whisper-base-te – AI Model by parambharat | AlphaNeural AI

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whisper-base-te

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Whisper Base Te - Bharat Ramanathan

This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2455
Wer: 42.6485

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: 96
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.6341	0.1	500	0.3894	60.7108
0.349	0.2	1000	0.3081	52.0935
0.2792	0.3	1500	0.2874	49.7079
0.2433	0.4	2000	0.2720	47.5657
0.2224	1.06	2500	0.2632	45.2288
0.2058	1.16	3000	0.2529	44.3038
0.1944	1.26	3500	0.2519	44.5959
0.1869	1.36	4000	0.2475	43.7196
0.1811	2.03	4500	0.2451	43.3301
0.1775	2.13	5000	0.2455	42.6485

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0
Datasets 2.7.1.dev0
Tokenizers 0.13.2