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rohitp1/
wav2vec2-base-timit-finetune-4-additonal-train

Name: wav2vec2-base-timit-finetune-4-additonal-train
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

generated_from_trainer

apache-2.0

endpoints_compatible

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wav2vec2-base-timit-finetune-4-additonal-train

This model is a fine-tuned version of rohitp1/wav2vec2-base-timit-finetune-3 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4123
Wer: 0.2905

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: 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
0.1543	2.77	200	0.3600	0.3153
0.1277	5.55	400	0.3621	0.3110
0.106	8.33	600	0.3862	0.3050
0.095	11.11	800	0.4055	0.3042
0.0862	13.88	1000	0.4001	0.3083
0.0834	16.66	1200	0.4228	0.2993
0.0676	19.44	1400	0.4245	0.2955
0.0593	22.22	1600	0.4123	0.2905

Framework versions

Transformers 4.24.0
Pytorch 1.12.1
Datasets 2.7.0
Tokenizers 0.11.0

rohitp1/wav2vec2-base-timit-finetune-4-additonal-train

wav2vec2-base-timit-finetune-4-additonal-train

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rohitp1/
wav2vec2-base-timit-finetune-4-additonal-train