wav2vec2-wer – AI Model by adrianSauer | AlphaNeural AI

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adrianSauer/
wav2vec2-wer

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automatic-speech-recognition

generated_from_trainer

mozilla-foundation/common_voice_16_1

glob-asr/wav2vec2-large-xls-r-300m-guarani-small

endpoints_compatible

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Common Voice 16

This model is a fine-tuned version of glob-asr/wav2vec2-large-xls-r-300m-guarani-small on the Common Voice 16 dataset. It achieves the following results on the evaluation set:

Loss: 0.3513
Wer: 49.7668

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: 1e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4171	1.0152	100	0.3798	55.2965
0.3376	2.0305	200	0.3628	53.8974
0.294	3.0457	300	0.3528	52.4983
0.2632	4.0609	400	0.3484	49.7668
0.2459	5.0761	500	0.3513	49.7668

Framework versions

Transformers 4.44.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1