---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_6_1
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-thai-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_6_1
      type: common_voice_6_1
      config: th
      split: test
      args: th
    metrics:
    - name: Wer
      type: wer
      value: 0.7234125438254773
---

<!-- 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. -->

# wav2vec2-large-mms-1b-thai-colab

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2452
- Wer: 0.7234

## 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: 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: 100
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.0794        | 0.17  | 100  | 0.3832          | 0.8329 |
| 0.561         | 0.33  | 200  | 0.3162          | 0.8099 |
| 0.5132        | 0.5   | 300  | 0.2907          | 0.7842 |
| 0.5015        | 0.66  | 400  | 0.2954          | 0.7998 |
| 0.5126        | 0.83  | 500  | 0.2812          | 0.7924 |
| 0.5182        | 0.99  | 600  | 0.2782          | 0.7631 |
| 0.4459        | 1.16  | 700  | 0.2735          | 0.7526 |
| 0.4694        | 1.32  | 800  | 0.2716          | 0.7628 |
| 0.4576        | 1.49  | 900  | 0.2649          | 0.7538 |
| 0.4749        | 1.65  | 1000 | 0.2614          | 0.7503 |
| 0.4282        | 1.82  | 1100 | 0.2687          | 0.7464 |
| 0.4009        | 1.98  | 1200 | 0.2622          | 0.7480 |
| 0.3976        | 2.15  | 1300 | 0.2619          | 0.7421 |
| 0.4306        | 2.31  | 1400 | 0.2620          | 0.7538 |
| 0.4413        | 2.48  | 1500 | 0.2551          | 0.7515 |
| 0.3888        | 2.64  | 1600 | 0.2545          | 0.7339 |
| 0.4213        | 2.81  | 1700 | 0.2541          | 0.7316 |
| 0.3945        | 2.98  | 1800 | 0.2507          | 0.7246 |
| 0.3765        | 3.14  | 1900 | 0.2495          | 0.7234 |
| 0.3859        | 3.31  | 2000 | 0.2498          | 0.7269 |
| 0.3931        | 3.47  | 2100 | 0.2469          | 0.7250 |
| 0.3737        | 3.64  | 2200 | 0.2470          | 0.7242 |
| 0.3716        | 3.8   | 2300 | 0.2454          | 0.7219 |
| 0.3582        | 3.97  | 2400 | 0.2452          | 0.7234 |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1