wav2vec2-2-gpt2-medium – AI Model by speech-seq2seq | AlphaNeural AI

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speech-seq2seq/
wav2vec2-2-gpt2-medium

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speech-encoder-decoder

automatic-speech-recognition

generated_from_trainer

librispeech_asr

endpoints_compatible

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

Loss: 3.5264
Wer: 1.7073

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: 8
eval_batch_size: 8
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: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.4032	0.28	500	4.6724	1.9406
4.6417	0.56	1000	4.7143	1.8874
4.5725	0.84	1500	4.6413	1.9451
4.0178	1.12	2000	4.5470	1.8861
3.9084	1.4	2500	4.4360	1.8881
3.9297	1.68	3000	4.2814	1.8652
3.707	1.96	3500	4.1035	1.8320
3.1373	2.24	4000	3.9557	1.7762
3.3152	2.52	4500	3.7737	1.7454
2.9501	2.8	5000	3.5264	1.7073

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.3
Tokenizers 0.11.0