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birgermoell/
wav2vec2-liepa-1-percent

Name: wav2vec2-liepa-1-percent
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

common_voice

generated_from_trainer

apache-2.0

endpoints_compatible

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wav2vec2-liepa-1-percent

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - LT dataset. It achieves the following results on the evaluation set:

Loss: 0.5774
Wer: 0.5079

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 15.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.23	100	3.3596	1.0
No log	0.46	200	2.9280	1.0
No log	0.69	300	1.5091	0.9650
No log	0.93	400	0.9943	0.9177
3.1184	1.16	500	0.7590	0.7793
3.1184	1.39	600	0.7336	0.7408
3.1184	1.62	700	0.7040	0.7618
3.1184	1.85	800	0.6815	0.7233
3.1184	2.08	900	0.6457	0.6865
0.7917	2.31	1000	0.5705	0.6813
0.7917	2.55	1100	0.5708	0.6620
0.7917	2.78	1200	0.5888	0.6462
0.7917	3.01	1300	0.6509	0.6970
0.7917	3.24	1400	0.5871	0.6462
0.5909	3.47	1500	0.6199	0.6813
0.5909	3.7	1600	0.6230	0.5919
0.5909	3.94	1700	0.5721	0.6427
0.5909	4.17	1800	0.5331	0.5867
0.5909	4.4	1900	0.5561	0.6007
0.4607	4.63	2000	0.5414	0.5849
0.4607	4.86	2100	0.5390	0.5587
0.4607	5.09	2200	0.5313	0.5569
0.4607	5.32	2300	0.5893	0.5797
0.4607	5.56	2400	0.5507	0.5954
0.3933	5.79	2500	0.5521	0.6025
0.3933	6.02	2600	0.5663	0.5989
0.3933	6.25	2700	0.5636	0.5832
0.3933	6.48	2800	0.5464	0.5919
0.3933	6.71	2900	0.5623	0.5832
0.3367	6.94	3000	0.5324	0.5692
0.3367	7.18	3100	0.5907	0.5394
0.3367	7.41	3200	0.5653	0.5814
0.3367	7.64	3300	0.5707	0.5814
0.3367	7.87	3400	0.5754	0.5429
0.2856	8.1	3500	0.5953	0.5569
0.2856	8.33	3600	0.6275	0.5394
0.2856	8.56	3700	0.6253	0.5569
0.2856	8.8	3800	0.5930	0.5429
0.2856	9.03	3900	0.6082	0.5219
0.2522	9.26	4000	0.6026	0.5447
0.2522	9.49	4100	0.6052	0.5271
0.2522	9.72	4200	0.5871	0.5219
0.2522	9.95	4300	0.5870	0.5236
0.2522	10.19	4400	0.5881	0.5131
0.2167	10.42	4500	0.6122	0.5289
0.2167	10.65	4600	0.6128	0.5166
0.2167	10.88	4700	0.6135	0.5377
0.2167	11.11	4800	0.6055	0.5184
0.2167	11.34	4900	0.6725	0.5569
0.1965	11.57	5000	0.6482	0.5429
0.1965	11.81	5100	0.6037	0.5096
0.1965	12.04	5200	0.5931	0.5131
0.1965	12.27	5300	0.5853	0.5114
0.1965	12.5	5400	0.5798	0.5219
0.172	12.73	5500	0.5775	0.5009
0.172	12.96	5600	0.5782	0.5044
0.172	13.19	5700	0.5804	0.5184
0.172	13.43	5800	0.5977	0.5219
0.172	13.66	5900	0.6069	0.5236
0.1622	13.89	6000	0.5850	0.5131
0.1622	14.12	6100	0.5758	0.5096
0.1622	14.35	6200	0.5752	0.5009
0.1622	14.58	6300	0.5727	0.5184
0.1622	14.81	6400	0.5795	0.5044

Framework versions

Transformers 4.19.0.dev0
Pytorch 1.10.0+cu113
Datasets 2.1.0
Tokenizers 0.12.1

birgermoell/wav2vec2-liepa-1-percent

wav2vec2-liepa-1-percent

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

birgermoell/
wav2vec2-liepa-1-percent