base-model-fairseq-V1 – AI Model by JordanWHLewis | AlphaNeural AI

You can deploy this model and start earning money today!

JordanWHLewis/
base-model-fairseq-V1

like

0

automatic-speech-recognition

generated_from_trainer

endpoints_compatible

Views

No views yet

base-model-fairseq-V1

This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.7644

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: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss
6.8808	4.04	200	4.6158
3.8357	8.08	400	4.5245
3.7928	12.12	600	4.7469
3.874	16.16	800	4.6975
3.7893	20.2	1000	4.7110
3.8368	24.24	1200	4.8101
3.7922	28.28	1400	4.7443
3.7842	32.32	1600	4.8832
3.7882	36.36	1800	4.7598
3.7777	40.4	2000	4.8261
3.834	44.44	2200	4.7754
3.7972	48.48	2400	4.7644

Framework versions

Transformers 4.28.0
Pytorch 2.0.1
Datasets 2.12.0
Tokenizers 0.13.2