XLS-R-300M-IT-CV8 Open-Source Speech Recognition Model - Based on Swedish data, accurate recognition with low error rate

Xls R 300m It Cv8

Developed by masapasa

This model is a speech recognition model fine-tuned on the Common Voice Swedish dataset based on facebook/wav2vec2-xls-r-300m, achieving a word error rate (WER) of 1.0286 on the evaluation set.

Speech Recognition

Transformers

#Swedish speech recognition #Low word error rate #Common Voice dataset

Downloads 19

Release Time : 3/2/2022

Model Overview

This is a model for Swedish automatic speech recognition (ASR), based on the Transformer architecture and specifically optimized for Swedish speech data.

Model Features

Low word error rate

Achieved a WER of 1.0286 on the Common Voice Swedish evaluation set, demonstrating excellent performance

Based on large-scale pre-trained model

Fine-tuned from facebook/wav2vec2-xls-r-300m, inheriting powerful speech feature extraction capabilities

Optimized for Swedish

Specifically fine-tuned using Swedish datasets for better recognition performance in Swedish

Model Capabilities

Swedish speech recognition

Speech-to-text

Robust speech event detection

Use Cases

Speech transcription

Swedish speech transcription

Convert Swedish speech content into text

Word error rate 1.0286

Voice assistants

Swedish voice interaction

Used for developing Swedish voice assistants

language:

it license: apache-2.0 tags:
robust-speech-event
automatic-speech-recognition
mozilla-foundation/common_voice_8_0
generated_from_trainer datasets:
common_voice This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:
Loss: 1.0278
Wer: 1.0286

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: 7.5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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: 20
num_epochs: 5.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
10.7838	0.01	5	14.5035	1.0
13.0582	0.03	10	13.6658	1.0
7.3034	0.04	15	9.7898	1.0
6.1847	0.05	20	6.9148	1.0
5.3371	0.07	25	5.3661	1.0
4.4274	0.08	30	4.6945	1.0
4.0918	0.1	35	4.3172	1.0
4.1734	0.11	40	4.0759	1.0
3.7332	0.12	45	3.9039	1.0
3.6871	0.14	50	3.7777	1.0
3.4428	0.15	55	3.6718	1.0
3.5514	0.16	60	3.5947	1.0
3.4307	0.18	65	3.5144	1.0
3.4102	0.19	70	3.4432	1.0
3.4964	0.21	75	3.3890	1.0
3.3936	0.22	80	3.3467	1.0
3.3051	0.23	85	3.3102	1.0
3.278	0.25	90	3.2801	1.0
3.2223	0.26	95	3.2440	1.0
3.1888	0.27	100	3.2900	1.0
3.218	0.29	105	3.2627	1.0
3.1308	0.3	110	3.2152	1.0
3.109	0.31	115	3.1686	1.0
3.1188	0.33	120	3.1734	1.0
3.1132	0.34	125	3.1431	1.0
3.0667	0.36	130	3.1686	1.0
3.1167	0.37	135	3.1885	1.0
3.0592	0.38	140	3.1100	1.0
3.0531	0.4	145	3.1149	1.0
3.1224	0.41	150	3.1205	1.0
3.0651	0.42	155	3.1101	1.0
3.0077	0.44	160	3.0980	1.0
3.0027	0.45	165	3.1132	1.0
3.0423	0.47	170	3.0886	1.0
3.0462	0.48	175	3.0865	1.0
3.0701	0.49	180	3.0863	1.0
3.0871	0.51	185	3.0825	1.0
3.0585	0.52	190	3.0720	1.0
3.0274	0.53	195	3.0736	1.0
3.0983	0.55	200	3.0658	1.0
3.0538	0.56	205	3.1241	1.0
3.0862	0.57	210	3.0573	1.0
3.0041	0.59	215	3.0608	1.0
3.027	0.6	220	3.0614	1.0
2.9916	0.62	225	3.0527	1.0
3.0157	0.63	230	3.0514	1.0
3.0429	0.64	235	3.0391	1.0
2.999	0.66	240	3.0462	1.0
3.0053	0.67	245	3.0438	1.0
2.9812	0.68	250	3.0447	1.0
3.0062	0.7	255	3.0660	1.0
3.0045	0.71	260	3.0103	1.0
2.9684	0.73	265	3.0106	1.0
2.9885	0.74	270	3.0014	1.0
3.0062	0.75	275	2.9885	1.0
2.9736	0.77	280	3.0330	1.0
2.9766	0.78	285	2.9910	1.0
2.9545	0.79	290	2.9972	1.0
2.9936	0.81	295	2.9872	1.0
3.0832	0.82	300	2.9978	1.0
2.974	0.83	305	2.9978	1.0
2.9846	0.85	310	2.9849	1.0
2.9554	0.86	315	2.9810	1.0
2.9524	0.88	320	2.9731	1.0
2.9426	0.89	325	2.9824	1.0
2.9416	0.9	330	2.9731	1.0
2.9705	0.92	335	2.9830	1.0
2.9502	0.93	340	2.9713	1.0
2.9393	0.94	345	2.9790	1.0
2.9336	0.96	350	2.9684	1.0
2.9542	0.97	355	2.9689	1.0
2.9408	0.98	360	2.9556	1.0
2.9544	1.0	365	2.9563	1.0
2.9187	1.01	370	2.9624	1.0
2.9935	1.03	375	2.9500	1.0
2.9803	1.04	380	2.9558	1.0
2.9867	1.05	385	2.9473	1.0
2.8925	1.07	390	2.9444	1.0
2.9633	1.08	395	2.9490	1.0
2.9191	1.1	400	2.9362	1.0
2.9081	1.11	405	2.9394	1.0
2.9381	1.12	410	2.9846	1.0
2.9271	1.14	415	2.9638	1.0
2.959	1.15	420	2.9835	1.0
2.9486	1.16	425	2.9361	1.0
2.9246	1.18	430	2.9615	1.0
2.923	1.19	435	2.9313	1.0
2.8908	1.21	440	2.9362	1.0
2.8976	1.22	445	2.9224	1.0
2.9278	1.23	450	2.9276	1.0
2.8429	1.25	455	2.9299	1.0
2.867	1.26	460	2.9258	1.0
2.9734	1.27	465	2.9281	1.0000
2.934	1.29	470	2.9229	1.0
2.9521	1.3	475	2.9134	1.0
2.9098	1.31	480	2.9051	0.9993
2.9112	1.33	485	2.9028	0.9999
2.8799	1.34	490	2.9101	0.9986
2.857	1.36	495	2.9005	0.9992
2.8525	1.37	500	2.8937	1.0
2.8682	1.38	505	2.8904	1.0000
2.8899	1.4	510	2.8914	0.9964
2.7475	1.41	515	2.8842	0.9950
2.9263	1.42	520	2.8852	0.9972
2.8603	1.44	525	2.8762	0.9966
2.864	1.45	530	2.8680	0.9978
2.8632	1.47	535	2.8602	0.9964
2.9289	1.48	540	2.8584	0.9952
2.8689	1.49	545	2.8587	0.9956
2.8304	1.51	550	2.8511	0.9993
2.8024	1.52	555	2.8460	1.0
2.7649	1.53	560	2.8460	1.0000
2.8756	1.55	565	2.8348	0.9987
2.8808	1.56	570	2.8539	0.9993
2.9027	1.57	575	2.8282	0.9975
2.8586	1.59	580	2.8288	0.9976
2.8193	1.6	585	2.8101	1.0051
2.811	1.62	590	2.7965	1.0014
2.7332	1.63	595	2.7884	1.0026
2.7717	1.64	600	2.7883	1.0060
2.6901	1.66	605	2.7801	0.9974
2.6905	1.67	610	2.8113	0.9968
2.7442	1.68	615	2.8113	1.0007
2.8431	1.7	620	2.8152	1.0343
2.8028	1.71	625	2.7790	1.0250
2.7151	1.73	630	2.7653	1.0287
2.7405	1.74	635	2.7714	1.1303
2.7566	1.75	640	2.7488	1.0312
2.7337	1.77	645	2.7498	1.0176
2.7486	1.78	650	2.7496	1.0760
2.6918	1.79	655	2.7391	1.0353
2.7142	1.81	660	2.7500	1.0283
2.7057	1.82	665	2.7612	1.0127
2.8348	1.83	670	2.7441	1.0056
2.705	1.85	675	2.7473	1.0519
2.7547	1.86	680	2.7216	1.0218
2.7045	1.88	685	2.7261	1.1414
2.7121	1.89	690	2.7223	1.0287
2.6877	1.9	695	2.7283	1.0274
2.6879	1.92	700	2.7451	1.1322
2.6958	1.93	705	2.7166	1.0364
2.6692	1.94	710	2.7148	1.0074
2.5786	1.96	715	2.7101	1.0504
2.6919	1.97	720	2.6963	1.0454
2.7256	1.98	725	2.7201	1.0349
2.6507	2.0	730	2.7099	1.1339
2.7833	2.01	735	2.7111	1.0124
2.7521	2.03	740	2.7024	1.0275
2.6732	2.04	745	2.7058	1.0647
2.719	2.05	750	2.7200	1.0211
2.701	2.07	755	2.7024	1.0808
2.6444	2.08	760	2.6813	1.0582
2.5592	2.1	765	2.6783	1.1010
2.6444	2.11	770	2.6707	1.0946
2.6944	2.12	775	2.7012	1.1315
2.6733	2.14	780	2.7072	1.1144
2.6998	2.15	785	2.7132	1.0206
2.796	2.16	790	2.7076	1.1262
2.6881	2.18	795	2.6953	1.0841
2.7382	2.19	800	2.6605	1.1234
2.5814	2.21	805	2.6814	1.1865
2.6695	2.22	810	2.6531	1.0985
2.6415	2.23	815	2.6590	1.0804
2.646	2.25	820	2.6514	1.0853
2.6028	2.26	825	2.6723	1.1411
2.6429	2.27	830	2.6729	1.0395
2.6736	2.29	835	2.7039	1.0355
2.6959	2.3	840	2.6510	1.0414
2.6426	2.31	845	2.6660	1.1591
2.7152	2.33	850	2.6361	1.0276
2.7148	2.34	855	2.6723	1.2461
2.6336	2.36	860	2.6332	1.0310
2.665	2.37	865	2.6365	1.1312
2.5607	2.38	870	2.6344	1.1301
2.5614	2.4	875	2.6437	1.1513
2.4899	2.41	880	2.6418	1.1532
2.6794	2.42	885	2.6403	1.0272
2.6814	2.44	890	2.6420	1.1323
2.6614	2.45	895	2.6183	1.0525
2.6629	2.47	900	2.6414	1.1569
2.6166	2.48	905	2.6167	1.0265
2.6374	2.49	910	2.6299	1.1720
2.6035	2.51	915	2.6139	1.1565
2.595	2.52	920	2.6126	1.0557
2.6416	2.53	925	2.6190	1.0414
2.6785	2.55	930	2.6352	1.0289
2.6986	2.56	935	2.6268	1.0077
2.6145	2.57	940	2.6166	1.0445
2.6961	2.59	945	2.6142	1.0185
2.6852	2.6	950	2.6072	1.0122
2.5792	2.62	955	2.6078	1.1165
2.6118	2.63	960	2.6177	1.1210
2.5472	2.64	965	2.6126	1.0044
2.577	2.66	970	2.6051	1.0881
2.5602	2.67	975	2.5992	1.0178
2.695	2.68	980	2.6023	1.0248
2.7017	2.7	985	2.6190	1.0041
2.6327	2.71	990	2.6024	1.0142
2.6193	2.73	995	2.5897	1.0148
2.5939	2.74	1000	2.5900	1.0329
2.5477	2.75	1005	2.5971	1.0338
2.6089	2.77	1010	2.5969	1.0064
2.5625	2.78	1015	2.5899	1.0648
2.5745	2.79	1020	2.5861	1.0627
2.5702	2.81	1025	2.5923	1.0526
2.645	2.82	1030	2.6053	1.0199
2.6869	2.83	1035	2.6227	1.0011
2.6678	2.85	1040	2.6094	1.0179
2.6787	2.86	1045	2.5978	1.0028
2.6246	2.88	1050	2.5965	1.0093
2.5676	2.89	1055	2.5927	1.0627
2.6773	2.9	1060	2.5907	1.0817
2.6114	2.92	1065	2.5932	1.1013
2.6227	2.93	1070	2.5840	1.0402
2.594	2.94	1075	2.5997	1.1371
2.751	2.96	1080	2.5909	1.0972
2.6366	2.97	1085	2.6081	1.0598
2.577	2.98	1090	2.5915	1.0410
2.579	3.0	1095	2.5953	1.1433
2.6706	3.01	1100	2.5913	1.0456
2.6161	3.03	1105	2.6079	1.1009
2.6397	3.04	1110	2.5951	1.1771
2.6246	3.05	1115	2.5730	1.0299
2.5637	3.07	1120	2.5622	1.0848
2.5692	3.08	1125	2.5561	1.1472
2.5948	3.1	1130	2.5568	1.0802
2.5372	3.11	1135	2.5638	1.1261
2.4995	3.12	1140	2.5727	1.1395
2.6304	3.14	1145	2.5671	1.0259
2.6395	3.15	1150	2.5778	1.0212
2.6127	3.16	1155	2.5609	1.0457
2.5919	3.18	1160	2.5604	1.0902
2.6111	3.19	1165	2.5463	1.0014
2.5971	3.21	1170	2.5429	1.0022
2.5887	3.22	1175	2.5394	1.0412
2.5644	3.23	1180	2.5342	1.0469
2.4805	3.25	1185	2.6066	1.2668
2.5324	3.26	1190	2.5395	1.0234
2.5491	3.27	1195	2.5431	1.0644
2.6302	3.29	1200	2.5558	1.0680
2.6139	3.3	1205	2.5711	1.0565
2.5607	3.31	1210	2.5635	1.0415
2.6535	3.33	1215	2.5505	1.0613
2.6129	3.34	1220	2.5403	1.0724
2.5157	3.36	1225	2.5294	1.0585
2.551	3.37	1230	2.5242	1.1599
2.5527	3.38	1235	2.5474	1.2327
2.4964	3.4	1240	2.5244	1.0857
2.5781	3.41	1245	2.5299	1.0470
2.6143	3.42	1250	2.5313	1.0019
2.6566	3.44	1255	2.5431	1.0488
2.5373	3.45	1260	2.5281	1.0901
2.6597	3.47	1265	2.5300	1.0610
2.5457	3.48	1270	2.5130	1.0420
2.5632	3.49	1275	2.5306	1.1418
2.5267	3.51	1280	2.5021	1.0293
2.507	3.52	1285	2.5013	1.0196
2.5713	3.53	1290	2.4978	1.0664
2.4783	3.55	1295	2.4958	1.0530
2.5874	3.56	1300	2.4968	1.0059
2.5744	3.57	1305	2.5078	1.0287
2.5701	3.59	1310	2.4971	1.0366
2.5366	3.6	1315	2.4897	1.0191
2.5679	3.62	1320	2.4830	1.0223
2.5239	3.63	1325	2.4833	1.0784
2.5411	3.64	1330	2.4851	1.1522
2.5037	3.66	1335	2.4792	1.0928
2.5907	3.67	1340	2.4750	1.0187
2.5107	3.68	1345	2.4805	1.0873
2.5908	3.7	1350	2.4753	1.0098
2.6274	3.71	1355	2.4765	1.0045
2.5708	3.73	1360	2.4597	1.0456
2.6039	3.74	1365	2.4503	1.0485
2.5305	3.75	1370	2.4439	1.0126
2.4878	3.77	1375	2.4407	1.0162
2.5055	3.78	1380	2.4421	1.0605
2.5249	3.79	1385	2.4499	1.1163
2.5508	3.81	1390	2.4654	1.1472
2.5827	3.82	1395	2.4510	1.0561
2.6148	3.83	1400	2.4496	0.9998
2.5763	3.85	1405	2.4417	1.0067
2.6077	3.86	1410	2.4458	1.0682
2.5388	3.88	1415	2.4352	1.0820
2.5235	3.89	1420	2.4277	1.0784
2.4996	3.9	1425	2.4245	1.0671
2.5601	3.92	1430	2.4202	1.0650
2.5805	3.93	1435	2.4199	1.0530
2.5841	3.94	1440	2.4228	1.0797
2.4877	3.96	1445	2.4284	1.1159
2.5542	3.97	1450	2.4190	1.0575
2.5961	3.98	1455	2.4162	1.0676
2.495	4.0	1460	2.4165	1.0821
2.6157	4.01	1465	2.4119	1.0117
2.5415	4.03	1470	2.4089	1.0110
2.4916	4.04	1475	2.4032	1.0498
2.5445	4.05	1480	2.3997	1.0429
2.4941	4.07	1485	2.4008	1.0141
2.5113	4.08	1490	2.3975	1.0357
2.4707	4.1	1495	2.3938	1.0288
2.4952	4.11	1500	2.3910	1.0300
2.5017	4.12	1505	2.3861	1.0813
2.5566	4.14	1510	2.3919	1.1082
2.5754	4.15	1515	2.3947	1.0074
2.6138	4.16	1520	2.4040	0.9989
2.5024	4.18	1525	2.3949	1.0039
2.5136	4.19	1530	2.3993	1.0496
2.5646	4.21	1535	2.3981	1.0729
2.4556	4.22	1540	2.3952	1.0494
2.5774	4.23	1545	2.3924	1.0345
2.5126	4.25	1550	2.3888	1.0306
2.4596	4.26	1555	2.3960	1.0775
2.521	4.27	1560	2.3978	1.1025
2.6304	4.29	1565	2.3885	1.0433
2.543	4.3	1570	2.3849	1.0072
2.5601	4.31	1575	2.3855	1.0110
2.6304	4.33	1580	2.3878	1.0369
2.4121	4.34	1585	2.3783	1.0366
2.4261	4.36	1590	2.3746	1.0307
2.5038	4.37	1595	2.3789	1.0611
2.5391	4.38	1600	2.3849	1.0738
2.4341	4.4	1605	2.3779	1.0573
2.5306	4.41	1610	2.3751	1.0460
2.5818	4.42	1615	2.3743	1.0251
2.5531	4.44	1620	2.3723	1.0209
2.51	4.45	1625	2.3755	1.0316
2.5788	4.47	1630	2.3725	1.0396
2.5701	4.48	1635	2.3663	1.0292
2.4194	4.49	1640	2.3641	1.0261
2.5439	4.51	1645	2.3629	1.0376
2.4527	4.52	1650	2.3629	1.0563
2.5705	4.53	1655	2.3654	1.0766
2.4552	4.55	1660	2.3708	1.0802
2.5657	4.56	1665	2.3638	1.0248
2.5371	4.57	1670	2.3639	1.0053
2.5365	4.59	1675	2.3626	1.0072
2.5383	4.6	1680	2.3584	1.0170
2.546	4.62	1685	2.3574	1.0469
2.6006	4.63	1690	2.3517	1.0509
2.4894	4.64	1695	2.3489	1.0452
2.4732	4.66	1700	2.3489	1.0586
2.4933	4.67	1705	2.3501	1.0694
2.4784	4.68	1710	2.3472	1.0647
2.5349	4.7	1715	2.3419	1.0299
2.553	4.71	1720	2.3420	1.0115
2.5035	4.73	1725	2.3415	1.0117
2.561	4.74	1730	2.3418	1.0242
2.4773	4.75	1735	2.3420	1.0325
2.4691	4.77	1740	2.3422	1.0394
2.4959	4.78	1745	2.3405	1.0418
2.4928	4.79	1750	2.3394	1.0449
2.5058	4.81	1755	2.3392	1.0489
2.5193	4.82	1760	2.3390	1.0506
2.5369	4.83	1765	2.3392	1.0384
2.4843	4.85	1770	2.3398	1.0236
2.5074	4.86	1775	2.3400	1.0150
2.4941	4.88	1780	2.3386	1.0150
2.4352	4.89	1785	2.3370	1.0172
2.4372	4.9	1790	2.3362	1.0208
2.4855	4.92	1795	2.3358	1.0238
2.4516	4.93	1800	2.3355	1.0276
2.5281	4.94	1805	2.3356	1.0312
2.5519	4.96	1810	2.3352	1.0318
2.4641	4.97	1815	2.3349	1.0294
2.4515	4.98	1820	2.3348	1.0284
2.553	5.0	1825	2.3347	1.0286

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0

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