---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-mms-1b-all-swc-kat6
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.37098445595854923
---

<!-- 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-mms-1b-all-swc-kat6

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.3710

## 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: 1
- 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: 400
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.8349        | 0.07  | 400   | inf             | 0.4899 |
| 0.8039        | 0.15  | 800   | inf             | 0.4912 |
| 0.7335        | 0.22  | 1200  | inf             | 0.4482 |
| 0.8395        | 0.3   | 1600  | inf             | 0.4829 |
| 0.7626        | 0.37  | 2000  | inf             | 0.4635 |
| 0.9035        | 0.44  | 2400  | inf             | 0.4391 |
| 0.6281        | 0.52  | 2800  | inf             | 0.4668 |
| 0.6756        | 0.59  | 3200  | inf             | 0.4254 |
| 0.7866        | 0.67  | 3600  | inf             | 0.4168 |
| 0.7413        | 0.74  | 4000  | inf             | 0.4168 |
| 0.749         | 0.81  | 4400  | inf             | 0.4148 |
| 0.8165        | 0.89  | 4800  | inf             | 0.4241 |
| 0.7302        | 0.96  | 5200  | inf             | 0.4124 |
| 0.7376        | 1.04  | 5600  | inf             | 0.4368 |
| 0.6833        | 1.11  | 6000  | inf             | 0.3953 |
| 0.6463        | 1.19  | 6400  | inf             | 0.4363 |
| 0.7236        | 1.26  | 6800  | inf             | 0.4383 |
| 0.8837        | 1.33  | 7200  | inf             | 0.4811 |
| 0.6854        | 1.41  | 7600  | inf             | 0.3930 |
| 0.6985        | 1.48  | 8000  | inf             | 0.3979 |
| 0.7139        | 1.56  | 8400  | inf             | 0.3977 |
| 0.6338        | 1.63  | 8800  | inf             | 0.4039 |
| 0.7227        | 1.7   | 9200  | inf             | 0.3922 |
| 0.6843        | 1.78  | 9600  | inf             | 0.4111 |
| 0.6948        | 1.85  | 10000 | inf             | 0.4093 |
| 0.6867        | 1.93  | 10400 | inf             | 0.3927 |
| 0.5753        | 2.0   | 10800 | inf             | 0.4080 |
| 0.6865        | 2.07  | 11200 | inf             | 0.3938 |
| 0.6155        | 2.15  | 11600 | inf             | 0.3920 |
| 0.6743        | 2.22  | 12000 | inf             | 0.3977 |
| 0.5801        | 2.3   | 12400 | inf             | 0.3801 |
| 0.8216        | 2.37  | 12800 | inf             | 0.3917 |
| 0.6199        | 2.44  | 13200 | inf             | 0.4052 |
| 0.6268        | 2.52  | 13600 | inf             | 0.3811 |
| 0.6505        | 2.59  | 14000 | inf             | 0.3855 |
| 0.6578        | 2.67  | 14400 | inf             | 0.3933 |
| 0.6442        | 2.74  | 14800 | inf             | 0.3868 |
| 0.5904        | 2.81  | 15200 | inf             | 0.3782 |
| 0.6249        | 2.89  | 15600 | inf             | 0.3788 |
| 0.5879        | 2.96  | 16000 | inf             | 0.3904 |
| 0.4844        | 3.04  | 16400 | inf             | 0.3728 |
| 0.6309        | 3.11  | 16800 | inf             | 0.3687 |
| 0.5825        | 3.19  | 17200 | inf             | 0.3663 |
| 0.7171        | 3.26  | 17600 | inf             | 0.3772 |
| 0.5471        | 3.33  | 18000 | inf             | 0.3718 |
| 0.5029        | 3.41  | 18400 | inf             | 0.3756 |
| 0.5605        | 3.48  | 18800 | inf             | 0.3751 |
| 0.5582        | 3.56  | 19200 | inf             | 0.3728 |
| 0.6358        | 3.63  | 19600 | inf             | 0.3712 |
| 0.4977        | 3.7   | 20000 | inf             | 0.3655 |
| 0.4828        | 3.78  | 20400 | inf             | 0.3671 |
| 0.6554        | 3.85  | 20800 | inf             | 0.3689 |
| 0.561         | 3.93  | 21200 | inf             | 0.3702 |
| 0.5515        | 4.0   | 21600 | inf             | 0.3710 |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.12.1