Teamim-large-v2_Random-True_Augmented_date-26-06-2024_10-34-39

cantillation/Teamim-large-v2_Random-True_Augmented_date-26-06-2024_10-34-39

Teamim-large-v2_Random-True_Augmented_date-26-06-2024_10-34-39 is a automatic speech recognition model from cantillation. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: whisper
Access: Public
Created: Jun 26, 2024
Updated: Jun 26, 2024
SHA: 4ef2e574

Base models

openai/whisper-large-v2

Model card

From the Hugging Face model README

he-cantillation

This model is a fine-tuned version of openai/whisper-large-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1042
Wer: 11.7960
Avg Precision Exact: 0.9096
Avg Recall Exact: 0.9088
Avg F1 Exact: 0.9088
Avg Precision Letter Shift: 0.9281
Avg Recall Letter Shift: 0.9274
Avg F1 Letter Shift: 0.9274
Avg Precision Word Level: 0.9303
Avg Recall Word Level: 0.9298
Avg F1 Word Level: 0.9297
Avg Precision Word Shift: 0.9743
Avg Recall Word Shift: 0.9743
Avg F1 Word Shift: 0.9739
Precision Median Exact: 1.0
Recall Median Exact: 1.0
F1 Median Exact: 1.0
Precision Max Exact: 1.0
Recall Max Exact: 1.0
F1 Max Exact: 1.0
Precision Min Exact: 0.0
Recall Min Exact: 0.0
F1 Min Exact: 0.0
Precision Min Letter Shift: 0.0
Recall Min Letter Shift: 0.0
F1 Min Letter Shift: 0.0
Precision Min Word Level: 0.0
Recall Min Word Level: 0.0
F1 Min Word Level: 0.0
Precision Min Word Shift: 0.1429
Recall Min Word Shift: 0.1
F1 Min Word Shift: 0.1176

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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Avg Precision Exact	Avg Recall Exact	Avg F1 Exact	Avg Precision Letter Shift	Avg Recall Letter Shift	Avg F1 Letter Shift	Avg Precision Word Level	Avg Recall Word Level	Avg F1 Word Level	Avg Precision Word Shift	Avg Recall Word Shift	Avg F1 Word Shift	Precision Median Exact	Recall Median Exact	F1 Median Exact	Precision Max Exact	Recall Max Exact	F1 Max Exact	Precision Min Exact	Recall Min Exact	F1 Min Exact	Precision Min Letter Shift	Recall Min Letter Shift	F1 Min Letter Shift	Precision Min Word Level	Recall Min Word Level	F1 Min Word Level	Precision Min Word Shift	Recall Min Word Shift	F1 Min Word Shift
No log	8e-05	1	5.8860	117.0584	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
0.1314	0.08	1000	0.1577	22.1766	0.8258	0.8234	0.8238	0.8536	0.8516	0.8518	0.8571	0.8560	0.8558	0.9351	0.9372	0.9352	0.9091	0.9	0.9	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0769	0.0769	0.0769
0.0929	0.16	2000	0.1322	18.0894	0.8613	0.8621	0.8611	0.8856	0.8866	0.8855	0.8888	0.8901	0.8888	0.9503	0.9528	0.9508	0.9231	0.9231	0.9231	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1429	0.1111	0.125
0.0402	0.24	3000	0.1180	15.3622	0.8830	0.8851	0.8835	0.9048	0.9071	0.9055	0.9080	0.9104	0.9087	0.9617	0.9655	0.9631	0.9333	0.9375	0.9524	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1429	0.125	0.1333
0.0372	0.32	4000	0.1094	14.3792	0.8904	0.8894	0.8894	0.9141	0.9132	0.9131	0.9170	0.9162	0.9161	0.9680	0.9686	0.9677	0.9375	0.9412	0.9524	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1429	0.1	0.1176
0.0256	0.4	5000	0.1081	13.3518	0.9001	0.8983	0.8987	0.9198	0.9181	0.9185	0.9224	0.9211	0.9213	0.9710	0.9710	0.9705	1.0	1.0	0.9655	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.2222	0.1667	0.1905
0.0211	0.48	6000	0.1029	12.5203	0.9063	0.9046	0.9050	0.9261	0.9245	0.9249	0.9281	0.9269	0.9271	0.9713	0.9714	0.9709	1.0	1.0	0.9677	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1	0.1	0.1111
0.0096	0.56	7000	0.1043	11.8699	0.9097	0.9091	0.9090	0.9284	0.9279	0.9277	0.9305	0.9304	0.9300	0.9731	0.9738	0.9730	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1429	0.1	0.1176
0.0108	0.64	8000	0.1042	11.7960	0.9096	0.9088	0.9088	0.9281	0.9274	0.9274	0.9303	0.9298	0.9297	0.9743	0.9743	0.9739	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1429	0.1	0.1176

Framework versions

Transformers 4.41.2
Pytorch 2.2.1
Datasets 2.20.0
Tokenizers 0.19.1

Task: Automatic Speech Recognition
Library: transformers
Type: whisper
License: apache-2.0
Languages: he
Created: Jun 26, 2024
Updated: Jun 26, 2024