hubert-base-libri-clean-ft100h-v3

danieleV9H/hubert-base-libri-clean-ft100h-v3

hubert-base-libri-clean-ft100h-v3 is a automatic speech recognition model from danieleV9H. 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: hubert
Access: Public
Created: May 22, 2022
Updated: May 26, 2022
SHA: f76c1e26

Datasets

librispeech_asr

Model card

From the Hugging Face model README

hubert-base-libri-clean-ft100h-v3

This model is a fine-tuned version of facebook/hubert-base-ls960 on the librispeech_asr dataset. It achieves the following results on the evaluation set:

Loss: 0.1120
Wer: 0.1332

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: 5e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 600
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.201	0.14	250	3.9799	1.0
2.8893	0.28	500	3.4838	1.0
2.8603	0.42	750	3.3505	1.0
2.7216	0.56	1000	2.1194	0.9989
1.3372	0.7	1250	0.8124	0.6574
0.8238	0.84	1500	0.5712	0.5257
0.6449	0.98	1750	0.4442	0.4428
0.5241	1.12	2000	0.3442	0.3672
0.4458	1.26	2250	0.2850	0.3186
0.3959	1.4	2500	0.2507	0.2882
0.3641	1.54	2750	0.2257	0.2637
0.3307	1.68	3000	0.2044	0.2434
0.2996	1.82	3250	0.1969	0.2313
0.2794	1.96	3500	0.1823	0.2193
0.2596	2.1	3750	0.1717	0.2096
0.2563	2.24	4000	0.1653	0.2000
0.2532	2.38	4250	0.1615	0.1971
0.2376	2.52	4500	0.1559	0.1916
0.2341	2.66	4750	0.1494	0.1855
0.2102	2.8	5000	0.1464	0.1781
0.2222	2.94	5250	0.1399	0.1732
0.2081	3.08	5500	0.1450	0.1707
0.1963	3.22	5750	0.1337	0.1655
0.2107	3.36	6000	0.1344	0.1633
0.1866	3.5	6250	0.1339	0.1611
0.186	3.64	6500	0.1311	0.1563
0.1703	3.78	6750	0.1307	0.1537
0.1819	3.92	7000	0.1277	0.1555
0.176	4.06	7250	0.1280	0.1515
0.1837	4.2	7500	0.1249	0.1504
0.1678	4.34	7750	0.1236	0.1480
0.1624	4.48	8000	0.1194	0.1456
0.1631	4.62	8250	0.1215	0.1462
0.1736	4.76	8500	0.1192	0.1451
0.1752	4.9	8750	0.1206	0.1432
0.1578	5.04	9000	0.1151	0.1415
0.1537	5.18	9250	0.1185	0.1402
0.1771	5.33	9500	0.1165	0.1414
0.1481	5.47	9750	0.1152	0.1413
0.1509	5.61	10000	0.1152	0.1382
0.146	5.75	10250	0.1133	0.1385
0.1464	5.89	10500	0.1139	0.1371
0.1442	6.03	10750	0.1162	0.1365
0.128	6.17	11000	0.1147	0.1371
0.1381	6.31	11250	0.1148	0.1378
0.1343	6.45	11500	0.1113	0.1363
0.1325	6.59	11750	0.1134	0.1355
0.1442	6.73	12000	0.1142	0.1358
0.1286	6.87	12250	0.1133	0.1352
0.1349	7.01	12500	0.1129	0.1344
0.1338	7.15	12750	0.1131	0.1328
0.1403	7.29	13000	0.1124	0.1338
0.1314	7.43	13250	0.1141	0.1335
0.1283	7.57	13500	0.1124	0.1332
0.1347	7.71	13750	0.1107	0.1332
0.1195	7.85	14000	0.1119	0.1332
0.1326	7.99	14250	0.1120	0.1332

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 1.18.3
Tokenizers 0.12.1

Task: Automatic Speech Recognition
Library: transformers
Type: hubert
License: apache-2.0
Languages: en
Created: May 22, 2022
Updated: May 26, 2022