Modelpublic

thiagolira/CiceroASR

sourceHugging Facemitupdated 3y agoView on Hugging Face

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Model Card

CiceroASR

This model is a fine-tuned version of facebook/w2v-bert-2.0 for the transcription of Classical Latin!

Example from the Aeneid: <video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/hYNFr2od1EKDlRRdzJmzR.webm"></video> Transcription: arma virumque cano (Of arms and men I sing)

Example from Genesis: <video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/9Q6DfG2h8FkABnl55DLBH.webm"></video> Transcription (little error there): creavit deus chaelum et terram (In the beggining God created the heaven and the earth)

It achieves the following results on the evaluation set of my dataset Latin Youtube:

—Loss: 0.5395
—Wer: 0.2220

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: 0.0001
—trainbatchsize: 16
—evalbatchsize: 8
—seed: 42
—gradientaccumulationsteps: 2
—totaltrainbatch_size: 32
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_steps: 300
—num_epochs: 15
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.6548	0.94	50	2.8634	0.9990
2.2055	1.89	100	1.0921	0.9727
1.667	2.83	150	0.7201	0.4615
1.3148	3.77	200	0.6431	0.3866
0.9899	4.72	250	0.5561	0.3116
0.9629	5.66	300	0.6027	0.3817
0.7557	6.6	350	0.7145	0.3145
0.9143	7.55	400	0.4926	0.2610
0.5837	8.49	450	0.5396	0.2619
0.7037	9.43	500	0.5076	0.2746
0.5986	10.38	550	0.5224	0.2415
0.5288	11.32	600	0.5332	0.2259
0.5034	12.26	650	0.5436	0.2249
0.4897	13.21	700	0.5171	0.2162
0.4738	14.15	750	0.5395	0.2220

Framework versions

—Transformers 4.38.1
—Pytorch 2.1.0+cu121
—Datasets 2.17.1
—Tokenizers 0.15.2