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greenw0lf/wav2vec2-large-xls-r-1b-frisian-cv-8-1h

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face

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wav2vec2-large-xls-r-1b-frisian-cv-8-1h

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the commonvoice8_0 dataset. It achieves the following results on the evaluation set:

—Loss: 0.4120
—Wer: 0.2373

And on the test set:

—Wer: 0.2540

Model description

This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 4 where I use as training set 1 hour of Frisian speech randomly selected from all validated data except the test and evaluation sets.

Intended uses & limitations

The intended use is for recognizing Frisian speech.

Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.

Training and evaluation data

The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split is 1 hour of Frisian randomly selected from validated data except for the recordings from test and evaluation splits.

Training procedure

The script used for training this model can be found in this GitHub repository: link.

Training hyperparameters

The following hyperparameters were used during training:

—learning_rate: 6e-05
—trainbatchsize: 32
—evalbatchsize: 8
—seed: 42
—optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.1
—num_epochs: 80
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.2987	4.35	100	3.0210	1.0
3.1424	8.7	200	2.9611	1.0
2.6299	13.04	300	0.9929	0.8377
1.3134	17.39	400	0.5679	0.5264
0.9747	21.74	500	0.4516	0.3764
0.8755	26.09	600	0.4515	0.3403
0.7227	30.43	700	0.4169	0.3211
0.6634	34.78	800	0.4159	0.2962
0.5568	39.13	900	0.4081	0.2795
0.7943	43.48	1000	0.4090	0.2709
0.5537	47.83	1100	0.4239	0.2649
0.5596	52.17	1200	0.4029	0.2561
0.5523	56.52	1300	0.4073	0.2524
0.4579	60.87	1400	0.4098	0.2470
0.6477	65.22	1500	0.4099	0.2446
0.4957	69.57	1600	0.4167	0.2475
0.3246	73.91	1700	0.4146	0.2389
0.3937	78.26	1800	0.4120	0.2373

Framework versions

—Transformers 4.28.1
—Pytorch 2.0.0+cu117
—Datasets 2.11.0
—Tokenizers 0.13.3