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: ASR4-for-40-epochs
  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: 0.26843348202571504
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ASR4-for-40-epochs

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: 0.4791
- Wer: 0.2684

## 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: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1111        | 0.92  | 100  | 0.7687          | 0.4387 |
| 1.1201        | 1.83  | 200  | 0.6388          | 0.3767 |
| 0.9734        | 2.75  | 300  | 0.6319          | 0.3658 |
| 0.9297        | 3.67  | 400  | 0.5740          | 0.3373 |
| 0.9142        | 4.59  | 500  | 0.5591          | 0.3268 |
| 0.8462        | 5.5   | 600  | 0.5627          | 0.3227 |
| 0.8366        | 6.42  | 700  | 0.5491          | 0.3158 |
| 0.8272        | 7.34  | 800  | 0.5398          | 0.3243 |
| 0.8137        | 8.26  | 900  | 0.5363          | 0.3113 |
| 0.7643        | 9.17  | 1000 | 0.5528          | 0.3117 |
| 0.7738        | 10.09 | 1100 | 0.5194          | 0.3285 |
| 0.7622        | 11.01 | 1200 | 0.5348          | 0.3043 |
| 0.707         | 11.93 | 1300 | 0.5179          | 0.2909 |
| 0.7242        | 12.84 | 1400 | 0.5153          | 0.3138 |
| 0.7093        | 13.76 | 1500 | 0.5116          | 0.2951 |
| 0.673         | 14.68 | 1600 | 0.5002          | 0.2941 |
| 0.6877        | 15.6  | 1700 | 0.4958          | 0.3050 |
| 0.6665        | 16.51 | 1800 | 0.5032          | 0.2865 |
| 0.6507        | 17.43 | 1900 | 0.4871          | 0.2809 |
| 0.6308        | 18.35 | 2000 | 0.4953          | 0.2947 |
| 0.6507        | 19.27 | 2100 | 0.4998          | 0.2837 |
| 0.6027        | 20.18 | 2200 | 0.4963          | 0.2868 |
| 0.623         | 21.1  | 2300 | 0.4955          | 0.2953 |
| 0.6047        | 22.02 | 2400 | 0.5034          | 0.2852 |
| 0.5825        | 22.94 | 2500 | 0.4781          | 0.2795 |
| 0.585         | 23.85 | 2600 | 0.4851          | 0.2843 |
| 0.5838        | 24.77 | 2700 | 0.4957          | 0.2742 |
| 0.5718        | 25.69 | 2800 | 0.4885          | 0.2810 |
| 0.5646        | 26.61 | 2900 | 0.4778          | 0.2724 |
| 0.5476        | 27.52 | 3000 | 0.4914          | 0.2751 |
| 0.5333        | 28.44 | 3100 | 0.4879          | 0.2788 |
| 0.5533        | 29.36 | 3200 | 0.4820          | 0.2726 |
| 0.5321        | 30.28 | 3300 | 0.4816          | 0.2686 |
| 0.5161        | 31.19 | 3400 | 0.4865          | 0.2812 |
| 0.5326        | 32.11 | 3500 | 0.4818          | 0.2704 |
| 0.5188        | 33.03 | 3600 | 0.4816          | 0.2669 |
| 0.506         | 33.94 | 3700 | 0.4804          | 0.2755 |
| 0.5122        | 34.86 | 3800 | 0.4803          | 0.2667 |
| 0.506         | 35.78 | 3900 | 0.4785          | 0.2708 |
| 0.5064        | 36.7  | 4000 | 0.4755          | 0.2730 |
| 0.4997        | 37.61 | 4100 | 0.4804          | 0.2708 |
| 0.4904        | 38.53 | 4200 | 0.4772          | 0.2678 |
| 0.4774        | 39.45 | 4300 | 0.4791          | 0.2684 |


### Framework versions

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