whisper-medium-he-teamim-aviv-bavly-without-nikud-test-1e-5-warmup-200-justTeamim – AI Model by cantillation | AlphaNeural AI

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cantillation/
whisper-medium-he-teamim-aviv-bavly-without-nikud-test-1e-5-warmup-200-justTeamim

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automatic-speech-recognition

hf-asr-leaderboard

generated_from_trainer

openai/whisper-medium

endpoints_compatible

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

Loss: 0.1002
Precision: 0.0339
Recall: 0.0359
F1: 0.0348
Precision Median: 0.0
Recall Median: 0.0
F1 Median: 0.0
Precision Max: 0.9231
Recall Max: 1.0
F1 Max: 0.9600
Precision Min: 0.0
Recall Min: 0.0
F1 Min: 0.0

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: 1e-05
train_batch_size: 8
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: 200
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Precision Median	Recall Median	F1 Median	Precision Max	Recall Max	F1 Max	Precision Min	Recall Min	F1 Min
0.2011	0.2	500	0.2334	0.0486	0.0506	0.0495	0.0	0.0	0.0	0.8947	0.9444	0.9189	0.0	0.0	0.0
0.0923	0.4	1000	0.1190	0.0946	0.1001	0.0972	0.0	0.0	0.0	0.9524	1.0	0.9756	0.0	0.0	0.0
0.033	0.59	1500	0.1151	0.0468	0.0491	0.0479	0.0	0.0	0.0	0.9375	1.0	0.9600	0.0	0.0	0.0
0.0323	0.79	2000	0.1002	0.0339	0.0359	0.0348	0.0	0.0	0.0	0.9231	1.0	0.9600	0.0	0.0	0.0

Framework versions

Transformers 4.36.2
Pytorch 1.13.1+cu117
Datasets 2.16.1
Tokenizers 0.15.0