metadata

license: cc-by-nc-4.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-base-finetune-vi-v2
    results: []
widget:
  - example_title: SOICT 2023 - SLU public test 1
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/055R7BruAa333g9teFfamQH.wav
  - example_title: SOICT 2023 - SLU public test 2
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/0BLHhoJexE8THB8BrsZxWbh.wav
  - example_title: SOICT 2023 - SLU public test 3
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/1ArUTGWJQ9YALH2xaNhU6GV.wav

wav2vec2-base-finetune-vi-v2

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: 0.2188
Wer: 0.1391

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: 24

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.3873	0.67	500	2.4321	0.9719
1.4812	1.34	1000	0.5449	0.3062
0.7731	2.0	1500	0.3793	0.2263
0.542	2.67	2000	0.3021	0.2002
0.4461	3.34	2500	0.2905	0.1862
0.4175	4.01	3000	0.2687	0.1771
0.3878	4.67	3500	0.2958	0.1751
0.3373	5.34	4000	0.2713	0.1721
0.3046	6.01	4500	0.2505	0.1616
0.2933	6.68	5000	0.2561	0.1611
0.285	7.34	5500	0.2405	0.1617
0.2998	8.01	6000	0.2363	0.1578
0.2486	8.68	6500	0.2254	0.1570
0.2682	9.35	7000	0.2306	0.1547
0.2327	10.01	7500	0.2289	0.1537
0.2141	10.68	8000	0.2383	0.1499
0.2124	11.35	8500	0.2261	0.15
0.2156	12.02	9000	0.2142	0.1511
0.2082	12.68	9500	0.2386	0.1467
0.1814	13.35	10000	0.2301	0.1448
0.1836	14.02	10500	0.2302	0.1446
0.18	14.69	11000	0.2244	0.1445
0.1756	15.35	11500	0.2280	0.1439
0.1693	16.02	12000	0.2307	0.1426
0.1588	16.69	12500	0.2164	0.1422
0.1587	17.36	13000	0.2198	0.1417
0.1738	18.02	13500	0.2282	0.1411
0.1524	18.69	14000	0.2274	0.1394
0.1569	19.36	14500	0.2178	0.1396
0.1433	20.03	15000	0.2200	0.1413
0.1512	20.69	15500	0.2193	0.1382
0.1375	21.36	16000	0.2174	0.1393
0.1302	22.03	16500	0.2246	0.1391
0.146	22.7	17000	0.2222	0.1392
0.1265	23.36	17500	0.2188	0.1391

Framework versions

Transformers 4.30.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3