whisper-medium-he-teamim-aviv-base

cantillation/whisper-medium-he-teamim-aviv-base

whisper-medium-he-teamim-aviv-base 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. --

Downloads · 30 days

3% of all-time downloads

All-time downloads

365

Public

Parameters

764M

6.1 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors3.1 GB · 100%

At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: whisper
Access: Public
Created: Feb 27, 2024
Updated: Mar 11, 2024
SHA: 449d5c4e

Base models

openai/whisper-medium

Model card

From the Hugging Face model README

he

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: 0.1138
Wer: 9.9943
Precision: 0.8917
Recall: 0.8913
F1: 0.8914
Precision Median: 1.0
Recall Median: 1.0
F1 Median: 1.0
Precision Max: 1.0
Recall Max: 1.0
F1 Max: 1.0
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: 4e-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
training_steps: 10000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Precision	Recall	F1	Precision Median	Recall Median	F1 Median	Precision Max	Recall Max	F1 Max
0.2168	0.04	500	0.2124	27.7691	0.6808	0.7027	0.6909	0.8125	0.8462	0.8276	1.0	1.0	1.0
0.1421	0.08	1000	0.1752	21.5191	0.7794	0.7820	0.7803	0.8889	0.8947	0.8947	1.0	1.0	1.0
0.086	0.12	1500	0.1510	17.9741	0.8044	0.8044	0.8040	0.9231	0.9231	0.9167	1.0	1.0	1.0
0.0822	0.16	2000	0.1357	17.1839	0.8070	0.8091	0.8078	0.9231	0.9231	0.9231	1.0	1.0	1.0
0.0675	0.2	2500	0.1227	14.9416	0.8324	0.8320	0.8319	0.9333	0.9333	0.9333	1.0	1.0	1.0
0.0583	0.24	3000	0.1224	14.0376	0.8528	0.8498	0.8510	0.9333	0.9333	0.9375	1.0	1.0	1.0
0.0528	0.28	3500	0.1167	13.8667	0.8393	0.8410	0.8399	0.9333	0.9333	0.9333	1.0	1.0	1.0
0.0431	0.32	4000	0.1173	13.3827	0.8546	0.8579	0.8560	0.9375	0.9412	0.9412	1.0	1.0	1.0
0.0402	0.36	4500	0.1154	12.1654	0.8695	0.8703	0.8697	0.9412	0.9412	0.9444	1.0	1.0	1.0
0.0385	0.4	5000	0.1173	11.9448	0.8593	0.8578	0.8584	0.9444	0.9444	0.9474	1.0	1.0	1.0
0.0266	0.44	5500	0.1144	12.1014	0.8706	0.8732	0.8717	0.9474	0.95	0.9583	1.0	1.0	1.0
0.021	0.48	6000	0.1161	11.7099	0.8737	0.8744	0.8739	1.0	1.0	0.9706	1.0	1.0	1.0
0.0228	0.52	6500	0.1109	10.9909	0.8685	0.8692	0.8687	1.0	1.0	0.9697	1.0	1.0	1.0
0.0172	0.56	7000	0.1075	10.7702	0.8780	0.8793	0.8784	1.0	0.9545	0.9697	1.0	1.0	1.0
0.0117	0.6	7500	0.1107	10.4356	0.8834	0.8825	0.8828	1.0	1.0	1.0	1.0	1.0	1.0
0.0151	0.64	8000	0.1101	10.3146	0.8886	0.8899	0.8891	1.0	1.0	0.9744	1.0	1.0	1.0
0.0136	0.68	8500	0.1079	10.0370	0.8895	0.8903	0.8897	1.0	1.0	1.0	1.0	1.0	1.0
0.0135	0.72	9000	0.1112	9.9445	0.8892	0.8892	0.8891	1.0	1.0	1.0	1.0	1.0	1.0
0.0084	0.76	9500	0.1136	9.8875	0.8967	0.8964	0.8964	1.0	1.0	1.0	1.0	1.0	1.0
0.0098	0.8	10000	0.1138	9.9943	0.8917	0.8913	0.8914	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Task: Automatic Speech Recognition
Library: transformers
Type: whisper
License: apache-2.0
Languages: he
Created: Feb 27, 2024
Updated: Mar 11, 2024