wav2vec2-base-timit-finetune – AI Model by rohitp1 | AlphaNeural AI

You can deploy this model and start earning money today!

rohitp1/
wav2vec2-base-timit-finetune

like

0

automatic-speech-recognition

generated_from_trainer

endpoints_compatible

Views

No views yet

wav2vec2-base-timit-finetune

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: 972.3115
Wer: 1.0

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: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 60
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3325.1297	1.39	100	4054.7283	1.0
1624.4673	2.77	200	1100.8928	1.0
1079.3557	4.17	300	1009.5025	1.0
1026.4995	5.55	400	979.0	1.0
1005.6487	6.94	500	964.3292	1.0
1000.4138	8.33	600	972.3115	1.0

Framework versions

Transformers 4.24.0
Pytorch 1.12.1
Datasets 2.7.0
Tokenizers 0.11.0