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nrshoudi/wav2vec2-base-960h-Arabic

sourceHugging Faceapache-2.0updated 4y agoView on Hugging Face

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wav2vec2-base-960h-Arabic

This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:

—Loss: 0.7209
—Wer: 1.0
—Cer: 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: 0.0005
—trainbatchsize: 16
—evalbatchsize: 6
—seed: 42
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 64
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_steps: 250
—num_epochs: 40
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.7909	1.0	51	0.8018	1.0	1.0
0.6736	2.0	102	1.1252	1.0	1.0
0.7037	3.0	153	0.7432	1.0	1.0
0.724	4.0	204	1.2762	1.0	1.0
0.8887	5.0	255	0.7064	1.0	1.0
0.835	6.0	306	1.0820	1.0	1.0
0.8042	7.0	357	1.0530	1.0	1.0
0.7475	8.0	408	0.6969	1.0	1.0
0.6998	9.0	459	0.7852	1.0	1.0
0.7048	10.0	510	0.6942	1.0	1.0
0.7883	11.0	561	0.7767	1.0	1.0
0.6773	12.0	612	0.7355	1.0	1.0
0.7421	13.0	663	1.5550	1.0	1.0
0.7573	14.0	714	0.8736	1.0	1.0
0.6911	15.0	765	1.3328	1.0	1.0
0.7129	16.0	816	0.8911	1.0	1.0
0.6619	17.0	867	1.0227	1.0	1.0
0.6807	18.0	918	0.7829	1.0	1.0
0.6409	19.0	969	0.9122	1.0	1.0
0.6588	20.0	1020	0.9179	1.0	1.0
0.6648	21.0	1071	0.8469	1.0	1.0
0.6521	22.0	1122	0.7197	1.0	1.0
0.7189	23.0	1173	0.7972	1.0	1.0
0.664	24.0	1224	1.0197	1.0	1.0
0.6611	25.0	1275	1.0786	1.0	1.0
0.6333	26.0	1326	0.8307	1.0	1.0
0.6407	27.0	1377	0.7388	1.0	1.0
0.6322	28.0	1428	0.6789	1.0	1.0
0.6283	29.0	1479	0.6917	1.0	1.0
0.6343	30.0	1530	0.7045	1.0	1.0
0.6292	31.0	1581	0.6717	1.0	1.0
0.6209	32.0	1632	0.7436	1.0	1.0
0.6088	33.0	1683	0.7392	1.0	1.0
0.6103	34.0	1734	0.7387	1.0	1.0
0.6084	35.0	1785	0.7647	1.0	1.0
0.6042	36.0	1836	0.8165	1.0	1.0
0.605	37.0	1887	0.7578	1.0	1.0
0.6042	38.0	1938	0.7359	1.0	1.0
0.6	39.0	1989	0.7287	1.0	1.0
0.5963	40.0	2040	0.7209	1.0	1.0

Framework versions

—Transformers 4.26.1
—Pytorch 1.13.1+cu116
—Datasets 2.10.1
—Tokenizers 0.13.2