xtreme_s_xlsr_300m_voxpopuli_en

anton-l/xtreme_s_xlsr_300m_voxpopuli_en

xtreme_s_xlsr_300m_voxpopuli_en is a automatic speech recognition model from anton-l. 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. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Apr 29, 2022
Updated: May 3, 2022
SHA: ee399968

Datasets

google/xtreme_s

Model card

From the Hugging Face model README

xtreme_s_xlsr_300m_voxpopuli_en

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - VOXPOPULI.EN dataset. It achieves the following results on the evaluation set:

Cer: 0.0966
Loss: 0.3127
Wer: 0.1549
Predict Samples: 1842

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: 8
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.4221	0.19	500	1.3325	0.8224	0.3432
0.8429	0.38	1000	0.7087	0.5028	0.2023
0.7377	0.57	1500	0.4900	0.2778	0.1339
0.5641	0.77	2000	0.4460	0.2540	0.1284
0.5787	0.96	2500	0.4242	0.2148	0.1167
0.3465	1.15	3000	0.4210	0.2087	0.1154
0.2787	1.34	3500	0.3954	0.2090	0.1155
0.2775	1.53	4000	0.3938	0.1992	0.1133
0.262	1.72	4500	0.3748	0.2104	0.1151
0.3138	1.92	5000	0.3825	0.1993	0.1134
0.4331	2.11	5500	0.3648	0.1935	0.1104
0.3802	2.3	6000	0.3966	0.1910	0.1109
0.3928	2.49	6500	0.3995	0.1898	0.1100
0.3441	2.68	7000	0.3764	0.1887	0.1103
0.3673	2.87	7500	0.3800	0.1843	0.1086
0.3422	3.07	8000	0.3932	0.1830	0.1092
0.2933	3.26	8500	0.3672	0.1915	0.1104
0.1785	3.45	9000	0.3820	0.1796	0.1072
0.321	3.64	9500	0.3533	0.1994	0.1126
0.1673	3.83	10000	0.3683	0.1856	0.1084
0.1757	4.02	10500	0.3365	0.1925	0.1102
0.1881	4.22	11000	0.3528	0.1775	0.1066
0.3106	4.41	11500	0.3909	0.1754	0.1063
0.25	4.6	12000	0.3734	0.1723	0.1052
0.2005	4.79	12500	0.3358	0.1900	0.1092
0.2982	4.98	13000	0.3513	0.1766	0.1060
0.1552	5.17	13500	0.3720	0.1729	0.1059
0.1645	5.37	14000	0.3569	0.1713	0.1044
0.2065	5.56	14500	0.3639	0.1720	0.1048
0.1898	5.75	15000	0.3660	0.1726	0.1050
0.1397	5.94	15500	0.3731	0.1670	0.1033
0.2056	6.13	16000	0.3782	0.1650	0.1030
0.1859	6.32	16500	0.3903	0.1667	0.1033
0.1374	6.52	17000	0.3721	0.1736	0.1048
0.2482	6.71	17500	0.3899	0.1643	0.1023
0.159	6.9	18000	0.3847	0.1687	0.1032
0.1487	7.09	18500	0.3817	0.1671	0.1030
0.1942	7.28	19000	0.4120	0.1616	0.1018
0.1517	7.47	19500	0.3856	0.1635	0.1020
0.0946	7.67	20000	0.3838	0.1621	0.1016
0.1455	7.86	20500	0.3749	0.1652	0.1020
0.1303	8.05	21000	0.4074	0.1615	0.1011
0.1207	8.24	21500	0.4121	0.1606	0.1008
0.0727	8.43	22000	0.3948	0.1607	0.1009
0.1123	8.62	22500	0.4025	0.1603	0.1009
0.1606	8.82	23000	0.3963	0.1580	0.1004
0.1458	9.01	23500	0.3991	0.1574	0.1002
0.2286	9.2	24000	0.4149	0.1596	0.1009
0.1284	9.39	24500	0.4251	0.1572	0.1002
0.1141	9.58	25000	0.4264	0.1579	0.1002
0.1823	9.77	25500	0.4230	0.1562	0.0999
0.2514	9.97	26000	0.4242	0.1564	0.0999

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.10.1+cu111
Datasets 1.18.4.dev0
Tokenizers 0.11.6

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Languages: en
Created: Apr 29, 2022
Updated: May 3, 2022