aradia-ctc-data2vec-ft

abdusah/aradia-ctc-data2vec-ft

aradia-ctc-data2vec-ft is a automatic speech recognition model from abdusah. Use it when you need speech turned into text. It is set up for transformers.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
Model type: data2vec-audio
Access: Public
Created: Mar 31, 2022
Updated: Apr 1, 2022
SHA: c26cc0cc

Model card

From the Hugging Face model README

aradia-ctc-data2vec-ft

This model is a fine-tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set:

Loss: 3.0464
Wer: 1.0

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: 0.0003
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.43	100	3.3600	1.0
No log	0.87	200	3.0887	1.0
No log	1.3	300	3.0779	1.0
No log	1.74	400	3.0551	1.0
4.8553	2.17	500	3.0526	1.0
4.8553	2.61	600	3.0560	1.0
4.8553	3.04	700	3.1251	1.0
4.8553	3.48	800	3.0870	1.0
4.8553	3.91	900	3.0822	1.0
3.1133	4.35	1000	3.0484	1.0
3.1133	4.78	1100	3.0558	1.0
3.1133	5.22	1200	3.1019	1.0
3.1133	5.65	1300	3.0914	1.0
3.1133	6.09	1400	3.0691	1.0
3.109	6.52	1500	3.0589	1.0
3.109	6.95	1600	3.0508	1.0
3.109	7.39	1700	3.0540	1.0
3.109	7.82	1800	3.0546	1.0
3.109	8.26	1900	3.0524	1.0
3.1106	8.69	2000	3.0569	1.0
3.1106	9.13	2100	3.0622	1.0
3.1106	9.56	2200	3.0518	1.0
3.1106	10.0	2300	3.0749	1.0
3.1106	10.43	2400	3.0698	1.0
3.1058	10.87	2500	3.0665	1.0
3.1058	11.3	2600	3.0555	1.0
3.1058	11.74	2700	3.0589	1.0
3.1058	12.17	2800	3.0611	1.0
3.1058	12.61	2900	3.0561	1.0
3.1071	13.04	3000	3.0480	1.0
3.1071	13.48	3100	3.0492	1.0
3.1071	13.91	3200	3.0574	1.0
3.1071	14.35	3300	3.0538	1.0
3.1071	14.78	3400	3.0505	1.0
3.1061	15.22	3500	3.0600	1.0
3.1061	15.65	3600	3.0596	1.0
3.1061	16.09	3700	3.0623	1.0
3.1061	16.52	3800	3.0800	1.0
3.1061	16.95	3900	3.0583	1.0
3.1036	17.39	4000	3.0534	1.0
3.1036	17.82	4100	3.0563	1.0
3.1036	18.26	4200	3.0481	1.0
3.1036	18.69	4300	3.0477	1.0
3.1036	19.13	4400	3.0505	1.0
3.1086	19.56	4500	3.0485	1.0
3.1086	20.0	4600	3.0481	1.0
3.1086	20.43	4700	3.0615	1.0
3.1086	20.87	4800	3.0658	1.0
3.1086	21.3	4900	3.0505	1.0
3.1028	21.74	5000	3.0492	1.0
3.1028	22.17	5100	3.0485	1.0
3.1028	22.61	5200	3.0483	1.0
3.1028	23.04	5300	3.0479	1.0
3.1028	23.48	5400	3.0509	1.0
3.1087	23.91	5500	3.0530	1.0
3.1087	24.35	5600	3.0486	1.0
3.1087	24.78	5700	3.0514	1.0
3.1087	25.22	5800	3.0505	1.0
3.1087	25.65	5900	3.0508	1.0
3.1043	26.09	6000	3.0501	1.0
3.1043	26.52	6100	3.0467	1.0
3.1043	26.95	6200	3.0466	1.0
3.1043	27.39	6300	3.0465	1.0
3.1043	27.82	6400	3.0465	1.0
3.1175	28.26	6500	3.0466	1.0
3.1175	28.69	6600	3.0466	1.0
3.1175	29.13	6700	3.0465	1.0
3.1175	29.56	6800	3.0465	1.0
3.1175	30.0	6900	3.0464	1.0

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.4
Tokenizers 0.11.6

Task: Automatic Speech Recognition
Library: transformers
Type: data2vec-audio
Created: Mar 31, 2022
Updated: Apr 1, 2022