wav2vec2-base-finetune-vi-v3

foxxy-hm/wav2vec2-base-finetune-vi-v3

wav2vec2-base-finetune-vi-v3 is a automatic speech recognition model from foxxy-hm. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: cc-by-nc-4.0
Model type: wav2vec2
Access: Public
Created: Sep 5, 2023
Updated: Sep 6, 2023
SHA: dd0c1c76

Model card

From the Hugging Face model README

wav2vec2-base-finetune-vi-v3

This model is a fine-tuned version of nguyenvulebinh/wav2vec2-base-vietnamese-250h on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2019
Wer: 0.1347

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.3444	0.59	500	3.0138	0.9938
1.6923	1.19	1000	0.6549	0.3873
0.8371	1.78	1500	0.3958	0.2492
0.6161	2.37	2000	0.3272	0.2161
0.5443	2.97	2500	0.2871	0.1967
0.4361	3.56	3000	0.2928	0.1852
0.3889	4.15	3500	0.2957	0.1745
0.3701	4.74	4000	0.2611	0.1702
0.353	5.34	4500	0.2514	0.1685
0.3299	5.93	5000	0.2239	0.1612
0.3235	6.52	5500	0.2362	0.1673
0.3	7.12	6000	0.2267	0.1593
0.285	7.71	6500	0.2397	0.1570
0.2766	8.3	7000	0.2415	0.1538
0.2652	8.9	7500	0.2265	0.1552
0.2396	9.49	8000	0.2299	0.1516
0.2442	10.08	8500	0.2136	0.1538
0.242	10.68	9000	0.2120	0.1510
0.2149	11.27	9500	0.2250	0.1494
0.2186	11.86	10000	0.2077	0.1491
0.2157	12.46	10500	0.2198	0.1461
0.2154	13.05	11000	0.2048	0.1484
0.2115	13.64	11500	0.2209	0.1444
0.2072	14.23	12000	0.2014	0.1414
0.1974	14.83	12500	0.2256	0.1451
0.1888	15.42	13000	0.1931	0.1445
0.1802	16.01	13500	0.2089	0.1452
0.1747	16.61	14000	0.2136	0.1441
0.1585	17.2	14500	0.2102	0.1411
0.1593	17.79	15000	0.1941	0.1413
0.1662	18.39	15500	0.2046	0.1426
0.165	18.98	16000	0.2077	0.1432
0.1696	19.57	16500	0.2062	0.1406
0.1499	20.17	17000	0.1997	0.1406
0.1459	20.76	17500	0.2063	0.1418
0.1381	21.35	18000	0.2040	0.1374
0.1504	21.95	18500	0.2013	0.1387
0.14	22.54	19000	0.2030	0.1393
0.1395	23.13	19500	0.2019	0.1357
0.1265	23.72	20000	0.2023	0.1364
0.1425	24.32	20500	0.1995	0.1362
0.1196	24.91	21000	0.2033	0.1368
0.1251	25.5	21500	0.2013	0.1357
0.1175	26.1	22000	0.2056	0.1360
0.1278	26.69	22500	0.2026	0.1353
0.1223	27.28	23000	0.1985	0.1365
0.1195	27.88	23500	0.2004	0.1358
0.1167	28.47	24000	0.1956	0.1364
0.1167	29.06	24500	0.2004	0.1350
0.1143	29.66	25000	0.2019	0.1347

Framework versions

Transformers 4.30.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: cc-by-nc-4.0
Created: Sep 5, 2023
Updated: Sep 6, 2023