transformers pytorch wav2vec2 generated_from_trainer base_model:nguyenvulebinh/wav2vec2-base-vietnamese-250h license:cc-by-nc-4.0 endpoints_compatible region:us
---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.25_g1.0-0.05_10_0.004_40
  results: []
---

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# fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.25_g1.0-0.05_10_0.004_40

This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0608
- Wer: 0.1012

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 533.9511      | 0.94  | 50   | 256.7028        | 15.6243 |
| 337.9142      | 1.89  | 100  | 82.7848         | 0.9952  |
| 61.5907       | 2.83  | 150  | 23.0722         | 1.0     |
| 28.765        | 3.77  | 200  | 21.1465         | 1.0     |
| 27.4031       | 4.72  | 250  | 20.4765         | 1.0     |
| 26.4393       | 5.66  | 300  | 19.7334         | 1.0     |
| 25.6404       | 6.6   | 350  | 19.2024         | 1.0     |
| 24.6513       | 7.55  | 400  | 18.8785         | 1.0     |
| 24.1773       | 8.49  | 450  | 18.6720         | 1.0     |
| 24.4131       | 9.43  | 500  | 18.5339         | 1.0     |
| 24.0393       | 10.38 | 550  | 18.2257         | 1.0     |
| 23.0434       | 11.32 | 600  | 14.5791         | 0.7926  |
| 15.8664       | 12.26 | 650  | 7.8034          | 0.4329  |
| 9.3173        | 13.21 | 700  | 4.4018          | 0.2799  |
| 6.3563        | 14.15 | 750  | 3.2101          | 0.2358  |
| 5.0252        | 15.09 | 800  | 2.6043          | 0.2082  |
| 4.2585        | 16.04 | 850  | 2.2239          | 0.1825  |
| 3.7273        | 16.98 | 900  | 1.9758          | 0.1832  |
| 3.3365        | 17.92 | 950  | 1.7976          | 0.1559  |
| 3.1388        | 18.87 | 1000 | 1.6666          | 0.1595  |
| 2.8685        | 19.81 | 1050 | 1.5991          | 0.1669  |
| 2.6594        | 20.75 | 1100 | 1.4857          | 0.1446  |
| 2.6179        | 21.7  | 1150 | 1.4291          | 0.1417  |
| 2.5211        | 22.64 | 1200 | 1.4383          | 0.1519  |
| 2.3276        | 23.58 | 1250 | 1.3807          | 0.1406  |
| 2.1864        | 24.53 | 1300 | 1.3551          | 0.1362  |
| 2.2149        | 25.47 | 1350 | 1.2799          | 0.1335  |
| 2.0461        | 26.42 | 1400 | 1.2590          | 0.1269  |
| 2.0038        | 27.36 | 1450 | 1.2624          | 0.1343  |
| 1.9107        | 28.3  | 1500 | 1.2001          | 0.1253  |
| 1.9049        | 29.25 | 1550 | 1.1936          | 0.1251  |
| 1.8057        | 30.19 | 1600 | 1.1986          | 0.1314  |
| 1.7086        | 31.13 | 1650 | 1.1642          | 0.1225  |
| 1.7464        | 32.08 | 1700 | 1.1177          | 0.1195  |
| 1.6634        | 33.02 | 1750 | 1.1247          | 0.1140  |
| 1.6189        | 33.96 | 1800 | 1.1151          | 0.1114  |
| 1.5062        | 34.91 | 1850 | 1.1218          | 0.1118  |
| 1.5323        | 35.85 | 1900 | 1.0949          | 0.1062  |
| 1.5779        | 36.79 | 1950 | 1.0786          | 0.1104  |
| 1.4826        | 37.74 | 2000 | 1.0774          | 0.1175  |
| 1.5034        | 38.68 | 2050 | 1.0891          | 0.1187  |
| 1.4051        | 39.62 | 2100 | 1.0873          | 0.1229  |
| 1.4084        | 40.57 | 2150 | 1.0893          | 0.1147  |
| 1.3231        | 41.51 | 2200 | 1.0818          | 0.1086  |
| 1.3182        | 42.45 | 2250 | 1.0795          | 0.1145  |
| 1.2747        | 43.4  | 2300 | 1.0833          | 0.1109  |
| 1.2657        | 44.34 | 2350 | 1.0797          | 0.1095  |
| 1.2867        | 45.28 | 2400 | 1.0753          | 0.1053  |
| 1.2034        | 46.23 | 2450 | 1.0782          | 0.1070  |
| 1.1649        | 47.17 | 2500 | 1.0685          | 0.1034  |
| 1.1314        | 48.11 | 2550 | 1.0622          | 0.0979  |
| 1.158         | 49.06 | 2600 | 1.0880          | 0.1032  |
| 1.0918        | 50.0  | 2650 | 1.0677          | 0.1022  |
| 1.0786        | 50.94 | 2700 | 1.0708          | 0.0980  |
| 1.1275        | 51.89 | 2750 | 1.0576          | 0.0989  |
| 0.9832        | 52.83 | 2800 | 1.0594          | 0.1048  |
| 1.0832        | 53.77 | 2850 | 1.0528          | 0.1026  |
| 1.0483        | 54.72 | 2900 | 1.0524          | 0.1072  |
| 0.9776        | 55.66 | 2950 | 1.0491          | 0.1049  |
| 0.972         | 56.6  | 3000 | 1.0471          | 0.1064  |
| 1.0257        | 57.55 | 3050 | 1.0680          | 0.1104  |
| 0.9965        | 58.49 | 3100 | 1.0723          | 0.1157  |
| 0.961         | 59.43 | 3150 | 1.0600          | 0.1040  |
| 0.9893        | 60.38 | 3200 | 1.0720          | 0.1123  |
| 0.8888        | 61.32 | 3250 | 1.0598          | 0.1060  |
| 0.9583        | 62.26 | 3300 | 1.0703          | 0.1057  |
| 0.8763        | 63.21 | 3350 | 1.0754          | 0.1114  |
| 0.9151        | 64.15 | 3400 | 1.0769          | 0.1045  |
| 0.8981        | 65.09 | 3450 | 1.0714          | 0.1014  |
| 0.8937        | 66.04 | 3500 | 1.0753          | 0.1049  |
| 0.8897        | 66.98 | 3550 | 1.0775          | 0.1071  |
| 0.8903        | 67.92 | 3600 | 1.0775          | 0.1039  |
| 0.8655        | 68.87 | 3650 | 1.0844          | 0.1026  |
| 0.8845        | 69.81 | 3700 | 1.0831          | 0.1020  |
| 0.854         | 70.75 | 3750 | 1.0877          | 0.1022  |
| 0.8797        | 71.7  | 3800 | 1.0739          | 0.1007  |
| 0.8357        | 72.64 | 3850 | 1.0680          | 0.1018  |
| 0.822         | 73.58 | 3900 | 1.0628          | 0.1026  |
| 0.7696        | 74.53 | 3950 | 1.0585          | 0.0995  |
| 0.8735        | 75.47 | 4000 | 1.0596          | 0.0994  |
| 0.8212        | 76.42 | 4050 | 1.0574          | 0.0978  |
| 0.8341        | 77.36 | 4100 | 1.0632          | 0.0998  |
| 0.8275        | 78.3  | 4150 | 1.0620          | 0.1019  |
| 0.7727        | 79.25 | 4200 | 1.0592          | 0.1002  |
| 0.8182        | 80.19 | 4250 | 1.0523          | 0.0969  |
| 0.813         | 81.13 | 4300 | 1.0672          | 0.1039  |
| 0.7961        | 82.08 | 4350 | 1.0689          | 0.1014  |
| 0.7956        | 83.02 | 4400 | 1.0666          | 0.0999  |
| 0.7853        | 83.96 | 4450 | 1.0648          | 0.1001  |
| 0.8167        | 84.91 | 4500 | 1.0634          | 0.0986  |
| 0.779         | 85.85 | 4550 | 1.0633          | 0.1012  |
| 0.8055        | 86.79 | 4600 | 1.0583          | 0.1004  |
| 0.7847        | 87.74 | 4650 | 1.0618          | 0.1016  |
| 0.7961        | 88.68 | 4700 | 1.0615          | 0.1016  |
| 0.7911        | 89.62 | 4750 | 1.0586          | 0.1017  |
| 0.7098        | 90.57 | 4800 | 1.0613          | 0.1031  |
| 0.8353        | 91.51 | 4850 | 1.0596          | 0.1015  |
| 0.7127        | 92.45 | 4900 | 1.0582          | 0.1012  |
| 0.824         | 93.4  | 4950 | 1.0597          | 0.1009  |
| 0.762         | 94.34 | 5000 | 1.0587          | 0.1014  |
| 0.7424        | 95.28 | 5050 | 1.0596          | 0.1013  |
| 0.7701        | 96.23 | 5100 | 1.0605          | 0.1011  |
| 0.7544        | 97.17 | 5150 | 1.0609          | 0.1014  |
| 0.7844        | 98.11 | 5200 | 1.0606          | 0.1014  |
| 0.7769        | 99.06 | 5250 | 1.0607          | 0.1012  |
| 0.7914        | 100.0 | 5300 | 1.0608          | 0.1012  |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1
