbenedek/
wav2vec2-base-cer

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automatic-speech-recognition

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facebook/wav2vec2-base

endpoints_compatible

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wav2vec2-base-cer

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: 0.0018
Cer: 0.0718

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: 64
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: 150
num_epochs: 200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
6.646	15.38	200	1.9010	0.6387
0.6207	30.77	400	0.0849	0.1757
0.0527	46.15	600	0.0643	0.1386
0.0325	61.54	800	0.0117	0.0888
0.0156	76.92	1000	0.0101	0.1148
0.0081	92.31	1200	0.0042	0.1255
0.0057	107.69	1400	0.0036	0.1284
0.0058	123.08	1600	0.0066	0.0891
0.0066	138.46	1800	0.0028	0.0926
0.0049	153.85	2000	0.0026	0.0391
0.0044	169.23	2200	0.0020	0.0574
0.0024	184.62	2400	0.0018	0.0745
0.0023	200.0	2600	0.0018	0.0718

Framework versions

Transformers 4.39.3
Pytorch 2.2.2+cu121
Datasets 2.14.5
Tokenizers 0.15.2