wav2vec2-base-MIR_ST500_ASR_109

gary109/wav2vec2-base-MIR_ST500_ASR_109

wav2vec2-base-MIR_ST500_ASR_109 is a automatic speech recognition model from gary109. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

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

Downloads · 30 days

2% of all-time downloads

All-time downloads

422

Public

Repo size

1.1 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin378 MB · 99%

At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Apr 15, 2022
Updated: Apr 15, 2022
SHA: c307e8fc

Datasets

mir_st500

Model card

From the Hugging Face model README

wav2vec2-base-MIR_ST500_ASR_109

This model is a fine-tuned version of facebook/wav2vec2-base on the /WORKSPACE/DATASETS/DATASETS/MIR_ST500/MIR_ST500.PY - ASR dataset. It achieves the following results on the evaluation set:

Loss: 0.6452
Wer: 0.3732

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 16
total_eval_batch_size: 16
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
12.5751	0.27	100	6.0291	1.0
4.343	0.53	200	2.8709	1.0
4.1911	0.8	300	2.5472	1.0
2.4535	1.06	400	2.4323	1.0
2.6157	1.33	500	2.2799	1.0
2.4839	1.6	600	2.2722	1.0
2.2787	1.86	700	2.2269	1.0
2.1981	2.13	800	2.2221	1.0
2.159	2.39	900	2.1657	1.0
2.1421	2.66	1000	2.1769	1.0
2.0841	2.93	1100	2.1688	1.0
2.0599	3.19	1200	2.1141	1.0
2.0257	3.46	1300	2.0445	1.0
1.979	3.72	1400	2.0180	1.0
1.9366	3.99	1500	1.9419	1.0
1.8547	4.26	1600	1.8765	1.0
1.3988	4.52	1700	1.4151	0.7999
1.1881	4.79	1800	1.1158	0.7347
0.9557	5.05	1900	1.0095	0.6485
0.9087	5.32	2000	0.9644	0.6848
0.8086	5.59	2100	0.8960	0.6119
0.9106	5.85	2200	0.8892	0.5941
0.8252	6.12	2300	0.8333	0.5756
0.8299	6.38	2400	0.8559	0.5838
0.8021	6.65	2500	0.8201	0.5883
0.7979	6.91	2600	0.8349	0.575
0.7223	7.18	2700	0.7883	0.5563
0.6754	7.45	2800	0.7590	0.5393
0.6454	7.71	2900	0.7411	0.5291
0.6228	7.98	3000	0.7464	0.5300
0.6475	8.24	3100	0.7478	0.5295
0.6452	8.51	3200	0.7555	0.5360
0.5636	8.78	3300	0.7369	0.5232
0.564	9.04	3400	0.7331	0.5076
0.6173	9.31	3500	0.7199	0.5034
0.625	9.57	3600	0.7243	0.5193
0.8122	9.84	3700	0.7436	0.5242
0.5455	10.11	3800	0.7111	0.4920
0.7928	10.37	3900	0.7137	0.4858
0.5446	10.64	4000	0.6874	0.4828
0.4772	10.9	4100	0.6760	0.4801
0.6447	11.17	4200	0.6893	0.4886
0.5818	11.44	4300	0.6789	0.4740
0.4952	11.7	4400	0.7043	0.4811
0.5722	11.97	4500	0.6794	0.4766
0.58	12.23	4600	0.6629	0.4580
0.5432	12.5	4700	0.6907	0.4906
0.4786	12.77	4800	0.6925	0.4854
0.5177	13.03	4900	0.6666	0.4532
0.5448	13.3	5000	0.6744	0.4542
0.5732	13.56	5100	0.6930	0.4986
0.5065	13.83	5200	0.6647	0.4351
0.4005	14.1	5300	0.6659	0.4508
0.4256	14.36	5400	0.6682	0.4533
0.4459	14.63	5500	0.6594	0.4326
0.4645	14.89	5600	0.6615	0.4287
0.4275	15.16	5700	0.6423	0.4299
0.4026	15.43	5800	0.6539	0.4217
0.3507	15.69	5900	0.6555	0.4299
0.3998	15.96	6000	0.6526	0.4213
0.4462	16.22	6100	0.6469	0.4230
0.4095	16.49	6200	0.6516	0.4210
0.4452	16.76	6300	0.6373	0.4133
0.3997	17.02	6400	0.6456	0.4211
0.3826	17.29	6500	0.6278	0.4042
0.3867	17.55	6600	0.6459	0.4112
0.4367	17.82	6700	0.6464	0.4131
0.3887	18.09	6800	0.6567	0.4150
0.3481	18.35	6900	0.6548	0.4145
0.4241	18.62	7000	0.6490	0.4123
0.3742	18.88	7100	0.6561	0.4135
0.423	19.15	7200	0.6498	0.4051
0.3803	19.41	7300	0.6475	0.3903
0.3084	19.68	7400	0.6403	0.4042
0.3012	19.95	7500	0.6460	0.4004
0.3306	20.21	7600	0.6491	0.3837
0.3612	20.48	7700	0.6752	0.3884
0.3572	20.74	7800	0.6383	0.3793
0.3638	21.01	7900	0.6349	0.3838
0.3658	21.28	8000	0.6544	0.3793
0.3726	21.54	8100	0.6567	0.3756
0.3618	21.81	8200	0.6390	0.3795
0.3212	22.07	8300	0.6359	0.3768
0.3561	22.34	8400	0.6452	0.3732
0.3231	22.61	8500	0.6416	0.3731
0.3764	22.87	8600	0.6428	0.3697
0.4142	23.14	8700	0.6415	0.3665
0.2713	23.4	8800	0.6541	0.3676
0.2277	23.67	8900	0.6492	0.3684
0.3849	23.94	9000	0.6448	0.3651
0.266	24.2	9100	0.6602	0.3643
0.3464	24.47	9200	0.6673	0.3607
0.2919	24.73	9300	0.6557	0.3677
0.2878	25.0	9400	0.6377	0.3653
0.1603	25.27	9500	0.6598	0.3700
0.2055	25.53	9600	0.6558	0.3614
0.1508	25.8	9700	0.6543	0.3605
0.3162	26.06	9800	0.6570	0.3576
0.2613	26.33	9900	0.6604	0.3584
0.2244	26.6	10000	0.6618	0.3634
0.1585	26.86	10100	0.6698	0.3634
0.2959	27.13	10200	0.6709	0.3593
0.2778	27.39	10300	0.6638	0.3537
0.2354	27.66	10400	0.6770	0.3585
0.2992	27.93	10500	0.6698	0.3506
0.2664	28.19	10600	0.6725	0.3533
0.2582	28.46	10700	0.6689	0.3542
0.2096	28.72	10800	0.6731	0.3527
0.4169	28.99	10900	0.6691	0.3521
0.2716	29.26	11000	0.6712	0.3517
0.2944	29.52	11100	0.6708	0.3509
0.2737	29.79	11200	0.6699	0.3491

Framework versions

Transformers 4.18.0
Pytorch 1.9.1+cu102
Datasets 2.0.0
Tokenizers 0.11.6

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Apr 15, 2022
Updated: Apr 15, 2022