transformers safetensors wav2vec2 automatic-speech-recognition generated_from_trainer vi dataset:ducha07/audio_HTV_thoisu base_model:facebook/mms-1b-all license:cc-by-nc-4.0 model-index endpoints_compatible region:us
---
language:
- vi
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- ducha07/audio_HTV_thoisu
metrics:
- wer
model-index:
- name: ASR-test
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: HTV news
      type: ducha07/audio_HTV_thoisu
    metrics:
    - name: Wer
      type: wer
      value: 0.2796665364074508
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ASR-test-1

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the HTV news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6593
- Wer: 0.2797

## 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: 0.001
- train_batch_size: 32
- 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: 100
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.8562        | 0.92  | 100   | 0.8316          | 0.4500 |
| 1.0777        | 1.83  | 200   | 0.6898          | 0.3899 |
| 0.98          | 2.75  | 300   | 0.6811          | 0.3740 |
| 0.8967        | 3.67  | 400   | 0.6332          | 0.3565 |
| 0.8965        | 4.59  | 500   | 0.6038          | 0.3517 |
| 0.8396        | 5.5   | 600   | 0.6040          | 0.3479 |
| 0.8137        | 6.42  | 700   | 0.5929          | 0.3408 |
| 0.8304        | 7.34  | 800   | 0.5911          | 0.3513 |
| 0.7894        | 8.26  | 900   | 0.6078          | 0.3357 |
| 0.7412        | 9.17  | 1000  | 0.6214          | 0.3230 |
| 0.7653        | 10.09 | 1100  | 0.5869          | 0.3444 |
| 0.7437        | 11.01 | 1200  | 0.5906          | 0.3213 |
| 0.7083        | 11.93 | 1300  | 0.5952          | 0.3139 |
| 0.7168        | 12.84 | 1400  | 0.5721          | 0.3267 |
| 0.7008        | 13.76 | 1500  | 0.5895          | 0.3177 |
| 0.6825        | 14.68 | 1600  | 0.5909          | 0.3098 |
| 0.6989        | 15.6  | 1700  | 0.5979          | 0.3673 |
| 0.6717        | 16.51 | 1800  | 0.5863          | 0.3077 |
| 0.6496        | 17.43 | 1900  | 0.5798          | 0.3043 |
| 0.6609        | 18.35 | 2000  | 0.5787          | 0.3555 |
| 0.628         | 19.27 | 2100  | 0.5889          | 0.3133 |
| 0.6322        | 20.18 | 2200  | 0.5913          | 0.3077 |
| 0.634         | 21.1  | 2300  | 0.5769          | 0.3193 |
| 0.6172        | 22.02 | 2400  | 0.5731          | 0.3005 |
| 0.6043        | 22.94 | 2500  | 0.5820          | 0.3075 |
| 0.6051        | 23.85 | 2600  | 0.5831          | 0.3435 |
| 0.5865        | 24.77 | 2700  | 0.5790          | 0.3029 |
| 0.5806        | 25.69 | 2800  | 0.5945          | 0.3053 |
| 0.5901        | 26.61 | 2900  | 0.5780          | 0.3126 |
| 0.5769        | 27.52 | 3000  | 0.5732          | 0.2963 |
| 0.5539        | 28.44 | 3100  | 0.5837          | 0.2950 |
| 0.5799        | 29.36 | 3200  | 0.5835          | 0.3178 |
| 0.5518        | 30.28 | 3300  | 0.5941          | 0.2943 |
| 0.549         | 31.19 | 3400  | 0.5960          | 0.2979 |
| 0.5612        | 32.11 | 3500  | 0.5747          | 0.3167 |
| 0.5411        | 33.03 | 3600  | 0.5855          | 0.2978 |
| 0.536         | 33.94 | 3700  | 0.5720          | 0.2944 |
| 0.5329        | 34.86 | 3800  | 0.5998          | 0.3186 |
| 0.5185        | 35.78 | 3900  | 0.5936          | 0.2884 |
| 0.5186        | 36.7  | 4000  | 0.5773          | 0.2901 |
| 0.5027        | 37.61 | 4100  | 0.5969          | 0.3264 |
| 0.52          | 38.53 | 4200  | 0.6184          | 0.2939 |
| 0.4992        | 39.45 | 4300  | 0.5887          | 0.2943 |
| 0.5064        | 40.37 | 4400  | 0.5814          | 0.2966 |
| 0.4928        | 41.28 | 4500  | 0.6128          | 0.2902 |
| 0.508         | 42.2  | 4600  | 0.5943          | 0.2923 |
| 0.4887        | 43.12 | 4700  | 0.6100          | 0.3039 |
| 0.4872        | 44.04 | 4800  | 0.6044          | 0.2875 |
| 0.4711        | 44.95 | 4900  | 0.5961          | 0.2974 |
| 0.4813        | 45.87 | 5000  | 0.6022          | 0.2945 |
| 0.4818        | 46.79 | 5100  | 0.6199          | 0.2898 |
| 0.4492        | 47.71 | 5200  | 0.6161          | 0.2943 |
| 0.4715        | 48.62 | 5300  | 0.6038          | 0.2838 |
| 0.4601        | 49.54 | 5400  | 0.6223          | 0.2829 |
| 0.4432        | 50.46 | 5500  | 0.6058          | 0.2965 |
| 0.4419        | 51.38 | 5600  | 0.6134          | 0.2917 |
| 0.4564        | 52.29 | 5700  | 0.6124          | 0.2857 |
| 0.4349        | 53.21 | 5800  | 0.6229          | 0.2877 |
| 0.4358        | 54.13 | 5900  | 0.6095          | 0.2898 |
| 0.4432        | 55.05 | 6000  | 0.6365          | 0.2881 |
| 0.4277        | 55.96 | 6100  | 0.6169          | 0.2870 |
| 0.4397        | 56.88 | 6200  | 0.6174          | 0.2849 |
| 0.4245        | 57.8  | 6300  | 0.6340          | 0.2858 |
| 0.4203        | 58.72 | 6400  | 0.6321          | 0.2909 |
| 0.4112        | 59.63 | 6500  | 0.6243          | 0.2866 |
| 0.4244        | 60.55 | 6600  | 0.6318          | 0.2775 |
| 0.4119        | 61.47 | 6700  | 0.6215          | 0.2798 |
| 0.403         | 62.39 | 6800  | 0.6213          | 0.2829 |
| 0.4158        | 63.3  | 6900  | 0.6451          | 0.2795 |
| 0.3997        | 64.22 | 7000  | 0.6317          | 0.2854 |
| 0.4006        | 65.14 | 7100  | 0.6329          | 0.2846 |
| 0.4051        | 66.06 | 7200  | 0.6318          | 0.2834 |
| 0.3953        | 66.97 | 7300  | 0.6442          | 0.2855 |
| 0.4119        | 67.89 | 7400  | 0.6345          | 0.2893 |
| 0.3976        | 68.81 | 7500  | 0.6361          | 0.2798 |
| 0.3965        | 69.72 | 7600  | 0.6355          | 0.2853 |
| 0.3957        | 70.64 | 7700  | 0.6457          | 0.2814 |
| 0.3837        | 71.56 | 7800  | 0.6396          | 0.2855 |
| 0.3893        | 72.48 | 7900  | 0.6424          | 0.2842 |
| 0.3816        | 73.39 | 8000  | 0.6496          | 0.2778 |
| 0.3855        | 74.31 | 8100  | 0.6427          | 0.2881 |
| 0.3767        | 75.23 | 8200  | 0.6394          | 0.2858 |
| 0.3747        | 76.15 | 8300  | 0.6513          | 0.2844 |
| 0.3829        | 77.06 | 8400  | 0.6602          | 0.2775 |
| 0.3721        | 77.98 | 8500  | 0.6427          | 0.2825 |
| 0.3708        | 78.9  | 8600  | 0.6507          | 0.2847 |
| 0.3767        | 79.82 | 8700  | 0.6518          | 0.2816 |
| 0.3655        | 80.73 | 8800  | 0.6597          | 0.2802 |
| 0.3614        | 81.65 | 8900  | 0.6542          | 0.2781 |
| 0.3629        | 82.57 | 9000  | 0.6520          | 0.2782 |
| 0.3621        | 83.49 | 9100  | 0.6501          | 0.2797 |
| 0.3616        | 84.4  | 9200  | 0.6528          | 0.2777 |
| 0.3519        | 85.32 | 9300  | 0.6549          | 0.2798 |
| 0.3572        | 86.24 | 9400  | 0.6541          | 0.2789 |
| 0.3585        | 87.16 | 9500  | 0.6497          | 0.2778 |
| 0.3531        | 88.07 | 9600  | 0.6523          | 0.2781 |
| 0.3586        | 88.99 | 9700  | 0.6578          | 0.2789 |
| 0.3463        | 89.91 | 9800  | 0.6565          | 0.2816 |
| 0.3508        | 90.83 | 9900  | 0.6559          | 0.2797 |
| 0.3513        | 91.74 | 10000 | 0.6611          | 0.2794 |
| 0.3425        | 92.66 | 10100 | 0.6538          | 0.2804 |
| 0.3596        | 93.58 | 10200 | 0.6639          | 0.2808 |
| 0.3632        | 94.5  | 10300 | 0.6561          | 0.2789 |
| 0.348         | 95.41 | 10400 | 0.6556          | 0.2786 |
| 0.3514        | 96.33 | 10500 | 0.6575          | 0.2791 |
| 0.3499        | 97.25 | 10600 | 0.6573          | 0.2795 |
| 0.3353        | 98.17 | 10700 | 0.6589          | 0.2797 |
| 0.3468        | 99.08 | 10800 | 0.6589          | 0.2799 |
| 0.3571        | 100.0 | 10900 | 0.6593          | 0.2797 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
