whisper-small-combined-data – AI Model by zagibest | AlphaNeural AI

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whisper-small-combined-data

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automatic-speech-recognition

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openai/whisper-small

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Whisper Small MN with custom data + Common voice + Fluers combined - Zagi

This model is a fine-tuned version of openai/whisper-small on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2272
Wer: 23.6455
Cer: 7.3582

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: 8
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: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.4602	0.39	500	0.4537	44.8910	13.9069
0.3075	0.79	1000	0.3099	33.6078	10.3059
0.1829	1.18	1500	0.2715	28.9205	8.8684
0.1838	1.58	2000	0.2486	26.7223	8.2105
0.1619	1.97	2500	0.2328	25.3029	7.8554
0.1038	2.37	3000	0.2339	24.4936	7.6527
0.0915	2.76	3500	0.2287	23.7944	7.3275
0.0815	3.16	4000	0.2272	23.6455	7.3582

Framework versions

Transformers 4.39.1
Pytorch 2.0.1+cu117
Datasets 2.18.0
Tokenizers 0.15.2