This model is a fine-tuned version of openai/whisper-tiny on the Speech Transcription in English from e-banking domain. dataset. It achieves the following results on the evaluation set:

Loss: 0.8668
Wer Ortho: 0.4009
Wer: 0.3823

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: 16
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 400
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.3501	3.57	100	0.7134	0.4568	0.4212
0.044	7.14	200	0.7639	0.4096	0.3746
0.0048	10.71	300	0.8265	0.4109	0.3854
0.0021	14.29	400	0.8668	0.4009	0.3823

Framework versions

Transformers 4.36.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.15.0

Shamik/whisper-tiny-polyAI-minds14

Whisper Tiny finetuned on PolyAI Minds14 English US

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Shamik/
whisper-tiny-polyAI-minds14